Tiwana Varrecchia
email: t.varrecchia@inail.it

Giorgia Chini
email: g.chini@inail.it

Sonny Tarbouriech
email: sonny.tarbouriech@gmail.com

Benjamin Navarro
email: benjamin.navarro@lirmm.fr

Andrea Cherubini
email: andrea.cherubini@lirmm.fr

Francesco Draicchio
email: draicchio@inail.it

Alberto Ranavolo
email: a.ranavolo@inail.it

The assistance of BAZAR robot promotes improved upper limb motor coordination in workers performing an actual use

Keywords: Human-robot collaboration, motor coordination, manual material handling, sEMG

Introduction

Some of the fourth industrial revolution (industry 4.0) technologies are creating a new way of working [START_REF] Lenz | Joint-action for humans and industrial robots for assembly tasks[END_REF], leading towards occupational changes [START_REF] Baltrusch | What about the human in human robot collaboration?[END_REF][START_REF] Pauliková | Analysis of the Impact of Human-Cobot Collaborative Manufacturing Implementation on the Occupational Health and Safety and the Quality Requirements[END_REF] and implying a widesweeping transformation of the whole productive system [START_REF] Paolillo | How to compete with robots by assessing job automation risks and resilient alternatives[END_REF]. In particular, human labor will be increasingly assisted in a variety of industries and manufacturing [START_REF] Ito | Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control[END_REF]. Such assistance is a viable option for ergonomic interventions for lowering biomechanical risk in manual material handling (MMH) (World Robotics, 2019; [START_REF] Ajoudani | Smart collaborative systems for enabling flexible and ergonomic work practices [industry activities[END_REF]. Human-robot collaboration (HRC) technologies, artificial intelligence [START_REF] Pauliková | Analysis of the Impact of Human-Cobot Collaborative Manufacturing Implementation on the Occupational Health and Safety and the Quality Requirements[END_REF][START_REF] Schwab | The fourth industrial revolution[END_REF] and wearable wireless miniaturized sensors (Ranavolo et al., 2018;[START_REF] Alberto | Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges-A Systematic Review[END_REF] are among the physical and digital systems enabling this transformation. In fact, they allow workers to be physically assisted and monitored, even during demanding occupational tasks. HRC technologies are meant to share the workspace with workers, to continually communicate with them, to manage the human partner's flexibility and ergonomics and to physically interact, in order to jointly work together on a common goal [START_REF] Behrens | Upcoming Technologies and Fundamentals for Safeguarding All Forms of Human-Robot Collaboration[END_REF]. Reconfigurable collaborative robots quickly adapt to the workers' intentions and task variations, while simultaneously offloading them from external loads (task-related payloads) and keeping them in ergonomic working circumstances, to improve efficiency and quality of the task execution (SOPHIA project, http://www.project-sophia.eu). Wearable sensors attached to the workers body (e.g., inertial measurement units -IMUs, insoles for measuring reaction forces and bipolar and high-density surface electromyography sensors -sEMG, 3D depth cameras, etc.) measuring his/her motion can greatly improve the accuracy and precision of the biomechanical evaluation, via data-model fusion and machine learning techniques [START_REF] Varrecchia | Trunk Muscle Coactivation in People with and without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities[END_REF][START_REF] Varrecchia | Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities[END_REF][START_REF] Varrecchia | Lifting activity assessment using kinematic features and neural networks[END_REF][START_REF] Varrecchia | Lifting activity assessment using surface electromyographic features and neural networks[END_REF]Ranavolo et al., 2020a[START_REF] Ranavolo | Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human-Robot Collaborative Technologies[END_REF]Ranavolo et al., , 2018Ranavolo et al., , 2017aRanavolo et al., , 2017b)).

Although there exist many hypotheses and scientific evidence regarding the beneficial effects of HRC, more research into human side effects is required. In this new collaborative scenario, human dexterity and flexibility are paired with the repeatability and precision of a robot, resulting in a reduction in human effort [START_REF] Ogenyi | Physical Human-Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators[END_REF]Brookham et al., 2016;[START_REF] Salvini | Benefits of human-robot interaction[END_REF] but also in an alteration of human motor strategy. From a broad perspective, although collaborative work (for example, annex A of ISO/DIS 11228-1) is a widespread method for lowering work-related musculoskeletal disorders (WMDs), the scientific literature reveals conflicting findings on its efficacy, due to factors affecting team capacity, autonomy, and coordination (Chini et Dennis and Barret, 2003a, 2003b, 2002; [START_REF] Lee | A study of efficiency of two-man lifting work[END_REF]).

In the latter case, the team must flawlessly coordinate, whereas a single individual can move the load freely and smoothly.

To measure motor coordination in body segments and joint stabilization during dynamic tasks, a common approach is muscle co-activation [START_REF] Chini | Trunk muscle co-activation and activity in one-and two-person lifting[END_REF][START_REF] Le | A review of methods to assess coactivation in the spine[END_REF][START_REF] Ranavolo | A new muscle co-activation index for biomechanical load evaluation in work activities[END_REF]2018;[START_REF] Granata | Cost-Benefit of Muscle Cocontraction in Protecting Against Spinal Instability[END_REF].

Compressive and shear forces across the joints increase when significant antagonist activations counteract the agonist actions, producing moments which do not contribute to the required net joint moments [START_REF] Lewek | Control of Frontal Plane Knee Laxity During Gait in Patients with Medial Compartment Knee Osteoarthritis[END_REF][START_REF] Childs | Alterations in Lower Extremity Movement and Muscle Activation Patterns in Individuals with Knee Osteoarthritis[END_REF][START_REF] Griffin | The Role of Mechanical Loading in the Onset and Progression of Osteoarthritis[END_REF][START_REF] Collins | The Impact of Stochastic Resonance Electrical Stimulation and Knee Sleeve on Impulsive Loading and Muscle Co-contraction During Gait in Knee Osteoarthritis[END_REF]. Then, co-activation may become functionally unfavorable [START_REF] Macaluso | Contractile Muscle Volume and Agonist-Antagonist Coactivation Account for Differences in Torque Between Young and Older Women[END_REF][START_REF] Brookham | The Utility of an Empirically Derived Coactivation Ratio for Muscle Force Prediction Through Optimization[END_REF]. For this reason, the use of HRC technologies must be proved primarily during subtasks in which the worker physically interacts with the robot, affecting the physiological motor strategy. Furthermore, no evidence of a motor coordination improvement exists even when the assistance is provided by an advanced collaborative robot (cobot) capable of bimanually handling a heavy object, while simultaneously moving in the workspace. Indeed, even though cobots are a potentially useful tool for enhancing worker ergonomics and are frequently used in assembly-line production environments [START_REF] Ajoudani | Smart collaborative systems for enabling flexible and ergonomic work practices [industry activities[END_REF][START_REF] Shi | Levels of human and robot collaboration for automotive manufacturing[END_REF][START_REF] Papakostasa | Industrial applications with cooperating robots for the flexible assembly[END_REF][START_REF] El Makrini | Working with walt: How a cobot was developed and inserted on an auto assembly line[END_REF], they tend to focus on lightweight tasks and are rarely in direct physical contact with humans. Indeed, they only share a workspace with their human partners. In our study, we developed and evaluated a more ambitious scenario in which the cobot works directly in contact with people on a high payload operation. The effectiveness of this collaboration needs to be evaluated not only in the reduction of physical effort, but also in the altered motor strategy adopted by the worker. In fact, the physical interaction could also lead to an ineffective motor strategy which could induce, for istance, upper-arm stiffness. Indeed, collaborative tasks have been shown to introduce critical issues related to autonomy, team skills and coordination between team members [START_REF] Chini | Trunk muscle co-activation and activity in one-and two-person lifting[END_REF][START_REF] Anton | The effect of lift teams on kinematics and muscle activity of the upper extremity and trunk in bricklayers[END_REF]van der Molen et al., 2012).

