Miguel Lima Teixeira
email: mdl.teixeira@campus.fct.unl.pt

Camilo Velez

Dian Li

João Goes

Microneedle Based ECG -Glucose Painless MEMS Sensor with Analog Front End for Portable Devices

Keywords: Electrocardiogram, ECG, Glucose, Sensor, Microneedles, MEMS, Electrodes, Mobile Phones, Cellphones, Electronics, Biological Signals, Healthcare, Portable 1 Introduction

A portable microelectromechanical system (MEMS) for mobile phones, or other portable devices, that measures body electrical signals, as well as, extracts transdermal biological fluid for invivo analysis is proposed. This system integrates two sensing methods: three points finger electrocardiography (ECG) and glucose monitoring, through one electrode with a microneedlearray. This work presents the: (1) device modeling and microneedle-array' fabrication method, (2) signal processing and biasing circuitry' design and simulation, (3) Analog Front End (AFE) for measured signals, and (4) Glucose sensor characterization. Design parameters and geometries are obtained by solving the capillarity model inside the microneedles and running optimization numeric methods. The AFE consists in a differential band pass filter that provides amplification, filtering, and noise rejection. This work presents clear technological innovation, for its miniaturization and integration of known biological signals' measurement methods in a portable Smart System, which points in the direction of Internet of Things' goals.

According to the World Health Organization, cardiovascular diseases are the main cause of death in the world and 422million people worldwide had diabetes in 2015, causing an average of 1.5milion deaths per year, as in 2012 (http://www.who.int/diabetes/en/). Portable sensors that can measure biological signals related with those illnesses and prevent body failure, are desired to be integrated in our normal life. Further more, the healthcare' current and future trend is to give patients the ability to monitor their biological signals, making health diagnosis and at certain times preventing serious health conditions, during their normal day life, reducing significantly the hospital costs. Mobile phones are excellent candidates to be carriers of such portable sensors, due to their omnipresence in our daily lives and because general population keeps them near themselves, as much as possible, for communication purposes. This work intends to combine those facts proposing a MEMS system that integrates both sensing methods (ECG -Glucose) in one electrode, using microneedles.

One of the most important techniques to measure electrical signals in clinic and biomedical application is the ECG (electrocardiogram), which records the electrical activities of the heart over time [START_REF] O'mahony | Microneedlebased electrodes with integrated through-silicon via for biopotential recording[END_REF][3][4] [START_REF] Raju | Heart-Rate and EKG Monitor Using the MSP430FG439[END_REF][6] [START_REF] Fulford-Jones | A portable, low-power, wireless two-lead EKG system[END_REF]. The standard commercial ECG electrodes utilize an electrolytic gel to form conductive interface between skin and electrode. Using modern microsystem design techniques, it is possible to eliminate the use of gel by creating and using microneedles as electrodes [START_REF] O'mahony | Microneedlebased electrodes with integrated through-silicon via for biopotential recording[END_REF][4] [START_REF] Yu | A MEMS-based bioelectrode for ECG measurement[END_REF]. At the same time, those microneedles can be used to extract interstitial fluid (ISF) to perform enzyme based diagnostic test, as shown in [START_REF] Mukerjee | Microneedle array for transdermal biological fluid extraction and in situ analysis[END_REF][10] [START_REF] Zimmermann | A microneedle-based glucose monitor: fabricated on a wafer-level using in-device enzime inmobilization[END_REF] [START_REF] Zhao | A MEMS viscometric sensor for continuous glucose monitoring[END_REF]. Due to careful microneedle array design the sensor is painless, because the length of microneedles is less than 500µm and so they don't reach the pain sensitive dermis' area, which will be explained in section 3.2.1. These concepts were applied in this device.

This work is divided in three main areas: microneedle fabrication, microfluidics for glucose sensor, and sensors' and AFE' electrical characterization. The microneedles for ECG electrodes and glucose electrodes share the same fabrication process. In this approach the microfluidic and electric models can be separated, because the ISF is static inside the channel during the glucose measurement. The link between both electrical sensors is the reference signal. A circuit for amplifying, eliminating noise and filtering is proposed for both systems, to connect them with a microcontroller.

The PhD study in which this work is being developed has the following research question and hypothesis: Research question: What would be a suitable way to do a low power system on chip (100s of µW) that performs advanced computations for low frequency signals (below 10kHz) as the brain does, as for example pattern recognition -namely Electrocardiogram (ECG) complete Arrhythmia detection, or fully implanted closed loop Brain Machine Interface (BMI) with brain signals' complex interpretation; without the use of a general purpose CPU? Hypothesis: One possible way is to develop a system on chip inspired in the brain that uses neurotechnology and neuromorphic circuits, creating an amplitude to time conversion with an Integrate and Fire circuit, which gives a stream of pulses. That system can then use pulse processing to do computations with the output pulses, for example with an automaton -without using conventional digital signal processing (DSP). If such kind of system is designed and prototyped then it may do these advanced computations with power consumption below 100µW. Because its pulse timing structure only has information for relevant input amplitudes defined by the user, reducing the redundancy and lowering the hardware specifications.

The motivation of this work is presented next: With this power consumption, the system could be implanted in the Brain as a battery less standalone device to compensate for loss brain function, or could be used as a small ECG recording and arrhythmia detection device that is in contact with the patient chest. Currently these kinds of devices don't exist for ECG, or have very simple functions in the case of BMI, due to the limitation in computation power of such systems on chip.

Contribution to Smart Systems

This work presents clear technological innovation in a portable cellphone with smart sensors that measure body signals of the upmost importance, as for its miniaturization and integration of known biological signals' measurement methods in a portable Smart System, which points in the direction of Internet of Things' goals.

Moreover, the present work shows a clear contribution to Smart Systems as it paves the path of integrating healthcare and fitness solutions directly in mobile devices, that are one of the most used nodes to access Internet of things (IoT). In an IoT era, these are important developments to improve humans' daily life.

Device Design

The system was divided in four major modules to facilitate the design process: the microfluidic model that governs the microneedle fabrication, the glucose sensor, the ECG electrode, and the AFE for both physical measurements. Every module is independent from the others in terms of modeling, simulations and development predictions. The microfluidic model and the glucose sensor can be independent, because the method described in this document to monitor the glucose is for zero flow measurements. In the other hand, the ECG and the glucose sensor can be independent, because they will only share the reference electrode and the voltage in this electrode can be set as a design parameter for both modules.

Capillarity Modeling

Every microneedle that will be inserted inside the dermis of the human skin will experiment a capillarity drawn action. As explained in [START_REF] Mukerjee | Microneedle array for transdermal biological fluid extraction and in situ analysis[END_REF], [START_REF] Bruss | Theoretical Microfluidics[END_REF], and [START_REF]Theoretical microfluidics[END_REF], the ISF inside the dermis area of the skin will have a different pressure as compared with the atmosphere. Difference in pressures and the interactions of the fluid with the needle walls will create forces (van der Waals, dipole-dipole interaction, London dispersionfluctuating induced pole, and hydrogen bonds) that will contribute to changes on the surface tension of the fluid at the interface. Changes on the surface tension will generate the so-called capillarity drawn. Equation [START_REF] O'mahony | Microneedlebased electrodes with integrated through-silicon via for biopotential recording[END_REF] shows the pressure difference in terms of liquid-gas surface tension (γlg) the contact angle between the liquid (θ), the volume and the solid (Asl) and liquid area (Alg).

. (1) Figure 5.4, page 96, of reference [START_REF]Theoretical microfluidics[END_REF] presents the capillary rise in a vertically standing cylindrical microchannel. As shown in that figure, the liquid will flow inside the needle until it reaches the distance h. A simplified model for h was demonstrated in [START_REF] Bruss | Theoretical Microfluidics[END_REF] and can be expressed as:

h = (2•γlg•cos(θ))/(ρ•g•a) . ( 2
)
Where ρ is the fluid density, g the gravity constant, and a the needle' radius. This maximum drawn distance h will be used later in this paper to optimize the design geometry.

Glucose Sensor

Operating Principles

The relationship between glucose concentrations in ISF and blood was studied in the past. In reference [START_REF] Thennadil | Comparison of Glucose Concentration in Interstitial Fluid, and Capillary and Venous Blood During Rapid Changes in Blood Glucose Levels[END_REF] the author compared the venous, capillary blood glucose and ISF glucose concentration in one subject during 3 days. A strong correlation was found which could lead to the useful method of measuring the glucose concentration from ISF instead of directly from blood. As the ISF can be extracted from the first layers of the skin, the measurement of glucose concentration from ISF can be painless. This is accomplished by making the microneedles for extraction of ISF smaller than 500µm long so they don't touch nerve cells when inserted in the skin. This is not possible in the extraction of blood that is always a process with some degree of pain. Thus, a small microneedle based glucose sensor can be designed. This is a major advantage of ISF glucose sensors as they can be painless, if designed properly. Comparing to usual commercially available blood glucose meters, for which the sample extraction is painful and can cause sensibility and irritation in the area of skin where the sample was taken, possibly making the user to have to switch the measurement location, between measurements.

