Arturo Guizar
email: arturo.guizar@insa-lyon.fr

Anis Ouni
email: anis.ouni@telecom-paristech.fr

Claire Goursaud
email: claire.goursaud@insa-lyon.fr

Nicolas Amiot
email: nicolas.amiot@univ-rennes1.fr

Jean-Marie Gorce
email: jean-marie.gorce@insa-lyon.fr

Impact of MAC Scheduling on Positioning Accuracy for Motion Capture with Ultra Wideband Body Area Networks

Keywords: C.2.1 [Network Architecture and Design]: Wireless communication, D.3.3 [Network Architecture and Design]: System de-sign issues, and performance modelling, H.1.2 [Models and Principles]: Miscellaneous Experimentation, Performance, Measurement Wireless Body Area Networks, Ultra WideBand, Localisation, Motion Capture, IEEE 802.15.6, Scheduling

Wireless Body Area Networks (WBANs) have recently emerged as a solution to enable effective location-aware applications. The rapid mobility of nodes is the most specific problem of this kind of networks and it is not well considered when designing MAC protocols. In this paper, we consider a WBAN using a typical TDMA-based Medium Access Control (MAC) protocol and an Impulse Radio Ultra Wideband (IR-UWB) physical layer defined by the standard IEEE802.15.6. We investigate the impact of mobility on the Motion Capture applications. The Root Mean Square Error (RMSE) of the estimated positions is analyzed according to different scheduling strategies at MAC layer under a real human mobility model. Our results show that an effective scheduling scheme leads to prioritize the own node position estimation. Finally, we propose to extend the study with a realistic human body channel.

INTRODUCTION

Wireless Body Area Networks (WBANs) are a core enabling technology that define "the Internet of you" which is the new brand of the Internet of things with the potential to change the way people go about their daily lives, specially in health care system. In this context, WBANs have raised the interest of researchers to find innovative solutions for e-Health applications [START_REF] Ullah | A review of IEEE 802.15.6 MAC, PHY, and security specifications[END_REF][START_REF] Hamalainen | On the WBAN radio channel modelling for medical applications[END_REF] such as emergency and rescue, disease detection or prevention, sport personal coaching, or personal multimedia. The latter can be achieved by estimating accurately the individual positions/postures with Motion Capture.

Individual Motion Capture systems can be classified in vision and non-vision techniques. The vision approach consist in a group of cameras capable to collect 3D location data of human motion i.e. Kinect, Vicon [START_REF]Vicon[END_REF]. However, vision systems can not be used in all kinds of environments, as they do need lighting and line of sight. On the contrary, WBANs can be used to monitor the human body movement, either with sensors (i.e. accelerometers, gyroscopes, ...) or distance estimation based algorithms. In this case, WBANs need to address several challenges such as energy efficiency, accurate precision, low complexity and reliable communication. For this purpose, Impulse Radio Ultra Wideband (IR-UWB) systems [START_REF] Xiao | A survey on impulse-radio UWB localization[END_REF] are a good solution to estimate precise range measurements using Time of Arrival (TOA) algorithm which is based on the transmission of packets : a request, and 1 or 2 responses. When considering a mobile target, the actual positions may be different for each packet. This may increase the error distance estimation, in particular, when the time between packets transmission increases. Thus, the scheduling is of primary importance. In order to give an effective location, the MAC layer must take into account the rapid mobility of WBAN nodes. In this paper, we tackle the impact of mobility on the position estimation accuracy and motion capture application. Different scheduling strategies are proposed and compared in order to give some engineering useful insights to design effective MAC protocols for location-aware applications.

The rest of the paper is organized as follows. Section 2 reviews the related work that focus on localization problems for motion capture. Then, in Section 3, we give the problem statement and the system model, and we explain our real mobility model for individual motion capture application. In Section 4, we present the proposed scheduling strategies to reduce the positioning error. Section 6 study the impact of mobility on location-aware applications. Finally, we conclude the paper in Section 7.

RELATED WORK

Extensive works of research on Individual Motion Capture have present the challenges to consider for localization IR-UWB systems, such as clock synchronization, NLOS environments (Non Line of Sight), interference mitigation and multipath [START_REF] Soganci | Accurate positioning in ultra-wideband systems[END_REF]. Nevertheless, they do not consider the MAC scheduling impact on ranging estimation to reduce the positioning error under mobility for WBAN scenarios. In [START_REF] Choliz | A framework for UWB-based communication and location tracking systems for wireless sensor networks[END_REF], they present the issues of ranging error, position update latency and calculation algorithms under mobility. They show the impact of MAC allocation resources on the capacity of the tracking system for Wireless Sensor Networks (WSN) scenarios.