Since, to the best of the authors' knowledge, no research has been done on upper limb co-activation and muscle activation during a hybrid worker-robot MMH, we hypothesize this collaboration may reduce upper limb muscle coactivation and muscular activation. Furthermore, we believe that studying the human-robot interaction throughout the execution of a real-world use case, extrapolated and recreated from an industrial context, will allow us to draw more general conclusions regarding its efficacy.

As a result, the purpose of this research was to evaluate upper limb muscle coordination and activation in workers performing an actual use-case MMH, by comparing a scenario where the workers are helped by a dual-arm cobot with a scenario without the cobot.

In this work, since high payloads (above 15 kg) are incompatible with most current day CoBot arms [START_REF] Elprama | Attitudes of factory workers towards industrial and collaborative robots[END_REF], we relied on the dual-arm mobile cobot BAZAR [START_REF] Cherubini | A collaborative robot for the factory of the future: BAZAR[END_REF], which is equipped with two 7-DOF Kuka LWR4 arms attached to a Neobotix MPO-700 omnidirectional mobile base. A Microsoft Kinect camera is mounted on BAZAR's head, and an ATI Mini 45 force/torque sensor is mounted on each of the robot's wrists. Each wrist is also equipped with a 3D printed concave part, designed to fit the shape of the part to manipulate, and denoted 'hand' in the

Materials and Methods

Participants

Eleven participants (5 females and 6 males; age: 27.73±5.99 years; body mass index [BMI]: 23.06±3.93 kg/m 2 ) took part in the study. The study was carried out at the University of Montpellier in accordance with the Helsinki Declaration and authorized by the University of Montpellier EuroMov's Laboratory Ethics Committee (Protocol number IRB-EM 2103A). Exclusion criteria included inability to give informed written consent, history of musculoskeletal disorders, upper limb, lower limb or trunk surgery, orthopedic or neurological diseases, disorders of the vestibular system, visual impairments or back pain, current pregnancy, current pharmacological treatment, and obesity.

Inertial Measurement Unit and Electromyographic recordings

The Inertial Measurement Unit (IMU) and Electromyographic (sEMG) data were acquired simultaneously, synchronizing the two systems.

IMU recordings

The Xsens MVN Link system (Xsens, Enschede, The Netherlands) was used to record whole-body kinematics on participants. The MVN Awinda system motion analysis system includes a protocol for measuring whole body kinematics, comprising 17 IMUs placed all over the body to measure the orientation of body segments: 1 on the head, 1 on the sternum, 1 on the pelvis (at the L5/S1 level), and 1 bilaterally on each scapula, upper arm, forearm, hand, thigh, shank, and foot. To achieve proper sensor placement, Xsens MVN whole-body lycra suits in various sizes (M to XXL) were used. Before beginning recordings, the Xsens system was calibrated using the "N-pose and walk" calibration technique for each participant. The Xsens MVN Analyze software (version 2018.0.0) was used to record Xsens data at 60 Hz.

sEMG recordings

The surface myoelectric signals were acquired using a 16-channel Wi-Fi transmission surface electromyograph (FreeEMG300 System, BTS, Milan, Italy) at a sampling rate of 1000 Hz. A Hamming bandpass filter (10-400Hz cut-off frequencies, and a common mode rejection ratio of 100 dB) was used for pre-processing filtering and denoising. Following skin preparation, bipolar Ag/AgCl surface electrodes (diameter 2 cm, H124SG Kendall ARBO, Tyco healthcare, Neustadt/Donau, Germany) were prepared with electroconductive gel and placed over the muscle belly in the direction of the muscle fibers (distance of 2 cm between the centers of the electrodes) in accordance with European recommendations for surface electromyography [START_REF] Hermens | Development of recommendations for SEMG sensors and sensor placement procedures[END_REF] and the atlas of muscle innervation zones.

Eight bipolar electrodes were placed over the following muscles: anterior deltoideus (AD), posterior deltoideus (PD), biceps brachii caput longum (BBCL), triceps brachii caput longum (TBCL), flexor carpi radialis (FCR), extensor carpi radialis (ECR), rectus abdominis superior (RAS), and erector spinae longissimus (ESL). These muscles were chosen because of their role as flexors and extensors of the shoulder, elbow, wrist, and trunk joints, respectively.

Experimental procedure

The goal of our experiment was to reproduce an industrial use case proposed by the SME HANKAMP (Netherlands) and shown in Figure 1 (A). The apparatus (shown in Figure 1) consisted of: the BAZAR collaborative robot (only referred to as BAZAR in the following), 4 tables, 1 cylindrical load (5 kilograms) to be displaced and 1 cleaning brush. The task consists in displacing the cylindrical load between the four tables (action which requires lifting, placing and carrying the load) in a predefined order, and cleaning it with the brush (action which requires holding the load in the air with one hand). The experiments are shown in the video available at: https://youtu.be/vul8iLO0Sdw. Each participant completed the task six times: three times without BAZAR and three times with BAZAR. When the task is performed with BAZAR, it eliminates some human activities (lifting, placing and carrying the load). Indeed, it moves the load from table 2 to table 3 (closer to the humans' starting position and at height equal to 100 cm) and from table 3 to table 4 to avoid humans lifting the load from table 2 and 3 respectively. Furthermore, in the brushing phase, BAZAR helps the human, by lifting the load, while he/she brushes it. We randomly ordered the two conditions (with and without BAZAR), for each subject. Before the participant performed either condition for the first time, we instructed him/her by showing a video of the task, and by letting him/her execute it once or until he/she was able to perform it on his/her own, without recording. All subjects performed and completed the task independently the first time without the need to repeat it.