Fig. 1 presents the glucose sensor' cross section view. The interstitial fluid (ISF) collected by the microneedle first flows over glucose oxidase (GOX), which is immobilized in the channel, Fig. 1. GOX will catalyze the glucose oxidation process to generate gluconic acid and hydrogen peroxide, equation [START_REF] Lourenço | Unveiling the biometric potential of finger-based ECG signals[END_REF]. Then hydrogen peroxide when in contact with the working electrode will be decomposed into water and electrons, equation ( 4). Such electrons increase the ISF's electrical conductivity. A 0.7V potential difference is added between the working electrode and the reference electrode that will induce a current. According to the whole reaction's equation the concentration of glucose in ISF is proportional to this current amplitude [START_REF] Wang | Minimally invasive extraction of dermal interstitial fluid for glucose monitoring using microneedles[END_REF].

Glucose +O 2 ®Gluconicacid + H 2 O 2 .
(3)

H 2 O 2 ® O 2 + 2H + + 2e - . (4)

First-order Model

The ISF inside of the channel can be treated as a linear resistor when the glucose concentration is small and a nonlinear one when the concentration is large. The lumped element model is simply a voltage source connected with a resistance in parallel. The governing equation of the reaction is Michaelis-Menten kinetics equation is:

max [] [] m IS i KS   . ( 5
)
Where i is current that is generated. I max is the maximum current achieved by the system. [] S is concentration of substrate. m K is Michaelis constant, which is the substrate concentration when current is half of max I .

The linearity between current and concentration depends on the assumption that the oxygen concentration in the ISF is enough. In reality, its concentration is limited. So when glucose concentration is more than 200mg/dl, it is nonlinear. The commercial glucose measurement devices based on this principle also present a nonlinear region above 200mg/dl.

From figure 3 of [START_REF] Suckale | Pancreas islets in metabolic signaling -focus on the Betacell[END_REF], that is based in the original data and curves of M. Daly, et al. [START_REF] Daly | Acute effects on insulin sensitivity and diurnal metabolic profiles of a high-sucrose compared with a high-starch diet[END_REF], it can be seen that the fluctuation of blood sugar in normal humans during a day with three meals is less than 125mg/dl. The proposed glucose sensor in this paper will work properly, because the linear region of the sensor is larger than the ordinary human glucose concentration region: 3mg/dl to 125mg/dl [START_REF] Suckale | Pancreas islets in metabolic signaling -focus on the Betacell[END_REF], [START_REF] Daly | Acute effects on insulin sensitivity and diurnal metabolic profiles of a high-sucrose compared with a high-starch diet[END_REF]. Moreover, when the blood sugar is at higher values, it is still possible to make a measurement by using a table stored in the micro controller memory.

Glucose Sensor Device Performance

The prospective dynamic range of glucose meter is: 0-180 mg/dl, Fig. 2. The resulting current is in the range 0 to 0.86nA. Thus, the sensitivity is 0.0048nA*dl/mg. This small current requires that the AFE has a high amplification gain, only possible with two or more amplifiers stages. Above a glucose concentration of approximately 180 mg/dl, the device response is in the nonlinear region.

ECG Electrodes

The designed finger ECG electrodes consist in dry electrodes that have a 3D shape. The electrodes are deposited on top of an array of micro needles to decrease the overall electrode' impedance. Each ECG electrode consists in a 2-wafer structure. The top wafer is micro fabricated to have an array of hollow micro needles. After this microfabrication, the electrodes' metal (AgCl +Ag) is sputtered in all the sides of the wafer and due to the microneedles geometry it is ensured that there is an electric contact between both sides of the wafer, through the micro needles' holes, Fig. 1. The bottom wafer for the ECG electrode has a metal line that makes a contact between the top wafer and the electronic circuitry. Both wafers are joined together by an Ag conductive glue, in the case of the 2 ECG electrodes, without the glucose sensor.

The extraction of ECG signal from the finger is a novel method. In [START_REF] Lourenço | Unveiling the biometric potential of finger-based ECG signals[END_REF] is presented a proof a principle of finger ECG, using commercial planar dry electrodes and in [START_REF] Raju | Heart-Rate and EKG Monitor Using the MSP430FG439[END_REF] is presented an heart beat rate monitor system that uses planar dry electrodes for the fingers. The micro needle based electrode for ECG is also a novel approach. Reference [START_REF] Yu | A MEMS-based bioelectrode for ECG measurement[END_REF] shows the microneedle device with a chamber, in the back of the microneedle structure, containing a conductive gel, to improve the conductance of these type of electrodes. It has been shown that dry microneedle electrodes are capable of acquiring signals from ECG, electromyography (EMG) and even electro encephalography (EEG), signals that are lower voltage signals, than ECG and EMG, and so require a very low impedance electrode, [START_REF] O'mahony | Microneedlebased electrodes with integrated through-silicon via for biopotential recording[END_REF] and [START_REF] Forvi | Preliminary technological assessment of microneedles-based dry electrodes for biopotential monitoring in clinical examinations[END_REF]. These microneedles's electrodes were designed to record ECG in the human chest. As an ECG measurement device has never been integrated directly in a cell phone.

The first order model for the ECG measurement and the lumped element model for the skin and electrode is presented in [START_REF] O'mahony | Microneedlebased electrodes with integrated through-silicon via for biopotential recording[END_REF] and [START_REF] Dorgan | A Model for Human Skin Impedance During Surface Functional Neuromuscular Stimulation[END_REF]. To do an ECG measurement it is required to use two active electrodes, in two fingers, or one finger and hand, and a third reference electrode in the other hand, or in another finger, or in a different zone.

ECG Device Performance

An estimate of the ECG electrodes' device performance was made. The electrode area is the area of the whole device, without electronic circuitry, as the electrode metal layer is deposited over all the microneedles in the array: electrode area=3.8mm*2.5mm with a AgCL layer, 25nm thick.

The maximum input signal range is -1mV to 1mV. The ECG electronic circuitry performs an amplification of 500 times, it has to have a linear response from 0.05Hz until 500Hz, that is half of the sampling frequency so it can measure accurately each one of the peaks that make the ECG QRSPT complex, and measure a minimum detectable signal of 5µV. These and other specifications are presented in the next tables: Table 1. Overall circuit' specifications.

Specifications

Value Supply voltage Vss 3.6V Imax (source) [START_REF] Fulford-Jones | A portable, low-power, wireless two-lead EKG system[END_REF] 40mA Power 144mW

Table 2. ECG AFE Specifications.

Specifications

Value Vbias [START_REF] O'mahony | Microneedlebased electrodes with integrated through-silicon via for biopotential recording[END_REF] 1.65V Input maximum signal range [START_REF] Raju | Heart-Rate and EKG Monitor Using the MSP430FG439[END_REF] -1mV to +1mV MDS [START_REF] Sarpeshkar | Ultra Low Power Bioelectronics Fundamentals[END_REF] 5μV Micro needles height [START_REF] Newman | Home blood glucose biosensors: a commercial perspective[END_REF] 20μm Sampling frequency (fs) [START_REF] Lourenço | Unveiling the biometric potential of finger-based ECG signals[END_REF] 1kHz To accomplish these circuit requirements it is important that the circuit has low noise. More details about the electronic circuits are given in section 5.

Design Optimization

In order to obtain the geometry values to generate the proper mask for the fabrication process it is necessary to analyze the behavior of the microfluidic module and its interaction with the human skin. Every microneedle will experiment the capillarity drawn for the ISF. It is necessary to design the system with the proper length so the ISF can fill the needle and the micro channel covering the electrodes. This will be the most important parameter to be considering for the mask design. Considering this parameter an optimization problem can be written in order to maximize the fluid volume inside the needle (cylinder volume) and the channel (rectangular channel). Fig. 3 shows the distances for the fluidic module. The optimization problem can be written in the standard format as in Fig. 3. Constrains for this problem include: 1) the capillarity equation shown in [START_REF] Newman | Home blood glucose biosensors: a commercial perspective[END_REF] with the ISF ρ=1060 kg/m 3 [START_REF] Dorgan | A Model for Human Skin Impedance During Surface Functional Neuromuscular Stimulation[END_REF], the ISF surface tension Γ=60mJ/m 2 [START_REF] Cutnell | [END_REF], and the contact angle θ=110° [20] between ISF and needle walls. 2) Represent the wafer thickness. 3) Is the maximum needle' dimension in order to avoid reaching dermis' pain sensitive area according to [START_REF] Sarpeshkar | Ultra Low Power Bioelectronics Fundamentals[END_REF]. 4) is the dermis typical thickness for human skin. This area contains the ISF. 5) is the stratum corneum thickness for human skin. And 6) is a fabrication limitation by the 20mm head of the Heidelberg laser stepper available in the University of Florida's Nanoscale Research Facility (NRF) clean room that will be used in this fabrication process. The Matlab® optimization results obtained, after rounding the numbers to fit the fabrication requirements on the available clean room, are: microneedle chamber length L3=170µm and microneedle diameter d1=20µm.