Moreover, previous works focusing on MAC design for localization with UWB systems proposed protocol strategies based on beacon-enabled Time Division Multiple Access and evaluated the performance in terms of accuracy and latency. In [START_REF] Macagnano | MAC performances for localization and tracking in wireless sensor networks[END_REF], they proposed cooperative ranging with Aggregated and Broadcast schemes to reduce the delay of 3WR transactions. In [START_REF] Maman | An intuitive prioritised medium access scheme for tracking applications in UWB ldr-lt networks[END_REF], they focus on better resource management with priority levels for communication. In [START_REF] Garcia | On the trade-off between accuracy and delay in UWB navigation[END_REF], they focus on the relation between MAC delay and UWB accuracy related to the number of anchors and the communication range of nodes under mobility. However, all these works focuses on localization applications for WSN which do not present the same problems of WBAN and they do not consider the impact of the scheduling of localization packets on the positioning estimation. In [START_REF] Denis | On the scheduling of ranging and distributed positioning updates in cooperative IR-UWB networks[END_REF], they proposed scheduling schemes for cooperative distributed localization with two different policies (node neighbourhood and links quality) to reduce positioning convergence, latency and overhead. However, they consider a 2D Positioning for WSN which again is not realistic for WBAN location-aware applications.

Furthermore, the recent works in localization with WBAN have been focusing on the radio issues and localization algorithms performances without a rigorous scope on MAC strategies. In [START_REF] Hamie | On-body toa-based ranging error model for motion capture applications within wearable UWB networks[END_REF], the authors modelled the ranging error in terms of TOA estimation with real IR-UWB channel measurements in order to perform better localization algorithms. In [START_REF] Bharadwaj | Localization of wearable ultrawideband antennas for motion capture applications[END_REF], they realize a realistic measurement setup to achieve accurate positioning of WBAN nodes and comparing the results with a vicon system [START_REF]Vicon[END_REF].

ASSUMPTIONS AND SYSTEM MODEL 3.1 Network Topology

We consider a mesh IR-UWB WBAN under full connectivity containing NT nodes of two kinds as in [START_REF] Hamie | On-body toa-based ranging error model for motion capture applications within wearable UWB networks[END_REF], on-body mobile nodes that do not know their own position (i = 1...N ) and on-body anchor nodes that know their own position (j = 1...M ), NT = N + M . A set of anchor nodes de-fine a Local Coordinate System (LCS) to localize nodes under mobility. We define the instantaneous distance of the node i with the anchor j as dij(t) and the estimated distance as dij(t) which is calculated through TOA estimation. Moreover, the instantaneous position for a node is defined as Pi(t) and the estimated position is defined as function of estimated distances Pi(t) = f ( di1(t), di2(t), ..., diN (t)).

Finally, a set of NT positions P (t) = {P1(t), ..., PN T (t)} at time t define a human body posture at the LCS and the estimated posture is defined as the set of estimated positions P (t) = { P1(t), ..., PN T (t)}.

Localisation System Model

As presented in Section 1, we use the IR-UWB systems for the motion capture to estimate the positions and distances between the nodes in real time. The distance dij(t) between two nodes is deduced with the Three-Way Ranging (3WR) protocol by combining the typical timers obtained from 3 transmissions [START_REF] Choliz | A framework for UWB-based communication and location tracking systems for wireless sensor networks[END_REF], corresponding to one Request (Qij) send by a node i to an anchor j and two Response packets (Rij and Sij) from anchor j to node i, as shown in Figure 1. We define ∆t1 (resp. ∆t2) as the time difference between the reception of a request packet and the transmission of a response 1 packet (resp. the time difference between the transmission of the responses packets).

dij(t) = 1 2 c [((T4 -T1) -∆t1) -((T6 -T4) -∆t2)] ( 1
)
where c is the light speed.

The position Pi(t) of a node is estimated with the Time Difference of Arrival (TDOA) technique [START_REF] Xiao | A survey on impulse-radio UWB localization[END_REF]. For this purpose, each node should communicate with at least four anchors, which is the minimum needed for a tridimensional positioning.