Task without BAZAR

The task without the use of BAZAR can be separated into the following sub-tasks (Figure 2A):

• standing 1: the participant was standing with his/her upper limbs lying along the trunk near table 1;

• walking 1: the participant walked to table 2;

• standing 2: the participant stood in front of table 2 with his/her upper limbs lying along the trunk, while waiting for an acoustic signal;

• lifting+carrying+placing 1: the participant lifted the load from table 2, carried it and placed it on table 1;

• standing 3: the participant stood in front of table 1, with his/her upper limbs lying along the trunk, waiting (approximately 5 seconds) for an acoustic signal;

• lifting+carrying+placing 2: the participant lifted the load, carried it and placed it on table 3;

• standing 4: the participant stood in front of the table 3 with his/her upper limbs lying along the trunk;

• lifting 1: the participant lifted the load and the brush from table 3;

• brushing: the participant cleaned the load with the brush;

• placing +lifting 2: the participant placed the load back on table 3, then lifted it again;

• moving on table: the participant placed the load on table 4;

• standing 5: the participant stood in front of table 4 with his/her upper limbs lying along the trunk;

• walking 2: the participant walked to table 1;

• standing 6: same as static 1.

Task with BAZAR

The task in collaboration with the cobot can be separated into the following sub-tasks (Figure 2B):

• standing 1: the participant stood with his/her upper limbs lying along the trunk in front of table 1 • walking 1: the participant walked to table 3;

• standing 2: the participant stood with his/her upper limbs lying along the trunk in front of table 3, while the robot moved the load from table 2 to table 3;

• lifting+carrying+placing 1: the participant lifted the load from table 3, carried it and placed it on table 1;

• static 3: the participant stood in front of table 1, with his/her upper limbs lying along the trunk, waiting (approximately 5 seconds) for an acoustic signal;

• lifting+carrying+placing 2: the participant lifted, carried and placed the load on table 3;

• standing 4: the participant stood in front of table 3, while the robot lifted the load;

• brushing: while the robot was holding the load, the participant lifted the brush from table 3, and used it to clean the load with his/her left hand;

• placing+walking 2: the participant placed the load on table 3 and walked to table 1 while the robot moved the load to table 4;

• standing 5: same as static 1.

BAZAR description

BAZAR's framework was programmed in C++ within RKCL (Robot Kinematic Control Library, https://gite.lirmm.fr/rkcl) [START_REF] Cherubini | A collaborative robot for the factory of the future: BAZAR[END_REF]. The framework was executed on a computer with an Intel(R) Xeon(R) E5-2620 v3 CPU running Linux with the PREEMPT-RT patch. We used the Fast Research Interface Library (FRI) (https://cs.stanford.edu/people/tkr/fri/html/) to communicate with BAZAR's Kuka arms at 200 Hz.

The framework is composed of:

• a unique kinematic control loop, which sequentially executes the various processes;

• several drivers in charge of exchanging data between each hardware component (e.g., Kuka arms, mobile base, force/torque sensors, camera, etc.) and the controller.

We designed the framework so that distinct hardware components could operate together, even if their control rates are different. This is possible, by parallelizing execution of the kinematic controller and of the components' communication drivers. Each process runs independently and at varying frequency: the drivers are triggered by the reception of new data coming from the sensors, so that the time rate is fixed for each component. The kinematic control loop time step, instead, is arbitrary and can be freely set by the user. The best performances are obtained when the control loop time step is set equal to the time rate of the fastest driver, so that the driver's bandwidth is not artificially limited.

In RKCL, each C++ application loads a YAML (https://yaml.org/) configuration file at launch time, with the parameters to be used. In this work, the scenario for the whole application is configured as a list of YAML subfiles, each of them defining a robotic action. To characterize an action, we specify two frames: the control frame to be moved by the robot, and the reference frame with respect to which the control frame is moved.

To define the reference frame, in some cases we relied on computer vision and QR codes. We used the Microsoft Kinect V2 RGB-D camera in BAZAR's head and on the ArUco Library (Garrido-Jurado et al., 2014), which provides real-time marker-based 3D pose estimation.

To generate trajectories between reference frames (i.e., waypoints), we used the Reflexxes Motion Library [START_REF] Kröger | Opening the door to new sensor-based robot applications-The Reflexxes Motion Libraries[END_REF] with the following parameters: maximum translational velocity=0.5m/s, maximum rotational velocity=0.5 rad/s, maximum translational acceleration=0.2 m/s 2 , and maximum rotational acceleration=0.2 rad/s 2 .

Another fundamental tool, which we used to make the robot safe and compliant, is admittance control [START_REF] Hogan | Impedance control: An approach to manipulation[END_REF].

Admittance control deforms the trajectories of each robot hand by passing the forces/torques measured at that hand, through a spring-damper model. The deformation will depend on pre-tuned damping (B) and stiffness (K) gains. These two gains can be tuned differently for forces (which induce deformations along the hand translational degrees of freedom)

and torques (which induce deformations along the hand rotational degrees of freedom). By varying the gains, we have designed three admittance control modes: pose, force, and damping control [START_REF] Cherubini | A collaborative robot for the factory of the future: BAZAR[END_REF], outlined in Table 1. In this work, as outlined in 2.3.2 and in Figure 2B, BAZAR had to execute four sub-tasks. We hereby recall the subtasks and specify the actions composing each subtask:

Degrees of freedom

1) Moving the load from table 2 to table 3 (during subtask 'static 2'). Actions: position arms before grasp, grasp load, lift load, turn base, place load, position arms to release load.

2) Lifting the load, with the participant standing in front of table 3 (during 'static 4'). Actions: position arms before grasp, grasp load, lift load.

3) Holding the load while the participant brushed it (during 'brushing'). Action: move load during HRC.

4) Moving the load from table 3 to table 4 after the participant finished brushing (during 'placing+walking 2').

Actions: turn base, place load, position arms to release load.

In summary, the whole experiment relied on the following actions: position arms (e.g., before grasp, to release load), grasp load, turn base, move the load (e.g., place it or lift it, including during HRC). We hereby describe each of these actions.

Position arms: for this action, since the hands are not constrained to one another (i.e., they are not holding anything) we control them independently with pose control. We set a control frame on each hand and use the appropriate QR code as reference frame. The two hands move together towards/away from the load, although each one has a distinct target.

Grasp load: again, we control the hands independently; yet here, we use force control to make the hands move to contact the load -for grasping. Thanks to force control and to the concavities on its hands, BAZAR was able to grasp the load even if its position was not exactly known. In practice, we observed that it could tolerate offsets up to 2 cm from the expected position.

Turn base: when the robot base moves, its proprioception (i.e., the odometry) cannot guarantee correct localization.