Device Fabrication

Device fabrication is summarized next: A. Backside deep reactive ion etching (DRIE) Bosh process (360µm) using Aluminum (Phosphoric -Acetic -Nitric (PAN) etched) and Photoresist as mask. B. Backside reactive ion etching (RIE) process (15µm) using Aluminum (PAN etched). C. Front side DRIE Bosh process (170µm) using thick Photoresist as mask. D. Sharpening etching (HNA solution) E. Backside metal deposition (Ag) using sputtering and electrochemical deposition (TSV). F. Front side metal deposition (Ag) using sputtering and electrochemical deposition (through-silicon via (TSV)) [START_REF] Mukerjee | Microneedle array for transdermal biological fluid extraction and in situ analysis[END_REF]. G. FeCl3 chemical oxidation to generate the AgCl and finalize the Ag/AgCl deposition of the TSV on both sides. H. Metal deposition for electrodes on the Group's Wafer. For electrodes in the glucose sensor. I.

Glucose immobilization. J. Silicon/Silicon anodic bounding for glucose sensor and conductive adhesive bounding for ECG electrodes.

Integration and Packaging

Mounting and Packaging

The device is integrated in a System in Package (SiP) that is inside the cellphone. The system is controlled by the ultra-low power microcontroller MSP430FR5739, from Texas Instruments [21], [START_REF]x Mixed Signal Microcontroller datasheet[END_REF]. The biosensor device, as was previously explained, consists in three different electrode structures: two dry ECG sensors and a third wet sensor for ECG and Glucose detection. One of the dry ECG electrodes (active electrode) is integrated in the system in package with the signal processing electronic circuitry. The other dry electrode (reference electrode) is also inside the cellphone, but is connected with a cable to the SiP. The third sensor includes an active ECG electrode and Glucose sensor, this last sensor is disposable and a platform similar to a SD card platform is used to change this disposable sensor. A cable connects this sensor to the electronic circuitry in the SiP. The disposition of these three sensors is shown in Fig. 4.

Fig. 4. Schematic example of Sensors and SiP disposition in the mobile phone

As was referred before both ECG dry electrodes and the glucose and ECG sensor consist in two Si wafers. The bottom wafer for the ECG electrode has a metal line that makes a contact between the top wafer and the bottom wafer and then connects to the electronic circuitry. Both wafers are joined together by an Ag conductive glue, in the case of the 2 ECG electrodes, without the glucose sensor. The contact in the bottom wafer is wire bonded to a wafer with the electronic circuitry and the microcontroller.

In the glucose and ECG sensor, the two wafers are joined by anodic bonding and the bottom wafer contains metal lines that are connect to a cable connector by wire bonding, this system is all inside the SD card platform.

Signal Processing Electronic Circuitry

The two active ECG electrodes are connected to the electronic signal processing circuitry similar to the one shown in figure 2 of [START_REF] Msp | Heart-Rate and EKG Monitor Using the MSP430FG439[END_REF], these are connected to a differential amplifier that is connected to a high pass filter amplifier that eliminates the DC voltage and then the signal goes through a high gain low pass filter amplifier that connects to and ADC channel of the microcontroller. The overall total gain of this amplifier is 500. Spice simulations were made using BSIM 3 models for MOSIS 350nm technology. A band pass filter with two amplifier stages and a capacitor feedback configuration was designed, Fig. 5. This circuit is a band pass filter. The negative feedback loop defined by R2 and C2 gives the high pass frequency of the circuit and R1 C1 plus the low pass filter behavior of the amplifier (any non ideal amplifier due to the devices, transistors and the way they behave in the amplifier stages has intrinsically a Gain roll of frequency that gives a low pass filter behavior) define the low pass frequency. The feedback gain is defined by Cin/Cf=C1/C2, being Cin the input capacitance and Cf the feedback capacitance [START_REF] Xiao | Low Power Circuits and Systems for Brain Machine Interface[END_REF] and [25], and in this example it is 100, note for just one amplifier stage. This structure is very common in bio amplifiers and provides good results [25]. R2 and R4 are pseudoresistors to achieve a high resistance value. Each amplifier stage consists in a single ended dual input, PMOS and NMOS input, current mirror Output Transconductance Amplifier (OTA) presented in Fig. 6. The glucose sensors are also connected to electronic circuitry. Fig. 6 As the glucose sensor has 100 microneedles the signals from each electrode are connected in parallel (working electrodes' signals are connected in parallel, and reference electrodes' signals are connected in parallel). This way the measured current is approximately 100 times the current of a single microneedle, so in the order of 100nA. This is considering that the reaction in all microneedles is approximately at same stage and in the maximum current reaction point, of 1nA per microneedle. The overall signal of the working electrodes and the signal from the reference electrode are then connected to the signal processing circuitry. The current from the working electrodes is converted to a voltage in a transimpedance amplifier and then it is connected to a differential amplifier together with the signal from the reference electrodes. The signal is then amplified in a high gain low pass filter amplifier and connected to another channel of the microcontrollers ADC. The overall gain of this amplification circuit should be in the order of 90dB, considering the total gain of the three amplification blocks.

The interface circuitry has a major influence in the system performance, because the sensors are measuring very small signals, the minimum detectable signal (MDS) of ECG is 5µV and the current detected in the glucose sensor array is of the order of 100nA. In this sense, noise is one of the main issues. To minimize noise: the system must have well designed fully differential amplifiers, the gain of the first amplifier stage should be maximized for each amplifier comparatively with the other stages. Also there should be a good electromagnetic insulation from the cellphone circuitry, especially from RF.

Results

A numeric optimization for the microfluidic parameters was done and the obtained results are: microneedle chamber length L3=170µm and microneedle diameter d1=20µm.

The layout of the fabrication masks for the glucose sensor device with the microneedle array for all the material layers and respective fabrication steps was made using the Ledit® software. Considering the fabrication process and as result of the optimization solution obtained using fmincon in Matlab®, the masks show in Fig. 7 were created using Ledit® software for layout design. 7 presents the final layout of the sensors array with the array of 10 needles by 10 needles, the electrical pads for the glucose sensing and the interconnection channels with a general outlet for generating the pressure difference. Alignment marks for the mask (global alignment, vernier and photoresists development) were also included in this layout. The final system (glucose sensor) area is 3.8mm x 2.5mm.

The AFE simulations results met the design specifications. Fig. 8 presents the transient analysis with a real ECG noisy signal with typical signal wander due to movement and variation in the electrodes' contact with skin. And Fig. 9 presents the AFE Bode plot from the AC analysis. Table 3 presents the AFE simulation results. 0.0048nA*dl/mg sensitivity for glucose sensing and an estimated dynamic range of 0-180 mg/dl and so it is suited to be used for daily ISF sugar monitoring. The ECG AFE was designed and simulated complying with all the design specifications. It has a Gain of 49dB and a good CMRR of 52dB. Some future challenges are: 1. How to expand the linear range of glucose sensor. (oxygen integrated). A possible way is to integrate some spare oxygen in an air bag, which is soluble to ISF. Making the saturation region smaller.

2. The cost per sensor is high due to electrode material and fabrication process. Ways to minimize cost should be studied and developed.

3. Also a method to avoid microneedle loss because of breakage, or use the option of making all sensors disposable should be considered and studied 4. The pseudo resistors used in the ECG AFE are not a definitive solution, because their value is not accurate enough, it is quite sensitive to process, voltage and temperature variations (PVT). It changes with process variation from chip to chip, with the slight differences in feature size and for the same reason the simulated values are different than the actual values on chip, making the band filter 3dB corner frequency slightly different than simulated and expected. This is specially an issue for the low pass frequency that should to be in the tight range of 0.05Hz to 0.5Hz.

This work shows a clear contribution to Smart Systems as it paves the path of integrating healthcare and fitness solutions directly in mobile devices, that are one of the most used nodes to access Internet of things (IoT). In an IoT era, these are important developments to improve humans' daily life.