PROPOSED SCHEDULING STRATEGIES

As explained in Section 3.1, it is necessary to estimate accurate distances for positioning the nodes of a WBAN in a LCS in order to enable the human motion capture. To this aim, we define a Medium Access Control (MAC) Layer based on the TDMA protocol and we assume that it is beacon enabled. Then, we reserve three transmission periods corresponding to the 3WR protocol. For the sake of our study, the scheduling of the 3WR periods is flexible to allow the comparison of different scheduling strategies (Section 4) to reduce the ranging error. In this study, we have to consider that the accuracy of the 3WR protocol depends on the delay taken to receive the response packets which varies when we have several nodes in the network, therefore, the ranging error estimated with the TOA may increase for some nodes.

To illustrate this assumption, we can take our previous scenario presented in [START_REF] Guizar | Impact of mobility on ranging estimation using ultrawideband[END_REF] where one node is sending a request for ranging estimation with an anchor, as shown in Figure 1. As the node is always moving, it is evident that the distance between the node and the anchor at time T 1 may be different to the distance at T 6, thus, there is a ranging error related to the time taken to receive the last response, considering the distance at T 1 as the one we want to estimate. From this study, we find that the time to send the first response ∆t1 has more impact on ranging estimation than ∆t2. Moreover, we find the same problem for the positioning, if we consider the case of one node sending 3WR packets to four anchors in order to find its position as in Figure 2, it is straightforward that the position of the node may not be the same between the moment it send the first request packet and at the moment it receive the response packets of the last anchor. This positioning error can be reduced by proposing the most appropriate scheduling scheme at the MAC layer. In this section, we define different scheduling strategies to minimize the ranging and positioning error.

Scheduling for accurate positioning

First, we define different strategies in order to reduce the positioning error a node i helped by j anchors in a WBAN. We consider that a node calculate its estimated position Pi(t) when he has estimated the distances dij(t) of at least four anchors. These schemes are shown in Figure 2 :

All request first (S1): The request packets Qij are sent in priority to all the anchors. Then each anchor answers at its turn with a Response 1 (R) and a Response 2 (S) consecutively. Ordered transaction (S2): The node starts a 3WR transaction with each anchor in order. Three period order (S3): The MAC Frame is divided into three periods dedicated to send the different 3WR packets in order: the Qij, the Rij and the Sij at the end. Priority for Response 1 (S4): The node send the Request and the anchor send a Response 1 packet immediately. Then, the anchors send the Sij at the end of the frame.

These four schemes have been selected because they all have their own advantage that can lead to a better accuracy. Indeed, S1 increases ∆t1 and ∆t2, therefore, the ranging error on the estimated distances is also increased but these will be more closed to the estimated position. S2 reduces ∆t1 and ∆t2 to give a more accurate distances, however they will be far from the last position. S3 reduces ∆t1 which is the most important for ranging estimation while increases ∆t2. S4 reduces highly ∆t1 and increases ∆t2. Thus, the goal of this study is to identify the best strategy.

Scheduling schemes for motion capture

In this second study, we focus on the motion capture. The goal is to locate precisely and simultaneously several mobile

SIMULATION FRAMEWORK 5.1 Simulation tools and parameters

In our study, we adopt a discrete-event simulation approach using the WSNet simulator [START_REF] Chelius | [END_REF] which allows to test different communication protocols under realistic situations, specially for the upper layers. Thus, we can reuse the data collected from the Mobility Model (Section 5.2) to study the impact of mobility on ranging estimation as in our previous study [START_REF] Guizar | Impact of mobility on ranging estimation using ultrawideband[END_REF]. This simulator is also suited for WBAN applications in terms of flexibility and simulation time, such as the motion capture. Moreover, it has a precision of time events in the nanoseconds order. Therefore, it is possible to study the performance of the 3WR protocol.

At the physical (PHY) Layer, we defined a protocol based on the IEEE802.15.6 PHY UWB [START_REF]IEEE standard for local and metropolitan area networks -part 15.6: Wireless body area networks[END_REF] in default mode (OOK modulation, data rate 0.4875 Mbps). For the sake of simplicity, we assume a Line of Sight (LOS) channel without packet loss in order to focus on the ranging error related to the mobility and the MAC scheduling. Therefore, we assume that our radio is capable to detect the first path of IR-UWB to detect the precise TOA at the receiver.

Mobility Model

We have considered in this study 2 kinds of mobility model.