Therefore, we use the ArUco markers to stop the robot when it is facing the load. We placed one marker on each table, at a predefined pose relative to the lifting/placing locations, and asked the participants to place the load at the indicated location after each operation. The control frame is attached to the QR code, and the reference frame is at the mobile base center.

Move load: for this action, we chose the cooperative task representation [START_REF] Chiacchio | Direct and inverse kinematics for coordinated motion tasks of a twomanipulator system[END_REF]. Instead of controlling each arm independently, we considered the robot as a unique entity and described the operations via an absolute task (which controls the motion of the robot in the workspace), and a relative task (which regulates the relative motion between the two hands). As soon as the hands are both in contact with the load, we switch from independent to collaborative control. The grasping and holding of the load is managed by the relative task, which maintains a constant force on the load, while the absolute task moves the load in the workspace. When there is no interaction with the human operator, the absolute task is fully position controlled (i.e. aims at reaching a target pose without considering force measurements), while during HRC we controlled the vertical translation and one horizontal rotation degree of freedom in damping mode.

By compensating gravity effects (Tarbouriech et al 2019), only the forces applied by the human make the robot move.

This allows him/her to move the load with minimal effort.

Isometric maximum voluntary contractions (iMVCs)

To obtain the isometric maximal voluntary contractions (iMVCs), we asked each participant to perform a specific exercise twice with each muscle (Hermens et Each subject sat comfortably, placed the proximal load of the arm on a table at shoulder height with the shoulder flexed at 90° and an elbow flexion such that the forearm was at 120° from the upper arm, and performed a forearm isometric flexion against resistance for measuring the BBCL iMVC [START_REF] Farina | Two methods for the measurement of voluntary contraction torque in the biceps brachii muscle[END_REF]) and an isometric forearm extension for measuring the TBCL iMVC [START_REF] Farina | Two methods for the measurement of voluntary contraction torque in the biceps brachii muscle[END_REF].

To acquire the iMVC of the FCR, we asked each participant to sit comfortably, place the forearm on a table with an elbow flexion of around 45°, and perform an isometric hand flexion against resistance [START_REF] Rota | Reliability of EMG normalisation methods for upper-limb muscles[END_REF]. From the same position, each participant performed an isometric hand extension against resistance for measuring the ECR iMVC [START_REF] Rota | Reliability of EMG normalisation methods for upper-limb muscles[END_REF].

Each subject was placed in a supine position and performed an isometric contraction during trunk flexion against resistance, so we could obtain the RAS iMVC, and each subject was placed in a prone position, lying on a mattress, and performed an isometric contraction during trunk extension against resistance, so we could obtain the ESL iMVC (Vera-Garcia et al., 2010).

Data Analysis

Data were processed using Matlab (version 2018b 9.5.0.1178774, MathWorks, Natick, MA, USA).

Sub-tasks detection

To segment each task in the sub-tasks defined in Sec. 2.3 and shown in Figure 2, we used the vertical velocities of the feet and hands. Figure 3

𝑇𝑀𝐶𝑓(𝑑(𝑘), 𝑘) = +1 - 1 1 + 𝑒 !"#(%(&)!(.*) 0 . (∑ 𝑠𝐸𝑀𝐺 + (𝑘)/𝑀) , +-" # 𝑚𝑎𝑥 +-"…, [𝑠𝐸𝑀𝐺 + (𝑘)]
where 𝑑(𝑘) is the mean of the differences between the kth samples of each pair of sEMG signals:

𝑑(𝑘) = ∑ ∑ |𝑠𝐸𝑀𝐺 ! (𝑘) -𝑠𝐸𝑀𝐺 " (𝑘)| # "$!%& #'& !$& 𝐽(𝑀!/(2! (𝑀 -2)!)) ( 1 )
In the above equations, J is the length of the signal (400 samples in this case); M is the number of considered muscles; 𝑠𝐸𝑀𝐺 ! (𝑘) and 𝑠𝐸𝑀𝐺 " (𝑘) are the k th sample values of the envelope of the sEMG signals of the m th and n th muscles, respectively. TMCf was computed for each antagonistic muscle pair in each joint (shoulder, elbow, wrist, and trunk), for the entire arm, and for all acquired muscles. More in detail, we calculated:

• TMCfAD-PD: coactivation of AD and PD muscles, for the shoulder joint;

• TMCfBBCL-TBCL: coactivation of BBCL and TBCL muscles, for the elbow joint;

• TMCfFCR-ECR: coactivation of FCR and ECR muscles, for the wrist joint;

• TMCfRAS-ESL: coactivation of RAS and ESL muscles, for the trunk joint;

• TMCfARM: coactivation of AD, PD, BBCL, TBCL, FCR and ECR muscles, arm coactivation as a whole;

• TMCfALL: coactivation of all acquired muscles, overall coactivation.

For each of the above-mentioned TMCf, we used the mean (for characterizing the overall task execution) and maximum (as a punctual information) values during the brushing sub-task as coactivation indices. Furthermore, for each of the above-mentioned TMCf, we also calculated the ADPF for the 10, 50, and 90 th percentiles. The data from the three repetitions for each condition were averaged for each subject.

Statistical Analysis

To check differences between the two conditions (with and without the cobot), the statistical analysis was carried out using Matlab (version 2018b 9.5.0.1178774, MathWorks, Natick, MA, USA). The Shapiro-Wilk test was used to determine the normality of the data distribution for each parameter. The paired sample t-test or the non-parametric Wilcoxon test (if the data was not normally distributed) was then used to evaluate whether the help of the cobot had determined significant changes in each parameter. The significance level for all statistical analyses was set at p-value < 0.05.

Results

For each condition (with and without robot), figures 4, 5 and 6 show the mean and standard deviation of mean values (Figure 4), max values (Figure 5) and ADPF (Figure 6) of each muscle (2 nd and 3 rd column) and of each evaluated TMCf (1 st and 4 th column), respectively. Specifically, we reported, for each row in the 2 nd and 3 rd columns, the values for antagonist muscles and in the 4 th column, the corresponding TMCf for each pair of these antagonist muscles. Furthermore, in the first column we reported the values of the TMCfARM and TMCfALL. Considering the mean values (Figure 4), significant effects of the presence of robot for each muscle except for AD and for each evaluated TMCf were found (Table 2): the mean values of muscles and of TMCf significantly decrease when the task is performed with the cobot.