Fig. 1 .

 1 Fig. 1. Microneedle Cross section for the ECG electrodes (left) and ECG and glucose sensor electrode (right), W: working electrode, C: counter electrode, R: reference electrode, GOX: Enzyme.

Fig. 2 .

 2 Fig. 2. The performance of glucose sensor, current versus substrate (glucose) concentration. The relation between glucose concentration and current is in blue. The perpendicular lines show the prospective dynamic range

Fig. 3 .

 3 Fig. 3. Left: . Standard format for the optimization problem. Objective Function, constraints (from 1 to 3) and boundaries (4 to 6). Right: Microneedle inserted in human skin. Geometry variables used in the optimization process. Main dimension constrains related with skin interaction are shown.

Fig. 5 .

 5 Fig. 5. Single ended Band Pass filter with Capacitor feedback for ECG AFE.

Fig. 6 .

 6 Fig. 6. Single ended dual input, PMOS and NMOS input, current mirror OTA.

Fig. 7 .

 7 Fig. 7. Array of 10 needles by 10 needles design layout, made with Ledit® software, and closeup of a single microneedle' layout with its own glucose sensor electrodes.

Fig.

  Fig.7presents the final layout of the sensors array with the array of 10 needles by 10 needles, the electrical pads for the glucose sensing and the interconnection channels with a general outlet for generating the pressure difference. Alignment marks for the mask (global alignment, vernier and photoresists development) were also included in this layout. The final system (glucose sensor) area is 3.8mm x 2.5mm.The AFE simulations results met the design specifications. Fig.8presents the transient analysis with a real ECG noisy signal with typical signal wander due to movement and variation in the electrodes' contact with skin. And Fig.9presents the AFE Bode plot from the AC analysis. Table3presents the AFE simulation results.

Fig. 8 .

 8 Fig. 8. Transient AFE Simulation Results -Top: input signal ECG in blue with noise and signal wander, Bottom: AFE signal output in red.

Conclusion

A novel minimal invasive micro needle based ECG and Glucose sensor was designed. The whole sensor can be integrated on smart phone using a system in package approach. The extraction of ECG signal from the finger with a microneedle array is a novel approach. The ECG AFE has 2mW power consumption that can be further reduced, by reducing the band pass filter bandwidth. Considering the design constraints and fabrication tools, a Matlab® design optimization was made for the microneedle array obtaining a microneedle chamber length L3=170um and a microneedle diameter d1=20um. The fabrication steps and respective micro and nano fabrication masks were developed. The final glucose sensor area, that consists in in a array of 10 by 10 microfabricated microneedles is 3.8mm x 2.5mm. The sensor has a


Thais Andrea Baldissera

Luis M Camarinha-Matos

Cristiano De Faveri
email: c.faveri@campus.fct.unl.pt

Service Personalization Requirements for Elderly Care in a Collaborative Environment

Keywords: Collaborative Business Service, service personalization, requirements, elderly care, aging

In diverse sectors companies collaborate to offer integrated usercentric services to obtain competitive advantage. However, regarding elderly care, services are typically isolated, and mostly provided by a single provider. Moreover, there is a need to personalize services in respect of the individuality of each senior and making these services evolve according to the lifestyle and necessities of the person. In this context, the concept of Elderly Care Ecosystem (ECE) emerges as a computer-based collaborative environment that promotes the integration of distinct services and providers. ECEs are complex systems that demand a clear description of their requirements, especially of those related to personalization of services. In this paper, we describe a set of core requirements and challenges for designing personalized services in an elderly care ecosystem. In particular, our contributions are: the identification of the main stakeholders of an ECE, the requirements to build a persona profile in ECE, and a reference requirements model based on i* for service personalization in ECE.

Introduction

Aging of the global population represents one of the most significant demographic changes in the humanity history. The increase of the average life expectancy associated to the declination of fertility led to the growing of the median age of the population, being expected that those over 80 years will exceed the young in 2050 [START_REF] Baldissera | Towards a Collaborative Business Ecosystem for Elderly Care[END_REF]. This aging process is responsible for changes at various levels, which may impact a number of different aspects of older adults' lives and limit the extent to which they are able to perform certain activities. This reality reflects the need for adaptations in the society to provide better healthcare and inclusion solutions for elderly people.

Research and development as well as industry practice in elderly care services has conventionally dedicated on the development of isolated services, contemplating only a single service provider and often showing an extreme focused on technology. Also services delivery often does not fully cover customers care needs and remain static until there is a customer request for change. As it is well-known in other sectors, to answer current demanding market challenges, organizations must collaborate to overcome their weaknesses and strengthen their expertise, to offer better integrated services, focusing on user-centric services and gaining competitive advantage [START_REF] Camarinha-Matos | Care services ecosystem for ambient assisted living[END_REF]. Furthermore, in the elderly care context, there is a need for personalized services that respect the individuality of each senior and cope with the evolution of needs, since the senior's life context changes along the aging process [START_REF] O'grady | Towards evolutionary ambient assisted living systems[END_REF].

In order to emphasize this perspective, the term Elderly Care Ecosystem (ECE) was proposed in [START_REF] Baldissera | Towards a Collaborative Business Ecosystem for Elderly Care[END_REF]. In particular, the ECE is aimed at enabling the integration of different services from distinct providers. In this way, care and assistance services for elderly can be seen as "the result of collaboration among various stakeholders, including local communities, governmental institutions, professionals, family and caregivers, and thus require a supporting collaboration environment" [START_REF] Baldissera | Towards a Collaborative Business Ecosystem for Elderly Care[END_REF].

As such, this research aims at contributing to a more effective provision of services for elderly care in the context of collaborative networks. The main research question addressed by this work is: "How to provide personalized care services for elderly?" The pursued hypothesis is: "Effective and reliable personalized services for elderly care can be provided if a suitable set of multi-provider business services is composed and integrated in the context of a collaborative network environment".

The first step to design such a system is to understand the domain of the problem we want to solve. This is covered during the first stages of requirements engineering, where domain analysis and elicitation are typical activities. These steps are highly intertwined and seek at grasping the system as-is, identifying problems and opportunities, discovering the real needs of the stakeholders with respect to the ecosystem, and exploring alternative ways to address those needs [START_REF] Van Lamsweerde | Requirements engineering: from system goals to UML models to software specifications[END_REF]. In this paper, we highlight requirements for elderly care ecosystems, including the set of stakeholders and their interactions.

The relationship to smart system is presented in Section 2. Background and approach paradigm (Section 3) support the provision of integrated services and help service providers to acquire agility and better survive in market. The Elderly Care Ecosystem Description (Section 4) introduces the environment and potential challenges in this domain. In Section 5, the ecosystem stakeholders' identification and relations between them are described as well as the elderly care ecosystem characterization and business service targeting. Finally, we conclude in Section 6 identifying the future work.

Relationship to Smart Systems

Recent trends in the development of smart environments and services for humancentered applications point to personalized care and improved quality of life [START_REF] Moar | Smart Wearable Devices: Market Trends & Competitive Landscape 2015-2020[END_REF]. The elderly community represents a relevant portion of the world population and tends to be more active and independent, assisted by emergent technologies, such as those based on the Internet of Things, embedded and wearable systems as well as cloud computing. The inclusion of progressive levels of intelligence in these systems is likely to provide better support to these users and increase their quality of life.

A practical application of smart system for elderly care is exemplified by the Su-san´s illustrative scenario in Fig. 1. Susan is 82 years old, lives alone, but has a very active life. She likes to go out with her friends, attends social events, and does social work. She takes care of her health and preserves natural resources as much as she can. She has a SPA device (Smart Personal Assistant) which interacts with her in different moments of Susan´s life and guides her steps in order to respect her lifestyle and intentions.

Fig. 1 Susan´s Illustrative Scenario

Trends in Elderly Care Ecosystem Design

Our focus is to describe requirements for the design and construction of an elderly care ecosystem that considers two main aspects: personalization and evolution of services. Both aspects depend on a set of data about customers, providers, regulations and the network itself, to determine a ranking of potential services for each customer. This ranking considers individual customer's needs and a set of criteria that suggests the best services in a given moment. Domain understanding and requirements elicitation are typically performed by studying key documents, investigating similar ecosystems and interviewing or observing the identified stakeholders. In the elderly care domain, potential ECE stakeholders include professional entities and care people, suppliers and support entities, and customers. We analyzed a number of research projects on ICT and aging as part of the elicitation process. These projects have been developed since 2001 as part of ICT and Aging agenda of European framework programs. For instance, AMIGO 1 Project -"Ambient Intelligence for the Networked Home Environment" -was one of the first addressing collaborative providers with integrated services in 2004. EMOTIONAAL 2 project effectively focused on services delivered through various providers addressing the healthcare-concept for elderly persons in rural areas in Europe. ePAL project (Extending Professional Active Life) developed a strategic research roadmap focused on innovative collaborative solutions and ensuring a balanced postretirement life-style.