The first and simplest one is a periodic linear movement (i.e. walking) at different speeds (1-20 m/s). The anchors stay fixed during the simulation and the node moves in a linear space of 30 cm. The human body movements have been captured on an area of 10 × 6 meters using an ultra high precision system based on a Vicon Mx40 motion capture technology [START_REF]Vicon[END_REF] at 100Hz. The system records the position of markers with the help of 16 cameras and is able to generate a C3D (Coordinates 3D) motion capture file containing the position of all the markers. Three different radio devices have been used along the scenarios of the measurement campaign: 1 IR-UWB "Be-Spoon Phone" [START_REF]Spoonphone[END_REF] and 2 tags providing distance observables, up to 10 IR-UWB "TCR" Devices from CEA LETI [START_REF] Pezzin | A low power, low data rate impulse radio ultra wide band transceiver[END_REF] providing Real time-Time Of Flight (RT-TOF) and up to 16 IEEE 802.15.4 "HikoB" Devices [START_REF]Hikob[END_REF], providing power measurements. In this study, only the Vicon data have been used.

This paper focuses on a specific LSIMC scenario where a subject is performing static postures, and the solely radio device position information is used in order to feed the WSNet simulator [START_REF] Chelius | [END_REF]. In this scenario, the subject were equipped The first step of this work was to extract the needed data, and generate the mobility model used in WSNet. To this aim, with the help of the open source PyLayers platform [START_REF] Amiot | Pylayers: An open source dynamic simulator for indoor propagation and localization[END_REF] and especially its associated body module [START_REF] Mhedhbi | Human body perturbed antenna integration in indoor propagation simulator[END_REF], it is possible to use the generated C3D file for visualizing the body movement, create an associated multi-cylinders body model and export the radio node positions. Hence, from the marker positions, a multi-cylinders body model is build to ensure the correct reconstruction of the body and of the radio nodes positions. Figure 6 compares a snapshot from a camera video shot and the associated multi-cylinders reconstruction, where the red dots represent the radio devices positions, and Figure 5 shows that feature by representing the different position of the body and of radio nodes as a function of time. At this point, it is directly possible to export a WSNet compliant file containing the timestamped radio nodes positions. Then, with a dedicated mobility model implemented in WSNet, it is possible to simulate the mobility of nodes and evaluate the performance of the upper layer protocols.

6. RESULTS

Impact on Positioning estimation

In this section, we present the positioning error related to the scheduling schemes as proposed in Section 4.1. First, in order to test our system model, we consider a controlled scenario composed of a WBAN with five sensors: one node (right wrist) and four anchors (chest, back, hip right, hip left) under a periodic linear movement (i.e. walking) at different speeds (1-20 m/s). We quantify the positioning error with the Root Mean Square Error (RMSE) -eq.2 by comparing the estimated position Pi(t) with the real position P f inal i (t) at the end of the frame:

RM SE = N f |P f inal i (t) -Pi(t)| N f (2)
N f is the number of frames during the simulation.

Then, we extend the study with the mobility traces from Section 5.2 by using the node on the right wrist (4) and four anchors on Right Front Chest (0), Left Front Chest (1), Left hip (2) and Back Chest (3) as shown in Figure 6. We use a speed factor (1-10) in order to accelerate the movement traces of the node.

Figure 7 and 8 show that RMSE increases with the speed for all the strategies. This is due to the fact that, as the speed increases, the distance covered during 3WR transmissions is higher, inducing more errors in the distance estimation.

Besides, for both Figures, strategies S1 and S2 give better positioning estimation than S3 and S4. In [START_REF] Guizar | Impact of mobility on ranging estimation using ultrawideband[END_REF], we proved that increasing ∆t1 has more impact on the ranging error while increasing ∆t2 has an impact on the reference distance we want to estimate, in our scenario we compare the estimated position with the real position at the end of the frame P f inal i (t). Thus, S1 and S2 are more efficient as they keep a reduced ∆t2 while in S3 and S4 it is increased. Moreover, we have completed this study by considering broadcast transmissions. In this case, the request is sent only once for all anchors, leading to a gain of Gain Brodcast = (M -1) * N , i.e. 3 slots in our case. We can observe in Figures 7 and8 that the broadcast on strategy S1 permits to reduce both error on positioning estimation and delay.