Similarly, considering the max values (Figure 5), for each muscle except for AD and FCR and for each evaluated TMCf, we found significant effects of the presence of the cobot (Table 3): the max values of muscles and of TMCf significantly decrease when the task is performed with the cobot. The 10 th , 50 th and 90 th percentiles of ADPFs are reported in Table 4. Considering the 10 th percentile (first dotted horizontal line in each plot of Figure 6), significant effects of the presence of cobot for each muscle (AD: p=0.001 , PD: p=0.001 , BBCL: p= 0.005, TBCL: p=0.002, FCR: p=0.001, ECR: p<0.001, RAS: p=0.025 and ESL: p=0.001) and for each evaluated TMCf (TMCfAD-PD: p=0.001, TMCfBBCL-TBCL: p<0.001 , TMCfFCR-ECR: p=0.001, TMCfRAS-ESL: p=0.001, TMCfARM: p<0.001 and TMCfALL: p=0.001) were found: the mean values of ADPF of muscles and of TMCf significantly decrease when the task is performed with the cobot. Considering the 50 th percentile (second dotted horizontal line in each plot of Figure 6), significant effects of the presence of cobot for FCR (p= 0.007) and ECR (p=0.013) muscles and for TMCfFCR-ECR (p=0.005), TMCfARM (p= 0.010) and TMCfALL (p=0.010) were found: the 50 th percentile of ADPF of these muscles and of TMCf significantly decrease when the task is performed with the cobot. Considering the 90 th percentile (first dotted horizontal line in each plot of Figure 6), significant effects of the presence of cobot for BBCL (p=0.001), TBCL (p=0.027) and ESL (p=0.002) muscles and for TMCfBBCL-TBCL (p=0.003), TMCfARM (p=0.008) and TMCfALL (p=0.002) were found: the mean values of ADPF of these muscles and of TMCf significantly increase when the task is performed with the cobot.

Mean±SD

Discussion

In this work, we investigated a hybrid worker-robot MMH, throughout the execution of a real-world use case, extrapolated and recreated from an industrial context, to examine the changes of worker motor coordination and physical effort. In detail, we examined upper limb coactivation and muscle activation while doing the task with and without the dual-arm mobile cobot BAZAR, to see how BAZAR could help improve coordination.

Overall, our findings suggest that using BAZAR to perform the specific MMH improves coordination and reduces the workers' physical effort as compared to performing the identical task without BAZAR. In particular, there is a reduction in both upper limb co-activation and muscle activation (Fig. 4 and 5, Table 2 and3).

The confirmation of our hypothesis shows that the worker executes the task with less physical effort and improved motor coordination throughout the subphases of the work cycle where there is true collaboration between this bimanual collaborative robot and the worker. This suggests that the deployment of cobots will represent innovative ergonomic interventions for reducing the occurrence of WMDs in the industrial setting. This could have a positive impact on employee absences, moves and early work interruption.

The TMCf was significantly lower in each antagonistic muscle pair of the shoulder, elbow, wrist, and trunk joints and for the entire arm and for all acquired muscles. This reduction was highlighted by taking into account not only the mean value (Fig. 4, Table 2) (Ranavolo 2021), which expresses what happens throughout the task and provides information on the task's overall execution, but also the maximum value (Fig. 5, Table 3) (Ranavolo 2021), which is a punctual index that provides information on the maximum value of simultaneous antagonist muscle activation during the task. These findings on muscle joint co-activation show that designing a hybrid worker-robot MMH promotes a significant reduction in antagonist muscle activations and in counteracting agonist actions, lowering moments that do not contribute to the needed net joint moments [START_REF] Childs | Alterations in Lower Extremity Movement and Muscle Activation Patterns in Individuals with Knee Osteoarthritis[END_REF][START_REF] Collins | The Impact of Stochastic Resonance Electrical Stimulation and Knee Sleeve on Impulsive Loading and Muscle Co-contraction During Gait in Knee Osteoarthritis[END_REF][START_REF] Griffin | The Role of Mechanical Loading in the Onset and Progression of Osteoarthritis[END_REF][START_REF] Lewek | Control of Frontal Plane Knee Laxity During Gait in Patients with Medial Compartment Knee Osteoarthritis[END_REF].

Compressive loading across the joints is also reduced as a result of the reduced muscle co-activation (Granata andMarras, 1995, 2000;[START_REF] Marras | Spine loading during trunk lateral bending motions[END_REF][START_REF] Marras | Electromyographic studies of the lumbar trunk musculature during the generation of low level trunk acceleration[END_REF][START_REF] Ranavolo | A new muscle co-activation index for biomechanical load evaluation in work activities[END_REF]Ranavolo et al., , 2018b)). Finally, lower coactivation of upper-limb and trunk muscles results in more functionally advantageous and efficient motions, by lowering physiological and metabolic costs, resulting in power growth [START_REF] Franchi | Skeletal Muscle Remodeling in Response to Eccentric vs. Concentric Loading: Morphological, Molecular, and Metabolic Adaptations[END_REF].

Considering the muscular activity of upper limb, a significant reduction is shown for each investigated muscle except for the mean (Fig. 4, Table 2) and maximum (Fig. 5, Table 3) values of anterior deltoideus muscle (which is a shoulder flexor muscle) and for the maximum value of flexor carpi radialis muscle (which is a wrist flexors muscle). These results could be due to a high variability of activation of these muscles also caused by a different posture and maintenance of the object for the participants, during the execution of the task without BAZAR.

The APDF (Figure 6) shows the trend of muscular activities in % MVC and of % of coactivation. When BAZAR collaborates with subjects the mean values of 10th percentile are lower respect to the condition without BAZAR for each muscle (Figure 6 and Table 4). The ADPFs increase more slowly when subjects perform the task without BAZAR with the initial muscle activities always higher with respect to the condition with BAZAR. Furthermore, when BAZAR helps the subjects, the mean values of 10 th percentile never exceeded the limits (5 % MVC) of acceptable muscular load for the static load level (10 th ) that were found in studies of muscular endurance during dynamic work [START_REF] Jonsson | Measurement and evaluation of local muscular strain in the shoulder during constrained work[END_REF].

Additionally, with the exception of the AD and ECR muscles, the mean values of the median (50 th percentile) load level are lower when BAZAR is used (Fig. 6 and Table 4), and they never go over the limit for static load levels (14%, [START_REF] Jonsson | Measurement and evaluation of local muscular strain in the shoulder during constrained work[END_REF]. Furthermore, the peak loads (90 th percentile) do not exceed the limit (70%) in both conditions with and without BAZAR.

The results of this study show that the HRC technologies which share the workers' workspace, not only offload them from external loads and improve efficiency and quality of the task execution; they also allow a reduction of the worker physical effort when s/he physically interacts with the robot, since the latter affects the worker's physiological motor strategy.

Indeed, "The experimental session involved the implementation of a hybrid scenario in which a human and a robot collaborate. In addition to subphases when the cobot replaces the worker, there is a subphase where worker and cobot interact physically. In this subphase, according to our findings, the worker's physical effort is reduced (a rather obvious effect), but it also enables the worker to use a more coordinated motor strategy. As a result, giving a load to the machine improves coordination, rather than reducing it, and enables the worker to cooperate and coordinate with the robot partner.