BRAID project -"Bridging Research in Ageing and ICT Development" [START_REF] Camarinha-Matos | A comprehensive research roadmap for ICT and ageing[END_REF] introduced four perspectives or life settings (adopted in our work): (i) independent living: how technology can assist in normal daily life activities, e.g., tasks at home, mobility, safety, agenda management (memory help), etc.; (ii) health and care in life: how technology can assist in health monitoring, disease prevention, and compensation for disabilities; (iii) occupation in life: how technology can support the continuation of professional activities along the ageing process; (iv) recreation in life: how technology can facilitate socialization and participation in leisure activities. Since 2010, 21 other relevant projects were identified, which focused on some form of collaborative providers. Out of these projects, 18 delivered atomics services and 3 developed integrated services, all with larger focus on independent living. In particular, Ambient Assistance Living for All -AAL4ALL3 project conducted a field survey aimed at both characterizing current users of AAL technologies and understanding how potential future users feel about and are willing to use this type of technology.

Requirements of ECE

ECE Stakeholders

An ECE model include many components, including customers (seniors) and their wants and limitations, care services, and service provision entities [START_REF] Baldissera | Towards a Collaborative Business Ecosystem for Elderly Care[END_REF]. The organization of this ecosystem model is proposed per four environments: customer, care needs, service, and service providers. Customer relates to relevant information about elderly as an entity within the ECE. Care need represents the main necessities of individuals within the elderly domain. Service is provided by service providers, and it represents a care service available within the ecosystem.

A detailed customer's profile, including historical record, is fundamental to identify the best option (ranking) of care services that attends his/her individual and specific needs. In addition, context analysis, considering available stakeholders and devices, can provide a valuable contribution for potential service rating. We identified the following environments in ECE:

A. Customer Environment, representing the elderly customers and their ambient and context. It includes:  Elderly. In ECE, a customer is characterized by typical personal attributes such as name, surname, nickname, gender, birth date, contact, marital status, etc. Geographical location is highlighted for guiding regional preferences and service coverage.

 Elderly Family, Caregivers and Friends. Many people live with their family when they begin to lose capabilities, representing the traditional way to deal with aging but that requires intense care from family. Therefore, other relevant information is about caregivers, family members and relatives who are responsible for them. Relatives need to actively participate in the aging process and support the elderly.

 Applications and Devices. Software and hardware which interact with the elderly leading to context-aware systems. Sensorial device information contributes to frequent contextual analysis and to input for efficient service adaptation to current elderly context.

B. Intermediaries Environment, representing people or organizations whose main function is to provide opportunities for the relation between customers and service providers. It includes:

 Broker. An entity responsible to receive customer's request and identify the services available to attend the request.

 Professional stakeholders. Representing medical professionals, care homes, housing associations, local communities, etc. These stakeholders are particularly associated with those individuals who live by themselves but require professional care to assist them.

 Suppliers. They are enterprises with a business in tele-medicine or tele-care, providers of the ICT infrastructures -networks and applications, enterprises of hardware and software and/or service provision, etc.

 Regulation and Support entities. Examples include policy-makers, social insurance companies, public administrations, government roles, standardization organizations, civil society organizations, media services providers, etc.

C. Organization Environment, representing the care provision stakeholders with the goal to provide care and assistance services. It includes:  Single Entity, representing an individual service provider.

 Virtual Organization, representing a set of collaborative services providers working together for a common goal. A single provider probably cannot properly fulfill all needs of a customer, if it operates alone. When a situation of tailored care arises, service providers can organize themselves in a partnership and deliver an integrated service.

ECE Characterization

In this section, we characterize the envisioned Elderly Collaborative Ecosystem and describe the requirements of the personalization process. In the elderly care domain, personalization involves the analysis of the senior´s life context and can be achieved by composing a set of specific business services. We assume a data-rich environment in which data can be acquired via specific sensors and devices. In an automated environment (fully or semi), the characteristics and composition of the services set can evolve. As a result, a new service can start, a current service may evolve, or a service can be disabled or excluded from the customer's portfolio. For each new context change, the responsible care and assistance provider organization shall analyze the situation (in collaboration with all relevant stakeholders), and evolve the service to fit that context.

Service Personalization and Evolution

The main requirements for a service personalization and evolution are synthetized in Fig. 2. A goal-oriented approach is used to express a strategic dependency model using i* language which allows to describe goals, softgoals, tasks, resources, and dependencies among multiple actors. The main goal of the model is to support a middle layer for service provision between the Service Provider Environment (actor Service Providers) and the Elderly Living Environment (actor Customer). The Broker is responsible for collecting information about ECE costumer´s care and assistance service requirements (through questionnaires, identification of similarities with other customers, etc.) and verifying if an adequate service description is already available in the service catalogue, as expressed in the tasks within the Broker boundary (Manage service requests info and its decompositions). If the service is available in the ECE, the goal Request/subscribe an existing service can be achieved. If it is an undefined service, the Broker is responsible for launching a new care and assistance service design, involving the relevant stakeholders along with the Service Composition activity [START_REF] Camarinha-Matos | Collaborative Business Services Provision[END_REF]. This is expressed in the goal Request a desired service.

Fig. 2. i* Rationale Strategic Model for the Service Personalization

The Sensorial Devices Information Management System gets information from the multiple sources and analyzes these data. This result is sent to the Intelligent Service Personalization Activity which, by using relevant information, is able to recommend and/or suggest changes in current services for each ECE customer. Customer profile is required to be constantly updated to reflect most fresh information about a person. Types of input information include: sensorial information, essentially for contextawareness; relevant information from the ECE customer, to verify if additional services are necessary; analysis of the ECE customer's daily habits to recommend or suggest a care and assistance services adapted to the corresponding routines; and analysis of ECE to discover new patterns in ECE customer´s behavior. A challenge here is to guarantee privacy of users and non-intrusiveness.

Business Service Targeting

Service proposition is a key component of the ECE. It is responsible for selecting and ranking a list of services based on the customer profiles and care needs. We highlight some key aspects of service proposition requirements and challenges that should be overcome during the design and implementation of an ECE. It is not our intention to present a comprehensive list of concerns (e.g. security and privacy, cold start phenomenon, fault tolerance, etc.). Instead, we describe concerns we understand are those more critical for designing a service recommendation feature in the context of the ECE.

 Service mapping. Mapping customer's need and potential services constitutes one of the major challenges when designing personalized services in the ECE. There is a multitude of parameters that may be considered to select a set of services in the ecosystem. Services can require distinct data to cover a certain care need, thus, the matching process should be flexible enough to connect both sides (customer needs and services). In this sense, it is necessary to identify a common "language" that maps them to a taxonomy of services and care needs. A partial example of such taxonomy is shown in Fig. 3. For instance, a service can be linked to Remote monitoring, which means it attends care needs related to Follow-up monitoring and Regular monitoring. However, a care need linked to Medication assistance is not matched by a service that is assigned to Health life style assistance.

 Ranking. Once a list of potential services is built, there is a need to rank relevant services to be suggested to the customer. This is a classical problem in recommendation systems that depend on a number of parameters to determine the most relevant outcome for a particular customer.

 Scalability. As the amount of providers and customers grow, selecting services in response to new customer's state can be computationally infeasible. There is a need to optimize the searching for potential services, while keeping the recommendation list relevant for a particular customer.

 Timing. As a customer may require several services that require multiple sensors to acquire proper data, determining the time interval of sensing remains a challenge.

If this time is too short, the ECE can suffer for a lack of resources. On the other hand, if this time is too long, the ECE may be inefficient to determine a service evolution. A preliminary approach to service recommendation and composition, following a set of service adherence criteria based on a fuzzy causal network for rating care services, was presented in [START_REF] Baldissera | Services Personalization Approach for a Collaborative Care Ecosystem[END_REF]. This system is now being refined, taking into account the identified requirements. An overview of its main elements is shown in Fig. 4.

Conclusions and Future Work

Elderly Care Ecosystems can play an important role in delivering collaborative and personalized services for the elderly community. ECE represents a complex system that demands a careful identification and analysis of requirements, such that more useful systems can be built. With this purpose, we presented a set of core requirements for service personalization in ECE and a set of relevant challenges regarding service recommendation, as part of a collaborative environment for elderly care. As part of our future work, we are refining the design of a multi-criteria mechanism to support the selection and ranking process of personalized services.