Performance of scheduling strategies for motion capture

In this second scenario, we evaluate the scheduling strategies proposed in Section 4.2 in terms of positioning error under the mobility model presented in Section 5.2. For this purpose, we keep the anchors at the same position, while the nodes to locate are on right wrist (4), left wrist (5), right ankle [START_REF] Choliz | A framework for UWB-based communication and location tracking systems for wireless sensor networks[END_REF] and right head [START_REF] Macagnano | MAC performances for localization and tracking in wireless sensor networks[END_REF], as shown in Figure 6. As in the last study, we use a speed factor to accelerate the movement of nodes. We use the RMSE to compare the position estimated Pi(t) with a reference position, thus, we define three Figure 9 shows that in the case of node 4 (right wrist), S7 is the best strategy to estimate P Note that when using S5, we have an averaged estimate of the positions between the start and the end of the frame. In the other side, S7 permits to estimate more accurate instantaneous positions. Therefore, nodes performing a position estimation at start of the frame (i.e. node 4) will have a better estimation of P f inal i with S5, while the nodes at the end of the frame (i.e. 7) will find better estimation of P f inal i with S7. We report the results on Table 1, the columns represent the position to estimate and the rows represent the best scheduling strategies to use depending on the scheduling of the node in the MAC frame.

Nodes scheduled at X of Frame P Ref

1 i P Ref 2 i P f inal i Start B-S7 B-S7 B-S5 Middle B-S7 B-S7 B-S7 End B-S5 B-S7 B-S7

CONCLUSION AND DISCUSSION

In this paper, we have addressed the problem of ranging error brought by the mobility of WBAN nodes to enable an Individual Motion Capture application with IR-UWB systems. First, we defined the architecture and workflow of the cross layer simulator, coupling "physical" blocks from Py-Layers for the mobility model with the WSNet network simulation tool to create a dedicated simulator for Body Area Networks scenarios. Then we proposed different scheduling strategies in order to reduce the positioning estimation. We find that the best strategy in terms of positioning precision is the case B-S7 when each node realize a complete Broadcast 3WR transaction with all anchors to calculate their positions one by one. Next step is to enlarge the study with channel models under Realistic short-term and long-term pedestrian mobility models.

Figure 1 :

 1 Figure 1: 2WR and 3WR protocols

Figure 2 :

 2 Figure 2: Scheduling strategies for 1 node and 4 anchors nodes to identify the posture of the person of interest. In this case, there is a tradeoff between having all distances corresponding to the same posture, and having these distances with high accuracy. To this aim, we propose the following strategies: All request first, anchor priority (S5): The nodes send the Qij packets at the beginning, then each anchor answer in order to all nodes with Rij and Sij. All request first, node priority (S6): As the previous, the nodes send Qij first then all anchors answer node by node with Rij and Sij. Ordered node positioning (S7): Each node repeat the scheme S1 one by one.

Figure 3 :

 3 Figure 3: Scheduling strategies for 2 nodes and 4 anchors

Figure 4 :

 4 Figure 4: Pictures of the subject from (a) front and (b) back, equipped with the 10 TCR radio nodes. Shining dots on the TCR devices are the markers allowing to determine the position of radio nodes using the Vicon system.

Figure 5 :Figure 6 :

 56 Figure 5: Decomposed body movement of the considered scenario along 3 different axes as a function of time. For this representation, the body position is sampled every 10 seconds along the complete 112 seconds scenario. Red dots correspond to the radio nodes positions.with 66 markers: 41 markers was used for the motion capture and 25 markers were used for positioning radio devices. The position of the 41 markers dedicated to the motion capture have been chosen in order to disambiguate the upcoming body reconstruction. The radio devices located on globally static body parts (chest, back or hip ) was equipped with a single marker to determine their positions. The other devices located on moving body parts (arms or legs) was equipped with 4 markers in order to determine their orientation in addition of their positions. The positions of those marker on the body can be observed in Figure4.

Figure 7 :Figure 8 :

 78 Figure 7: Impact of Scheduling under linear mobility

Figure 9 :Figure 10 :Figure 11 :Figure 12 :

 9101112 Figure 9: (a) Position error of node 4 respect to P Ref 1 i . (b) Position error of node 4 respect to P Ref 2 i . (c) Position error of node 4 respect to P f inal i .

  Ref 1 i and P Ref 2 i while S5 is better to estimate P f inal i . Figure10shows that in the case of node 5 (left wrist), S7 is a good choice to estimate P Ref 2 i , but without remarkable difference (1cm) for estimating P Ref 1 i and P f inal i in respect to S5 and S6. Moreover, for nodes 4 and 5, the estimation of P f inal i has a more important error than the estimation of P Ref 1 i and P Ref 2 i . Figure11shows that in the case of node 6 (right ankle), S7 is the best strategy to estimate P Ref 2 i and P f inal i . However in the case of the estimation of P Ref 1 i there is a small difference between S5, S6 and S7 (2mm). Figure12shows that in the case of node 7 (right head), S7 is the best strategy to estimate P Ref 2 i and P f inal i , while S5 is better to estimate P Ref 1 i . Moreover, when applying a Broadcast transmission for the request packets for the best strategies of each scenario, Figures show an improvement (1-2cm) for all cases, but in this case we find a gain of 12 slots.