Limitation and future developments

The small sample size (11 subjects) is a limitation of the study due to the fact that the experimental session was very complex and long since it involved performing the work task twice, without and with the BAZAR robot. Furthermore, the experiments were possible only during a limited time period, when two research groups could work together at the University of Montpellier, sharing their knowledge and technologies within the European Union's Horizon 2020 SOPHIA project (please see the "funding" section). Future studies should include larger sample sizes, useful for confirming the results of this study, and we will include subjective information collected through the administration of questionnaires related to usability and acceptability of the cobot by the worker. Furthermore, future experimental sessions should include subjective effort indicators such as subjective workload (e.g., NASA Task Load Index, [START_REF] Hart | Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research[END_REF] or ratings of perceived exertion (e.g., fatigue scale, [START_REF] Borg | Psychophysical bases of perceived exertion[END_REF]. We should also verify whether these subjective indicators are correlated to the objective measures presented in this article.

Furthermore, in this study, BAZAR moves in the space interacting with the worker thanks to the presence of QR codes. This could be considered as a limitation of the study. A future development could include the use of a robot that supports the suitability for individualization that is the possibility of adapting to the needs and abilities of the worker [START_REF] Ajoudani | Smart collaborative systems for enabling flexible and ergonomic work practices [industry activities[END_REF]. Indeed, the new opportunity represented by the fourth industrial revolution is allowing the design of flexible and reconfigurable hybrid work environments, thanks to the use of innovative wearable sensors for monitoring and feedback (e.g. haptic stimuli to specific areas of the body). Exoskeletons and cobots embed new instrumental-based tools (i.e. myoelectric interfaces which monitor muscle activation amplitude and fatigue), with advanced interaction and sensing capabilities. These tools can evaluate the workers' physical states, assess their effectiveness on motor performance, respond to their intentions in a timely manner and offload them from internal loadings (Lotti et In the panel A the sub-tasks of the task without BAZAR are shown: standing 1; walking 1; standing 2; lifting+carrying+placing 1; standing 3; lifting+carrying+placing 2; standing 4; lifting 1; brushing; placing +lifting 2; moving on table; standing 5; walking 2; standing 6. In the panel B the sub-tasks of the task with BAZAR are shown: standing 1; walking 1; standing 2; lifting+carrying+placing 1; lifting+carrying+placing 2; standing 4; brushing; placing+walking 2; standing 5.

Figure 3 Alt Text:

Panel A shows the vertical velocity of the right (blue) and left (red) foot, the vertical velocity of the left hand, the raw sEMG signal for biceps brachii caput longum and raw sEMG signal for triceps brachii caput longum for the task without BAZAR. In each plot there are dashed vertical lines indicating the sub-tasks (standing 1; walking 1; standing 2; lifting+carrying+placing 1; standing 3; lifting+carrying+placing 2; standing 4; lifting 1; brushing; placing +lifting 2; moving on table; standing 5; walking 2; standing 6), which are numbered on the figure and listed in the legend.

Panel B shows the same data referring to the task with BAZAR (sub-tasks: standing 1; walking 1; standing 2; lifting+carrying+placing 1; lifting+carrying+placing 2; standing 4; brushing; placing+walking 2; standing 5).

  rest of the paper. BAZAR is being assessed as a cobot in the context of "SOPHIA-Socio-Physical Interaction Skills for Cooperative Human-Robot Systems in Agile Production", a project funded by the European Union's Horizon 2020 Research and Innovation Program, under grant agreement No. 871237 (http://www.project-sophia.eu),.SOPHIA attempts to contribute to the development of a new generation of HRC technologies which could improve human ergonomics during MMH. The project's technological innovations will allow for improved management of the occupational health challenges posed by WMDs in Europe, which include sick leave, disability, and early job interruption (Van Der Beek et al., 2017; Eurofound, 2015).

Fig. 1 .

 1 Fig. 1. Industrial use case scenario (A) and laboratory scenario (B).

Fig. 2

 2 Fig. 2 Sub-tasks of the task without (A) and with (B) BAZAR.

Table 1 .

 1 Damping (B) and Stiffness (K) gains along the translational and rotational degrees of freedom for pose, damping and force control modes.

  shows this division in sub-tasks. Starting with the feet velocity signals we conducted a first segmentation, and then we identified the brushing sub-task using the hand velocity signals. This sub-task is the HRC part, which we analyzed in this study to determine the cobot effect on the task. During the task, the IMU and sEMG signals were time-normalized and then interpolated according to the brushing sub-task duration (400 samples)[START_REF] Ranavolo | A new muscle co-activation index for biomechanical load evaluation in work activities[END_REF] Ranavolo et al., 2018;[START_REF] Varrecchia | Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities[END_REF][START_REF] Varrecchia | Trunk Muscle Coactivation in People with and without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities[END_REF]) using a linear interpolation procedure to allow a comparison between tasks with different durations.2.4.2 Bipolar sEMG ProcessingTo reduce low-frequency artifacts and high-frequency noise, we band-pass filtered the sEMG signals recorded for both iMVC and tasks, with a 3rd order Butterworth filter of bandwidth 25-400 Hz[START_REF] Butler | The Interpretation of Abdominal Wall Muscle Recruitment Strategies Change when the Electrocardiogram (ECG) is Removed from the Electromyogram (EMG)[END_REF][START_REF] Drake | Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques[END_REF]. Then, we filtered the signals with a Notch's filter with cutoff frequency 50Hz, to remove noise due to network signal. The envelope of each task's sEMG signals was then extracted using full-wave rectification and low-pass filtering with a 4th order Butterworth filter at 5 Hz[START_REF] Winter | Biomechanics and Motor Control of Human Movement[END_REF]. The average iMVC peak value for each muscle has been used to normalize the sEMG envelope (Ranavolo 2021; Burden 2010;[START_REF] Marras | A non-MVC EMG normalization technique for the trunk musculature: Part 1. Method development[END_REF]. The mean (as the information regarding the overall execution of the task) and maximum (as a punctual information) values within the brushing sub-task were determined as muscle parameters. Furthermore, for each muscle we have calculated also the amplitude probability distribution function (ADPF,[START_REF] Jonsson | Measurement and evaluation of local muscular strain in the shoulder during constrained work[END_REF] for the 10, 50, and 90 th percentiles. The data from the three repetitions for each condition were averaged for each subject.2.4.3. Time-Varying Multi-Muscle Coactivation Function (TMCf)The simultaneous activation (coactivation) of agonist and antagonist muscles is one of the mechanisms adopted by the central nervous system to stabilise the joints or the spine[START_REF] Granata | Cost-Benefit of Muscle Cocontraction in Protecting Against Spinal Instability[END_REF]. We used the time-varying multimuscle coactivation function (TMCf)[START_REF] Ranavolo | A new muscle co-activation index for biomechanical load evaluation in work activities[END_REF] to perform, unlike what is possible with other methods, a time-varying monitoring of the co-activation of more than two muscles without the need for an a-priori classification of the muscles, depending on the generated moment. The TMCf has shown to be particularly useful because it is sensitive to the level of risk and correlated to shear and compression forces in manual lifting activities carried out by a single worker(Ranavolo et al., 2018), as well as in lifting activities shared by multiple workers[START_REF] Chini | Trunk muscle co-activation and activity in one-and two-person lifting[END_REF]. In fatiguing lifting activities[START_REF] Varrecchia | Trunk Muscle Coactivation in People with and without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities[END_REF] TMCf is correlated to the local manifestation of muscle fatigue. It was estimated using the following formula:

Fig. 3 .