  Accuracy prediction. Sensors data can be incomplete, due to failures on sensing devices, noise in the communication, among other factors. Under this situation, a recommendation function demands predictive mechanisms to keep ECE operational.

Fig. 3 .

 3 Fig. 3. Health and Care in life taxonomy partial example.

Fig. 4 .

 4 Fig. 4. Service recommendation and composition process on ECE

ftp://ftp.cordis.europa.eu/pub/ist/docs/directorate_d/st-ds/amigo-project-story_en.pdf

http://www.aal-europe.eu/projects/emotionaal/

http://www.aal4all.org/

Acknowledgment. This work has been funded in part by the Center of Technology and Systems and the Portuguese FCT-PEST program UID/EEA/00066/2013 (Impactor project), and by the Ciência Sem Fronteiras program and Erasmus Mundi project (Brazil and European Commission).


Filipa Ferrada

Luis M Camarinha-Matos

A System Dynamics and Agent-Based Approach to Model Emotions in Collaborative Networks

Keywords: Emotions, Collaborative Emotions, Collaborative Networks, System Dynamics, Agent-Based Modeling

A good amount of research within the last few decades has been focusing on computational models of emotion and the relationships they have with human emotional processes and how they affect the surrounding environments. The study of emotions is interdisciplinary and ranges from basic human emotion research, like in psychology, to the social sciences studies present in sociology. The interactions between those and the computational sciences are becoming a challenge. One particular challenge that is presented in this paper is the study of collaborative emotions within a Collaborative Network (CN) environment. A CN is composed of different participants with different interaction characteristics such as, expectations, will to cooperate and share, leadership, communication, and organizational abilities, among others. This paper presents an approach, based on system dynamics and agent-based modeling, to model the emotional state of an individual member of the network (via a non-intrusive way).Some simulation results illustrate the approach.

Introduction

Nowadays the area of Collaborative Networks (CNs) is being challenged by the necessity of improvements not only in technical terms but also in relation to social interactions among their participating members. According to some research in sociotechnical systems [START_REF] Baxter | Socio-technical systems: From design methods to systems engineering[END_REF][START_REF] Morris | A system dynamics view of stress: Towards humanfactor modeling with computer agents[END_REF], the failure of large complex systems, such as CNs, is not directly related to the technology neither to the operational systems that compose them. Rather, they fail because they do not recognize the social and organizational complexity of the environment in which these systems are deployed. A survey conducted by Morris et al. [START_REF] Morris | A system dynamics view of stress: Towards humanfactor modeling with computer agents[END_REF] highlights that neglecting social and organizational complexity can cause large, and often serious, technological failures and also recognize that there is a need to provide "human-tech" friendly systems with cognitive models of human factors like stress, emotion, trust, leadership, expertise, or decision-making ability.

Emotion is an important factor in human cognition and social communication [START_REF] Damasio | Descarte's Error: Emotion, Reason, and the human Brain[END_REF] and has been used as a mean of interaction in several fields of science like psychology, sociology, AI, and HCI with the use of emotional agents. In this context, a large amount of research within the last few decades has been focusing on computational models of emotion and the relationship they have with human emotional processes and how they affect the surrounding environments.

A new approach that is expected to improve the performance of existing CNs, namely the collaboration sustainability and interactions, is introduced here by adopting some of the models developed in the psychology, sociology and affective computing areas. The idea is to "borrow" the concept of human emotion and apply it within the context of a CN environment, turning it into a more "human-tech" friendly system without being intrusive, i.e. without violating the intimacy of each member.

When thinking about complex systems such as CNs that are composed of several nodes representing organizations, SMEs, large companies, among others, collaborating with the aim to achieve a purpose, it is reasonable to imagine that all of these entities interacting might also generate "emotions" that would be affected by the dynamics of the collaborative environment. Thus, the emotional state of each participating organization (CN Member) would contribute to the assessment of the collective emotional state of the CN and in this way contribute for its wellfunctioning. The individual emotional state of a member would affect its performance and relationships within the CN [START_REF] Ferrada | An Emotional Support System for Collaborative Networks, in Technological Innovation for Cloud-Based Engineering Systems[END_REF]. In this paper, it is assumed that the modeling of individual emotions will allow a CN Administrator to have a better understanding of each member's reality within the network environment.

In this context, the main research question guiding this work is: What could be a suitable modeling framework to support the concept of collaborative emotions and assist on simulation experiments in order to understand the behavioral dynamics within a collaborative networked environment?

The modeling framework developed to give an answer to this research question is based on System Dynamics and Agent-based Modeling and Simulation. System Dynamics allow the understanding of the behavior of each member over time. Agentbased models and simulation offer a good representation of the real-world environments with appropriate level of complexity and dynamism and allow to explain (simulate) a variety of situations and behaviors through selection of scenarios, which are difficult to analyze with the traditional approaches.

The remainder of this paper is organized as follows: Section 2 identifies the relationship of this work to smart systems; Section 3 gives a brief overview of the psychological and computational models of emotion; Section 4 presents the proposed modeling framework; Section 5 presents the model implementation and the simulation results; and finally Section 6 concludes and identifies the future work.

Relationship to Smart Systems

The current increase of systems complexity and the fast growing hyper connectivity raises the necessity to have mechanisms to assess the behavior of such systems. Their complex dynamism not only incorporates technological and operational mechanisms but also integrates social and organizational constructs [START_REF] Baxter | Socio-technical systems: From design methods to systems engineering[END_REF][START_REF] Morris | A system dynamics view of stress: Towards humanfactor modeling with computer agents[END_REF], becoming sociotechnical systems. These socio-technical systems are composed of complex intelligent sub-systems with a vast degree of autonomy and tendentiously configured as collaborative systems.

In this direction, the work presented in this paper proposes an approach to bridge the gap between the technological and social aspects. The introduction of the concept of collaborative emotions to complex collaborative systems, contributes to a new generation of hyper-connected smart socio-technical systems [START_REF] Eposs | Smart Systems in the Multi-Annual Strategic Research and Innovation Agenda of the JTI ECSEL[END_REF].

Emotions and Computational Models of Emotion

Emotions are unique to each human being and deviations from person to person are the result of each person's genes and the involved environment in conjunction. Emotions can be distinguished from feelings, affects, moods, and sentiments. Emotions are driven by specific events, actions or objects. They are more dynamic and episodic processes than moods, which are generally less intense [START_REF] Mandler | The nature of emotion, in States of mind: Conversations with psychological investigators[END_REF][START_REF] Siegert | Modeling Users' Mood State to Improve Human-Machine-Interaction[END_REF], longer lasting [START_REF] Ekman | Expression and the nature of emotion[END_REF] and not directed at specific stimuli or event [START_REF] Parrott | Emotions in Social Psychology[END_REF], although this distinction is more often made theoretically than empirically [START_REF] Fredrickson | The broaden-and-build theory of positive emotions[END_REF]. Affect is a broader term and can be defined as a valence evaluation in reference to the self [START_REF] Baumeister | How emotion shapes behavior: Feedback, anticipation, and reflection, rather than direct causation[END_REF]. Put simply, affect is an umbrella concept that covers a broad range of feelings, indicating if something is good or bad for oneself. Affect is often used as the denominator for both emotion and mood [12]. Sentiments are, according to Gordon: "socially constructed pattern[s] of sensations, expressive gestures, and cultural meanings organized around a relationship to a social object, usually another person... or group such as a family" [START_REF] Gordon | The sociology of sentiments and emotion[END_REF].

Numerous theories involving the origins, mechanisms and nature of emotions have been generated over the years. This is a challenge since emotions can be analyzed from many different perspectives. All of the classic theories of emotion have fallen under criticism at various times, though many modern theorists still use them as a basis to work from. The most known theoretical models of emotion are [START_REF] Scherer | Emotions are emergent processes: they require a dynamic computational architecture[END_REF]:

1. Physiological or Somatic Emotion Theories [START_REF] James | What is an Emotion?[END_REF][START_REF] Schachter | Cognitive, social and physiological determinants of emotional state[END_REF][START_REF] Zajonc | On the primacy of affect[END_REF]: concede that emotions are primary to cognitive processes [START_REF] Zajonc | On the primacy of affect[END_REF]. Prior to analyzing a perceived object, and even before recording any impressions, the (human) brain is able to immediately invoke an emotion associated with this object. 2. Basic Emotion Theories [START_REF] Damasio | Descarte's Error: Emotion, Reason, and the human Brain[END_REF][START_REF] Darwin | Expression of the emotions in man and animals[END_REF][19][START_REF] Izard | Basic emotions, relations among emotions, and emotion-cognition relations[END_REF][START_REF] Tomkins | Affect as amplification: some modification in theory[END_REF][START_REF] Ekman | An argument for basic emotions[END_REF]: adopt a certain number of basic emotions.