Table 1 :

 1 Best Scheduling Strategy for Motion CaptureBased on these results, we find that in the case of an application looking for individual motion capture of the nodes (estimation of P Ref 2 i ) with high refreshment rate, the best scheduling is a Broadcast transmission with S7. If we enlarge the scope to applications looking for posture recognition (estimation of positions at the end (resp. start) of frame P f inal i (resp. P Ref 1 i ), it is possible with a Broadcast transmission with S5.

ACKNOWLEDGEMENTS

This work has been carried out in the frame of the COR-MORAN project, which is funded in part by the French National Research Agency (ANR) under the contract number ANR-11-INFR-010.


B Denis

N Amiot

B Uguen

A Guizar

C Goursaud

A Ouni $

C Chaudet

Qualitative Analysis of RSSI Behavior in Cooperative Wireless Body Area Networks for Mobility Detection and Navigation Applications

Keywords: Body Shadowing, Cooperative Body Area Networks, Motion Capture, Navigation, Mobility Detection, Ranging, Received Signal Strength Indicator, Zigbee

In this paper, we account for radio-location experiments aiming at both indoor navigation and mobility detection applications for Wireless Body Area Networks (WBAN). This measurement campaign involved IEEE 802.15.4-compliant integrated radio devices organized within a full mesh topology over on-body and off-body links. The latter devices produce peerto-peer Received Signal Strength Indicators (RSSI) that could feed ranging, positioning or tracking algorithms. An in-depth behavioral analysis of the collected time-stamped radio-location metrics is thus proposed with respect to the captured human mobility (including body shadowing). Based on our observations and interpretations, practical insights are finally drawn in terms of system and algorithms design.

I. INTRODUCTION

Benefiting from recent advances in electronics and shortrange ultra-low power radio design, Wireless Body Area Networks (WBANs) have emerged as a new technology. It covers the needs of wide practical and innovative applications in security, healthcare, sports and entertainment. Recently, significant research interests have been devoted to locationaware functionalities, i.e. individual posture detection or indoor navigation. Most of the studies referenced in this field focus on WBAN measurements based on costly pieces of equipment, either for channel modeling purposes [START_REF] Cotton | A review of radio channel models for body centric communications[END_REF], [START_REF] Rosini | Off-body Channel Modelling at 2.45 GHz for Two Different Antennas[END_REF] or for the validation of on-body localization algorithms based on impulse radio [START_REF] Mekonnen | Constrained Maximum Likelihood Positioning for UWB based Human Motion Tracking[END_REF], [START_REF] Shaban | Toward a Highly Accurate Ambulatory System for Clinical Gait Analysis via UWB Radios[END_REF]. Other approaches aim at characterizing mesh connectivity with integrated narrow-band devices from a pure communication-oriented perspective [START_REF] Lauzier | Full Mesh Channel Measurements on Body Area Networks under Walking Scenarios[END_REF]. Finally some works simply rely on simulations [START_REF] Hamie | Joint Motion Capture and Navigation in Heterogeneous Body Area Networks with Distance Estimation over Neighborhood Graph[END_REF]. But only rare contributions have considered using real devices to assess the actual performance of on-body nodes localization in large scale, while pointing out critical physical effects related to body mobility [START_REF] Hamie | On-Body Localization Experiments using Real IR-UWB Devices[END_REF].

This paper accounts for an unprecedented and extensive localization-oriented measurement campaign based on integrated devices in a full mesh multi-standard WBAN context mixing both on-body and off-body links (i.e. at the body scale or with respect to fixed elements of infrastructure, respectively). During this campaign, a side infrared motion capture system was exploited for accurate space/time referencing [START_REF] Bideau | Using Virtual Reality to Analyze Sports Performance[END_REF]. Although both Impulse Radio -Ultra Wideband (IR-UWB) [START_REF] Pezzin | A Low Power, Low Data Rate Impulse Radio Ultra Wide Band Transceiver[END_REF], [START_REF]BeSpoon SpoonPhone and tags[END_REF] and 2.45GHz IEEE 802.15.4-compliant [START_REF]HiKoB FOX sensor[END_REF] technologies have been considered under common deployment configurations, the paper focuses on the early exploitation of narrow-band data, as a preliminary step. The behavior of timestamped Received Signal Strength Indicators (RSSI) is thus illustrated and analyzed. In particular, we provide interpretations related to body shadowing under biomechanical constraints, conditionally informative (distance-dependent) path loss and relative body orientation with respect to fixed elements of infrastructure. These observations may have strong implications in terms of WBAN design and localization strategies for human mobility classification, detection or even tracking.