 3 Fig. 3. Vertical velocity of the right (blue) and left (red) foot (a), vertical velocity of the left hand (b), raw sEMG signal for biceps brachii caput longum (c) and raw sEMG signal for triceps brachii caput longum (d) for the task without (A) and with (B) BAZAR. For both tasks, the dashed vertical lines indicate the sub-tasks, which are numbered on the figure and listed in the legend.

Fig. 4 .

 4 Fig. 4. Mean values (Mean ± SD) of muscles [% MVC] and TMCf [% of coactivation] in both conditions, without and with cobot. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Time-varying multi-muscle coactivation function. * statistical significance (p < 0.05).

Fig. 5 .

 5 Fig. 5. Max values (Mean ± SD) of muscle [% MVC] and TMCf [% of coactivation] in both conditions, without and with BAZAR. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Time-varying multi-muscle coactivation function. * statistical significance (p < 0.05).

Fig. 6 .

 6 Fig. 6. Mean values (Mean ± SD) of amplitude probability distribution function (APDF) with the 10, 50, and 90 th percentiles in both conditions, without and with cobot. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Time-varying multi-muscle coactivation function. * statistical significance (p < 0.05).

Figures: caption and alt textFigure 1

 1 Figures: caption and alt textFigure1Caption: Industrial use case scenario (A) and laboratory scenario (B).

Figure 1

 1 Figure 1 Alt Text: Industrial use case scenario proposed by the SME HANKAMP (Netherlands) is shown in the panel A while in the panel B the laboratory scenario is shown: it consisted of the BAZAR collaborative robot (only referred to as BAZAR in the following), 4 tables, 1 cylindrical load (5 kilograms) to be displaced and 1 cleaning brush.

Figure 2

 2 Figure 2 Caption: Sub-tasks of the task without (A) and with (B) BAZAR.

Figure 2

 2 Figure 2 Alt Text:In the panel A the sub-tasks of the task without BAZAR are shown: standing 1; walking 1; standing 2; lifting+carrying+placing 1; standing 3; lifting+carrying+placing 2; standing 4; lifting 1; brushing; placing +lifting 2; moving on table; standing 5; walking 2; standing 6. In the panel B the sub-tasks of the task with BAZAR are shown: standing 1; walking 1; standing 2; lifting+carrying+placing 1; lifting+carrying+placing 2; standing 4; brushing; placing+walking 2; standing 5.

Figure 3

 3 Figure 3 Caption: Vertical velocity of the right (blue) and left (red) foot (a), vertical velocity of the left hand (b), raw sEMG signal for biceps brachii caput longum (c) and raw sEMG signal for triceps brachii caput longum (d) for the task without (A) and with (B) BAZAR. For both tasks, the dashed vertical lines indicate the sub-tasks, which are numbered on the figure and listed in the legend.

Figure 4 Caption:

 4 Figure 4 Caption: Mean values (Mean ± SD) of muscles [% MVC] and TMCf [% of coactivation] in both conditions, without and with cobot. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Time-varying multi-muscle coactivation function. * statistical significance (p < 0.05).

Figure 4

 4 Figure 4 Alt Text: A bar plot of Mean values (Mean ± SD) of each acquired muscles [% MVC] and each evaluated Timevarying multi-muscle coactivation function [% of coactivation] in both conditions, without and with cobot are shown in the figure. TMCf: Time-varying multi-muscle coactivation function. The figure shows statistical significance reported in the text with an asterisk.

Figure 5 Caption:

 5 Figure 5 Caption: Max values (Mean ± SD) of muscles [% MVC] and TMCf [% of coactivation] in both conditions, without and with cobot. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Time-varying multi-muscle coactivation function. * statistical significance (p < 0.05).

Figure 5

 5 Figure 5 Alt Text: A bar plot of Max values (Mean ± SD) of each acquired muscles [% MVC] and each evaluated Timevarying multi-muscle coactivation function [% of coactivation] in both conditions, without and with cobot are shown in the figure. TMCf: Time-varying multi-muscle coactivation function. The figure shows statistical significance reported in the text with an asterisk.

Figure 6 Caption:

 6 Figure 6 Caption: Mean values (Mean ± SD) of amplitude probability distribution function (APDF) with the 10, 50, and 90th percentiles in both conditions, without and with cobot. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Time-varying multi-muscle coactivation function. * statistical significance (p < 0.05).

Figure 6

 6 Figure 6 Alt Text: Figure shows the Mean values (Mean ± SD) of amplitude probability distribution function (APDF) with the 10, 50, and 90 th percentiles in both conditions, without and with cobot. The APDF are shown for each acquired muscles [% MVC] and each evaluated Time-varying multi-muscle coactivation function.

  al., 2022; Anton et al., 2013; van der Molen et al., 2012; Faber et al., 2012; Kim et al., 2012; Barret and Dennis, 2005; Lee, 2004;

  al., 2000; Barbero et al., 2012; Vera-Garcia et al., 2010).In more detail, each participant performed the empty can test[START_REF] Schwartz | Normalizing shoulder EMG: An optimal set of maximum isometric voluntary contraction tests considering reproducibility[END_REF][START_REF] Boettcher | Standard maximum isometric voluntary contraction tests for normalizing shoulder muscle EMG[END_REF] for measuring the iMVC of the AD and PD.