The fundamental assumption is that a specific event triggers a specific affect corresponding to one of the basic emotions producing physiological response mostly through facial expressions. [START_REF] Arnold | Emotion and Personality[END_REF][START_REF] Lazarus | Thoughts on the relations between emotions and cognition[END_REF][START_REF] Lazarus | Emotion and Adaptation[END_REF][START_REF] Scherer | Appraisal Processes in Emotion: Theory, Methods, Research[END_REF][START_REF] Ortony | The Cognitive Structure of Emotions[END_REF]: suggest that before the occurrence of emotion, there are certain cognitive processes that analyze stimuli [START_REF] Lazarus | Emotion and Adaptation[END_REF][START_REF] Frijda | The Emotions[END_REF]. In such a way, the emotions are related to a certain history of a human (agent or robot). The relation to the history should follow the process of recognition (since the objects and their relations to the agent's emotion should be first recognized).

Appraisal Theories of Emotion

Thus, the appraisal theory postulates a certain priority of cognitive processes over emotions. 4. Dimensional Emotion Theories -providing a suitable framework for representing emotions from a structural perspective. These theories established that emotions can be differentiated on the basis of dimensional parameters, such as arousal and valence. Russell [START_REF] Russell | A Circumplex Model of Affect[END_REF][START_REF] Russell | Core affect and the psychological construction of emotion[END_REF] proposes a two-dimensional framework consisting of pleasantness and activation to characterize a variety of affective phenomena such as emotions, mood and feelings. Another approach is the threedimensional framework proposed by Russell and Mehrabian [START_REF] Russell | Evidence for a three-factor theory of emotions[END_REF], which describes emotions based on their level of pleasantness, arousal, and dominance. This model is known as the PAD model [START_REF] Mehrabian | Pleasure-Arousal.Dominance: A General Framework for Describing and Measuring Individual Differences in Temperament[END_REF].

Furthermore, there are also a number of psychological theories of emotion that do not fit exactly into the ones outlined above, focusing on a specific aspect or component of emotion, such as motivation or action preparation, or combined features from the major theoretical orientations (see [START_REF] Moors | Theories of emotion causation: A review[END_REF]).

Computational Models of Emotion are complex software systems conceived to embrace design decisions and assumptions, inherited from the psychological and computational traditions from where they emerged, and synthesize the operations and architectures of some components that constitute the process of human emotions [START_REF] Marsella | Computational models of emotion[END_REF]. In general, computational models of emotion include mechanisms for the evaluation of emotional stimuli, the elicitation of emotions, and the generation of emotional responses, creating means for the recognition of emotions from human users and artificial agents, the simulation and expression of emotional feelings and the execution of emotional responses (or behaviors) [START_REF] Rodríguez | Development of Computational Models of Emotions for Autonomous Agents: A Review[END_REF]. Table 1 illustrates some computational models of emotion according to some theoretical models of emotion. For other reviews of computational models of emotion consult [START_REF] Marsella | Computational models of emotion[END_REF][START_REF] Handayani | Systematic review of computational modeling of mood and emotion[END_REF][START_REF] Kowalczuk | Computational Approaches to Modelling Artificial Emotion -An Overview of the Proposed Solutions[END_REF].

Table 1. Some computational models of emotion

Computational Model Theoretical Model Computational Techniques

CATHEXIS [START_REF] Velásquez | Cathexis: A Computational Model for the Generation of Emotions and their Influence in the Behavior of Autonomous Agents[END_REF] Physiological theory by Damasio [START_REF] Damasio | Descarte's Error: Emotion, Reason, and the human Brain[END_REF] and Appraisal theories by Roseman [START_REF] Roseman | Appraisals of Emotion-Eliciting Events: Testing a Theory of Discrete Emotions[END_REF]. Marvin Minsky's Paradigm [START_REF] Minsky | The Society of Mind[END_REF].

Synthetic Agents

FLAME [START_REF] El-Nasr | FLAME-Fuzzy Logic Adaptive Model of Emotions[END_REF] Combination of OCC [START_REF] Ortony | The Cognitive Structure of Emotions[END_REF] and Roseman's [START_REF] Roseman | Appraisals of Emotion-Eliciting Events: Testing a Theory of Discrete Emotions[END_REF] appraisal theories.

Software Agent (does not incorporate group behavior); Fuzzy logic ALMA [START_REF] Gebhard | ALMA -A Layered Model of Affect[END_REF] OCC appraisal theory [START_REF] Ortony | The Cognitive Structure of Emotions[END_REF] and PAD dimensional theory [START_REF] Mehrabian | Pleasure-Arousal.Dominance: A General Framework for Describing and Measuring Individual Differences in Temperament[END_REF].

Virtual Character or Agent WASABI [START_REF] Becker-Asano | WASABI: Affect Simulation for Agents with Believable Interactivity[END_REF] OCC [START_REF] Ortony | The Cognitive Structure of Emotions[END_REF] and Scherer [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF] appraisal theory and PAD dimensional theory [START_REF] Mehrabian | Pleasure-Arousal.Dominance: A General Framework for Describing and Measuring Individual Differences in Temperament[END_REF].

Software Agents; BDI KISMET [START_REF] Breazeal | Emotion and sociable humanoid robots[END_REF] Physiological theories of Tinbergen and Lorenz and PAD dimensional theory.

Robotic Agent

The work presented in this paper is based on a combination of the WASABI and KISMET's computational models, applied to organizations in a collaborative environment, as presented in the following sections.

Proposed Modeling Framework

In this work, the C-EMO modeling framework is proposed, which was developed to simulate dynamically changing emotions in virtual agents representing members of a CN. As known, members of a CN are organizations that might be dispersed geographically with different purposes and competences, and not human beings, yet they are managed by humans. Emotions are without any doubt related to humans and it is unquestionable that organizations cannot feel emotions in the same way humans do. Nevertheless, the authors believe that a kind of individual emotional state of an organization can be appraised once it makes part of a virtual environment that presupposes interaction and collaboration among its members.

In this context, the main challenges herein are twofold: first what aspects of the emotion theories should be applicable to organizations and second how to capture the stimulus, the evidences and the concepts that should be used in order to mount a model based on human-related emotional theories but applied to organizations. Furthermore we should take into consideration that the applied methods should be non-intrusive, i.e., preserving the privacy of each organization. Another aspect that is also developed, but out of this paper's scope, is the concept of collaborative emotional state that represents the CN's emotional state as a whole.

Fig. 1. C-EMO Modeling Framework

In order to accomplish the proposed challenges, the C-EMO Modeling Framework illustrated in Fig. 1 was designed, comprising four main systems:

 Perception System: Collects the external events, environmental states and stimuli from the CN environment in conjunction with the internal state of the member agent in order to prepare an emotional evidences vector of the individual agent.  Internal System: Maintains the information about the individual agent updated.

 Emotion System: Responsible for assessing the emotional evidences, activating the corresponding agent's emotion and making the emotion manifest to the CN environment.  Behavior System: According to the activated emotion, a behavioral response(s) is designated to the agent and made available to the CN environment.

Due to the complexity and dynamic nature of emotions, the C-EMO framework was modeled using the Agent-Based and System Dynamics methodologies. In Agent-Based Modeling (ABM), complex system is modeled as a collection of autonomous decision-making entities called agents (either individual or collective entities such as organizations or groups). Each agent individually evaluates its situation and makes decisions on the basis of a set of rules. According to Siebers et al. [START_REF] Siebers | Discrete-Event Simulation is Dead[END_REF], an Agent-Based Modeling system should be used when the problem has a natural representation of agents, i.e., when the goal is modeling the behavior and interactions of individual entities in a diverse population in the form of a range of alternatives or futures. In this line, the individual entities are the CN members and the diverse population is the collection of individual members that belong to the collaborative network. Thus, each CN individual member is represented by an agent and the collection of members are represented by a population of agents that "live" inside the agent that represents the Collaborative Network as illustrated below (Fig. 2).

Fig. 2. Agent-Based Model of the Collaborative Network Environment

The model is then composed of two different types of agents; (i) the Individual Member Agent (IMA), which represents each participating member of the CN, and (ii) the CN Agent (CNA), which represents the CN itself and thus the collection of IMA agents that belong to the CN.

The focus of this paper is on the modeling and simulation/estimation of IMA agent's emotions. In the C-EMO Framework it is the Emotion System that is responsible for it.