In Section II, we present the measurement set-up and scenarios. Then Section III provides illustrating examples of RSSI evaluations over representative links. In Section IV, we disclose insights for the design of location-enabled cooperative WBAN systems in terms of mobility and posture detection.

II. MEASUREMENT SET-UP AND SCENARIOS

A. Equipment a) IEEE 802.15.4-compliant radio devices: The involved HiKoB FOX wireless sensors [START_REF]HiKoB FOX sensor[END_REF] embed a processor and a radio chipset whose physical layer is IEEE 802.15.4-compliant in the 2.45GHz ISM band. The FOX radio chipset gives access to the average RSSI obtained over the last 8 received modulation symbols. Similarly to [START_REF] Lauzier | Full Mesh Channel Measurements on Body Area Networks under Walking Scenarios[END_REF], a Time Division Multiple Access (TDMA) is considered, each node transmitting periodically in a pre-determined timeslot. An additional longer timeslot is dedicated for logging onto the µ-SD card all the information locally collected by the nodes during each frame. The corresponding protocol adapts the frame duration depending on the number of nodes (e.g. 26ms for 16 nodes). During this new measurement campaign, up to 12 nodes have been deployed on the subject bodies, whereas 4 more nodes were placed in the immediate environment, serving as fixed anchors of infrastructure, AP i, i =1...4, at 1.06m of height (Fig. 1 and2).

b) Reference motion capture system: Besides radio devices, the Oxford Metrics Group's Vicon motion capture system, traditionally used to analyze elite athletes' movements [START_REF] Bideau | Using Virtual Reality to Analyze Sports Performance[END_REF], was involved in our measurement campaign. In particular, it was used to deliver high-precision spatial referencing (i.e. seen as ground truth), accurate synchronization and mobility modeling for the deployed on-body nodes. The Vicon capture was performed at 100 Hz using 12 infrared cameras surrounding the scene. In each experiment, we equipped the test subject with several tens of markers placed on the on-body devices and on anatomical landmarks to precisely reconstruct each body parts 3D position and orientation. The acquired information is used in the following to compute the exact distances separating the radio nodes and determine radio obstructions at any time.

B. Experimental Scenario

The experiments were realized in a gymnasium dedicated to motion capture studies at ENS/IRISA Cachan Bretagne, France. The capture zone was restricted to a 13mx8m area, including the 4 anchors and surrounded by the Vicon infrastructure (See Fig. 1 and2). The considered canonical scenario deliberately mixed moderate pedestrian walking and static poses (for mobility and attitude detection), each trial lasting for about 110 sec. The subject was holding a smartphone in his right hand to mimic the use of a navigation display device. At the beginning of the acquisition, after synchronizing motion capture and radio sub-systems through the crossdetection of a specific arm gesture, the subject body was standing right in the middle of the scene, facing each anchor for approximately 5 sec each and rotating over the 4 anchors sequentially. Then at time t = 20 sec, the subject started moving along a rectangular trajectory centered on the starting point. The sequence is then divided into 9 moving subsequences, where the subject walks from one point of interest to another, with 9 intercalated static sequences. For the sake of illustration, Fig. 2 reports time-stamped snapshots of the body posture and orientation at different instants (corresponding to the 9 sequences) spanning from t = 0 sec till t = 90 sec.

III. ILLUSTRATION AND ANALYSIS OF RSSI BEHAVIOR

In this section, we qualitatively interpret the evolution of RSSI over on-body and off-body links separately, based on the high-definition body motion capture and scenario time-line.