Test Statistics df p value effect size power

		with	6.786±4.152
	TMCfAD-PD	without 12.206±10.656	z=-2.223	9.504 0.026	0.708	0.54
		with	5.664±4.816
	TMCfBBCL-TBCL without 6.878±3.913	t=4.486	10	0.001	1.404	0.987
		with	1.989±1.153
	TMCfFCR-ECL without 21.307±14.159	z=-2.934	9.504 0.003	1.385	0.979
		with	2.905±1.971
	TMCfESL-RAS without 5.382±2.302	z=-2.934	9.504 0.003	1.204	0.936
		with	2.980±1.229
	TMCfARM	without 7.510±3.331	t=6.415	10 <0.001	1.91	0.999
		with	1.845±0.975
	TMCfALL	without 5.477±2.39	t=6.594	10 <0.001	1.845	0.999
		with	1.580±0.776
	AD	without 23.324±26.935 with 16.038±16.487	z=-5.333	9.504 0.594	0.31	0.148
	PD	without 11.669±10.48 with 3.081±2.012	z=-2.667	9.504 0.008	0.892	0.736
	BBCL	without 14.594±10.931 with 3.241±3.261	z=-2.854	9.504 0.004	1.168	0.923
	TBCL	without 5.959±3.877 with 2.120±0.844	t=3.249	10	0.009	1.087	0.9
	FCR	without 18.159±21.82 with 3.957±4.109	z=-2.934	9.504 0.003	0.707	0.539
	ECR	without 53.376±24.896 with 4.150±2.022	t=6.818	10 <0.001	2.055	0.999
	RAS	without 3.556±2.183 with 2.781±1.357	z=-2.667	9.504 0.008	1.454	0.987
	ESL	without 13.171±5.52	z=-2.845	9.504 0.004	1.282	0.96

Table 2 .

 2 Statistical results of the effect of the cobot on each muscle and TMCf mean values. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Timevarying multi-muscle coactivation function. Bold: statistical significance (p<0.05).

			Mean±SD	Test Statistics df p value effect size power
	AD	without 49.541±48.629 with 33.996±26.499	z=-0.978	9.504 0.328	0.369	0.19
	PD	without 23.584±16.643 with 8.417±4.748	t=2.880	10	0.016	1.021	0.862
	BBCL	without 32.030±17.844 with 8.559±6.398	z=-2.845	9.504 0.004	0.991	0.841
	TBCL	without 11.618±7.211 with 5.420±3.246	t=2.338	10	0.041	0.991	0.841
	FCR	without 32.146±33.531 with 19.936±20.505	z=-1.956	9.504 0.051	0.417	0.23
	ECR	without 96.659±38.115 with 20.852±14.658	z=-2.934	9.504 0.003	2.276	0.999
	RAS	without 9.921±5.94 with 8.547±4.59	t=2.995	10	0.013	0.255	0.12
	ESL	without 23.863±9.57 with 16.852±6.136	z=-2.490	9.504 0.013	0.835	0.68
	TMCfAD-PD	without 21.482±15.484	z=-2.134	9.504 0.033	0.771	0.611
		with	11.037±5.775
	TMCfBBCL-TBCL without 13.011±6.666	t=3.732	10	0.004	1.419	0.988
		with	4.633±2.091
	TMCfFCR-ECL without 35.840±25.587	z=-2.934	9.504 0.003	1.019	0.841
		with	13.063±9.921
	TMCfESL-RAS without 10.643±5.222	t=4.016	10	0.002	0.669	0.518
		with	7.597±3.119

Table 3 .

 3 Statistical results of the effect of the cobot on each muscle and TMCf max values. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Timevarying multi-muscle coactivation function. Bold: statistical significance (p<0.05).

10 th 50 th 90 th

	AD	without 15.795±20.496 22.686±26.843 31.658±35.245
		with	2.546±1.757 17.210±16.611 37.141±29.583
	PD	without 8.523±8.509 12.41±11.375 17.274±14.478
		with	1.489±1.127	7.951±4.841 25.983±17.118
	BBCL	without 9.241±7.828 13.777±10.525 20.467±14.575
		with	1.733±1.020	11.434±8.51 33.436±18.595
	TBCL	without 4.125±2.548	5.720±3.699	7.993±5.494
		with	0.937±0.498	4.739±3.009 12.068±6.877
	FCR	without 13.365±17.217 18.673±23.908 26.713±34.577
		with	2.272±2.284	12.487±	28.522±35.095
	ECR	without 38.227±19.744 51.69±24.621 69.798±30.01
		with	4.263±1.93	31.39±15.827 81.047±37.503
	RAS	without 1.672±1.264	2.843±1.844	7.101±4.387
		with	1.156±0.767	2.618±1.435	6.835±3.755
	ESL	without 8.944±4.72	13.00±5.413 17.245±6.887
		with	2.480±1.211 11.390±4.967 22.714±9.924
	TMCfAD-PD	without 8.876±8.486 12.003±10.699 15.689±12.923
		with	1.512±0.922	8.68±5.724 21.125±12.371
	TMCfBBCL-TBCL without 4.908±2.893	6.636±3.863	9.026±5.055
		with	0.873±0.347	5.387±3.188 14.214±6.702
	TMCfFCR-ECL without 14.991±10.151 21.022±14.095 27.649±18.704
		with	1.930±1.071 13.089±8.966 26.229±14.275
	TMCfESL-RAS without 3.599±1.652	5.012±2.084	7.855±3.624
		with	1.789±0.676	4.514±1.934	8.433±3.559
	TMCfARM	without 5.978±2.774	7.336±3.349	9.161±3.839
		with	0.935±0.381	5.383±2.018 12.097±4.287
	TMCfALL	without 4.377±1.983	5.366±2.389	6.639±2.82
		with	0.825±0.349	4.111±1.447	8.775±3.042

Table 4 .

 4 Amplitude probability distribution function (APDF) at the 10 th , 50 th , and 90 th percentiles (Mean ± SD) of muscle [% MVC] and TMCf [% of coactivation] in both conditions, without and with BAZAR. AD: anterior deltoideus, PD: posterior deltoideus, BBCL: biceps brachii caput longum, TBCL: triceps brachii caput longum, FCR: flexor carpi radialis, ECR: extensor carpi radialis, RAS: rectus abdominis superior, ESL: erector spinae longissimus muscles. TMCf: Timevarying multi-muscle coactivation function. * statistical significance (p < 0.05).

  In future work, BAZAR could be controlled by using kinematic, kinetic and sEMG-based signals acquired from workers through an effective interaction between humans and robots, that ensures a correct information exchange between natural and artificial side. Indeed, only these suitable interfaces can monitor human behavior to properly plan the execution of the collaborative task and can allow mutual awareness of the human-robot dyad[START_REF] Ajoudani | Smart collaborative systems for enabling flexible and ergonomic work practices [industry activities[END_REF]. The research presented in this article was carried out as part of the SOPHIA project, which has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 871237.

al., 2020; Ajoudani et al., 2020; Kapelner et al., 2019, 2020; Lobov et al., 2018; Roche et al., 2014; Hahne et al., 2014; Gordon et al., 2013; Kiguchi et al., 2012).

Funding:

Acknowledgments:

We are extremely grateful to Freek Tonis and Pim Siahaya from HANKAMP for their help with the experimental procedure.

Disclosure statement:

The authors report there are no competing interests to declare.

Availability of data:

The data that support the findings of this study are openly available in https://gite.lirmm.fr/humar/researchprojects/sophia_dataset_example.