Emotion System. The Emotion System is composed of two main modules. The first one is responsible for the emotional appraisal and the second one is in charge of activating the corresponding emotion for the individual agent, as depicted in Fig. 3.

The Emotional Assessment module receives, from the Perception System, an emotional evidences vector that is composed of different parameters relative to the IMA agent own data and also with information about the CN environment, through the CNA agent. For this paper purposes, the interactions of the IMA agent and the CNA agent are not taken into consideration. Table 2 describes the parameters composing the emotional evidences.

Fig. 3. Emotion System

The two modules composing the Emotional Assessment are the Individual Emotional Model and the Individual Emotional Simulation. Due to uncertainty of how emotions emerge in a virtual collaborative network and how they influence the collaboration itself, a qualitative approach for modeling emotions is proposed using the methodology of System Dynamics.

Table 2. Emotional Evidences Vector

Parameters Description

Past Valence

The previous value of Valence. Represents one dimension of the past emotional state of the Member and assumes the initial value of the Valence variable.

Past Arousal

The previous value of Arousal. Represents the other dimension of the past emotional state of the Member and assumes the initial value of the Arousal variable.

VBE Total VOs The total number of VOs operating within the VBE. Parameter given by the VBE Management System.

# VOs as Planner

The number of VOs a Member belongs to as a Planner. Parameter given by the VBE Management System.

# VOs as Partner

The number of VOs a Member belongs to as a Partner. Parameter given by the VBE Management System.

Net Income Value

The total earnings or profit of a Member. This is the result of the difference between the total revenue and the total expenses. Parameter calculated by the VBE Management System.

Member Satisfaction

Represents the level of satisfaction of the Member. This parameter is calculated through a questionnaire that is sent to the VBE Members periodically.

Member Needs & Expectations

Represents the level of expectancy a Member has achieved/met regarding its involvement in the VBE. This parameter is calculated through a questionnaire that is sent to the Member when it joins the VBE and whenever the Member wishes, during the VBE lifecycle.

Performance Evaluation

The performance evaluation value of the Member. This parameter is given by the VBE Management System

Belonging Groups

The percentage of groups a Member belongs to in a certain time. This parameter is calculated by the VBE Management System and is the difference between the number of groups joined and the number of groups left by the Member in relation to the total number of existing groups within the VBE.

Shared Knowledge & Resources

The percentage of knowledge and resources a Member shares within the VBE. This parameter is estimated by the VBE Management system and represents the relation between the knowledge and resources a Member shares and the generation of knowledge that result from it.

Communication Frequency

The rate at which the Member communicates with others within the VBE. This parameter is based on a social network analysis of the VBE environment and is given by the VBE Management System.

Communication Intensity

The measure of the effectiveness of communication within the collaborative VBE environment. This parameter, which is also based on the theory and analysis of social networks, is delivered by the VBE Management System.

Invitations to Form VOs

The percentage of invitations to form VOs a Member has, in relation to the total of existent VOs in preparation phase within the VBE.

System Dynamics Modeling (SDM), initially proposed by Jay Forrester [START_REF] Forrester | Industrial Dynamics[END_REF], is a methodology and set of modeling tools that allows the understanding of the behavior of complex systems over time. It deals with internal feedback loops and time delays that affect the behavior of the entire system. It has two model representations: i) causal loop diagrams (qualitative), which are used to depict the basic cause-effect mechanisms of the system and also the circular chains of those mechanisms that form a feedback or closed loop; and ii) stock and flow diagrams (quantitative) that show relationships between variables that have the potential to change over time and distinguishes between different types of variables. The resulting structure of the system, built with stocks and flows, determines the behavior of the system. The corresponding emotion causal loop diagram and stock and flow diagram of the IMA agent is illustrated in Fig. 4 and Fig. 5 respectively. emotional state of the member company is neutral. The values in bold are the ones that change in order to cope with the assumptions. The implementation of the model and the simulation results are presented in Fig. 6. On the left hand side the evolution of the tuple (Valence, Arousal) is presented. On the right hand side, a representation of the circumplex model.

Fig. 6 .

 6 Fig. 6. Simulation runs and results

Table 5 .

 5 Adopted simulation scenario for a Company A

	Parameters	Day 0 -15	Day 15 -30	Day 30 -50	Day 50-70	Day 70
		(A2+A3)	(all)	(A2+A3 +A5)	(none)	(A4)
	Past Valence	0	0.4	0.7	-0.3	-0.3
	Past Arousal	0	-0.3	0.2	-0.1	0.3
	VBE Total VOs	3	3	3	3	3
	VOs as Planner	1	1	1	1	1
	VOs as Partner	1	1	1	1	1
	Net Income Value	100 000	100 000	100 000	100 000	100 000
	Member Satisfaction	50%	100%	100%	20%	20%
	Member Needs & Expect.	100%	100%	20%	20%	20%
	Performance Evaluation	5	5	2	0.5	0.5
	Belonging Groups	50%	100%	100%	20%	100%
	Shared Knowledge & Resou.	50%	100%	100%	20%	100%
	Communication Frequency	0.2	0.2	0.2	0.2	0.2
	Communication Intensity	2.5	2.5	2.5	2.5	2.5
	Invitations to Form VOs	0	1	1	0	0
	Emotion Activation	Contentment	Excitement 	Excitement 	Depression	Frustration

Acknowledgments. This work has been funded in part by the Center of Technology and Systems and the Portuguese FCT-PEST program UID/EEA/00066/2013 (Impactor project), and partly by the GloNet project funded by the European Commission.

Valence

The dynamic model proposed for the representation of individual emotion in CNs is based on the Russell's pair of variables and follows the agent dynamics of Garcia's baseline model for emotional dynamics [START_REF] Garcia | Emotions in Product Reviews -Empirics and Models[END_REF]. Russell's circumplex model [START_REF] Russell | A Circumplex Model of Affect[END_REF], states that all affective states arise from two fundamental variables: Valence, a pleasuredispleasure continuum, and Arousal that is related to the level of activation, uncertainty, novelty, expectation and complexity of the stimuli. Therefore, each individual emotion can be understood as a linear combination of these two dimensions and the IMA agent emotional state described as:

(1)

In this way, the IMA agent expresses its emotional state according to the tuple of values of valence and arousal. Both variables ranging between -1 and 1.

Taking into consideration that members of collaborative networks are organizations, four individual emotions were adopted, two positives and two negatives as described in Table 3. Following the Russell's dimensional approach there is one possible active emotion for each circumplex quadrant that is the result of the equation (1).

Model Implementation and Simulation Results

The first developments of the Emotional System were conducted within the GloNet project [START_REF] Ferrada | An Emotional Support System for Collaborative Networks, in Technological Innovation for Cloud-Based Engineering Systems[END_REF], which was extremely helpful because it was possible to present the concept and the main ideas behind this work to end-users and get some feedback in what concerns its practical usefulness and find alternative approaches [START_REF] Camarinha-Matos | Collaborative Enterprise Networks for Solar Energy[END_REF][START_REF] Camarinha-Matos | Supporting Collaborative Networks for Complex Service-Enhanced Products, in Risks and Resilience of Collaborative Networks[END_REF]. One of the principal insights was that due to the inexistence of available data to validate the work, there is a need for a notable amount of experimentation, and this is the reason why the work evolved to develop, in a second step, a modeling framework based on System Dynamics Modeling and Agent-Based Modeling. In this context, this second implementation was developed using the AnyLogic multi-method simulation tool [START_REF] Anylogic | AnyLogic: Multimethod Simulation Software[END_REF], which comprises, on a single object-oriented platform, three modeling and simulation methods: System Dynamics [START_REF] Forrester | Industrial Dynamics[END_REF], Agent-Based, and Discrete Event [START_REF] Gordon | A General Purpose Systems Simulation Program[END_REF].

In order to make a preliminary evaluation of the proposed emotion model, a scenario was designed for a given Company A. The idea behind the scenario consists in a simulation of the daily life of the company during 100 days. Having into account the lack of a real data set, some assumptions were taken in order to have the first results of the proposed model, as follows.

Table 4. Simulation assumptions

Assumptions A1

A company when invited to form a VO gets enthusiastic.

A2

A company seeing its expectations met tends to stay comfortable.

A3

A company whose performance evaluation is high tends to be pleased.

A4

A company that shares its knowledge and resources and maintains working groups is more energetic.

A5

A company that is satisfied keeps engaged.

During the simulation period, several parameters of the emotional evidences vector change according to the previous assumptions, as shown in Table 5. The initial In a first overall analysis, the proposed model provided promising indications. Nevertheless, it was found that some adjustments are needed on two main aspects: a) the weight of some of the parameters and, b) the time of the emotion decay.

Conclusions and Future Work