A. On-Body RSSI Behavior

This study consists in analyzing the behavior of on-body links related to the nodes under mobility. Time-dependent RSSI measurements are evaluated over different links associated with the highly mobile node placed on the Right Wrist (RW). The links exhibiting similar behaviors over time have been grouped into 4 subsets: Subset 2 gathering the links between WR and the right upper part of the body, mostly in radio visibility (i.e. HeadRight, T orsoRight, ElbowRight, HipRight); Subset 3 gathering the links between RW and the left upper part of the body (plus the back), most often and strongly shadowed (i.e. T orsoT opLef t, BackCenter, ElbowLef t) and Subset 4 gathering links between RW and other fast moving terminations (i.e. W ristLef t, KneeLef t, AnkleLef t, AnkleRight). One more Subset 5, gathering the links between Left Wrist (LW) and nodes placed at HeadRight, T orsoT opLef t, BackCenter and ElbowLef t, is added for comparison with RW. Our Subsets labelling arbitrarily respects that of the acquisition for processing convenience (unshown Subset 1 stands for the links wrt. anchors). First of all, the 9 sequences described in Section II-B are clearly noticeable on all the curves of Fig. 3, even if they are more visible in Subsets 3 and 4. Periods of fast signal variations (resp. slow signal variations) represent the portions of time when the subject is walking (resp. static). Moreover, the results of Subset 2 show a quasi-constant RSSI value with low dispersion and slow variations compared to the results of Subset 5 for the left wrist. In fact, the right hand holding a smartphone is time-invariant and in Line of Sight (LoS) with respect to the body torso and the head, whereas the left hand is mobile and in Non Line of Sight (NLoS). Other locally periodic effects over walking portions of time, caused by cyclic movements and fast accelerations of other peripheral nodes over repeated gaits, are more directly revealed by Subset 4 results, as expected. Subset 3 clearly illustrates the effect of body shadowing, leading to higher RSSI versatility and dispersion, but also stronger average attenuations. Finally, we also observe in Subset 3 a much higher packet loss rate (i.e. the received power being below the Rx RSSI sensitivity of -90dBm). In this case on-and/or off-body cooperation would be beneficial to communication QoS.

B. Off-Body RSSI Behavior

This study focuses on the channel characteristics of offbody links. We are particularly interested by the links between access points and the T orsoT opLef t (TTL) and BackCenter (BC) nodes, since the latter experience low relative on-body mobility while the subject is walking and they offer orthogonal obstruction conditions. The RSSI evolution over these links and their corresponding inverse squared distances in log-scale are shown on Fig. 4 for the 40 first sec of the acquisition. For the two links with respect to AP 1, the body obstruction status is superposed on the evolution of the ground truth quantity for illustration purposes. This status has been retrieved based on motion capture acquisition after simplifying the body as a set of 11 cylinders, thus determining intersections of the LoS path by one of these cylinders at any time. During the first 20 sec of the sequence, the subject is facing sequentially the anchors {AP i} i=1...4 . Accordingly, the distances with respect to AP 1 and AP 3 (resp. to AP 2 and AP 4) are constant and approximately equal. The distances between the subject and AP 2 and AP 4 being larger than for AP 1 and AP 3 according to the scene configuration, the log inverse of their squared distances naturally appears to be smaller. The observed RSSI variations (and relative mistmatch to the model) are thus not due to the variation of distance but essentially to the body shadowing status. For instance, when the subject is facing AP 1, a deep body shadowing is produced over the link AP 2 -T T L, leading to the low observed level of -70 dBm. For the second portion of time, when the subject is now facing AP 2 and the link status switches rapidly to LoS conditions, the RSSI level increases suddenly up to -60 dB. For the third static posture, the subject is facing AP 3 but the device placed on the left part of torso remains in optical visibility from AP 2, and the RSSI level drops down to -66 dB. The fourth static posture corresponding to the subject facing AP 4, AP 2 is strongly obstructed and RSSI is dropping at an even lower level of -80dBm. When the subject is walking in visibility in the direction of AP 1, the RSSI increases rapidly in accordance with the inverse square of the actual distance. Overall, the full pattern of the 4 observed RSSI values with respect to the anchors (especially the differential behaviours observed at T T L and BC on-body nodes) seems to provide relevant indications on both the anchors proximity and the relative body orientation. This result is valid not only during the stance phases but under mobility as well.

IV. IMPLICATIONS FOR RSSI-BASED MOBILITY DETECTION AND NAVIGATION IN COOPERATIVE WBANS

A. Blind Classification and Detection of Mobility Patterns

As seen in Fig. 3, the RSSI evolution jointly observed over different groups of on-body links is clearly an indicator of the mobility status. Accordingly, it could be beneficial to human activity and body posture detection. For instance, patterns of empirical statistics computed on-line over judiciously chosen Subsets of on-body links (e.g. calculating the sample mean, standard deviation and kurtosis) of the RSSI (or even the instantaneous packet loss rate) measured over time could be used for preliminary learning and subsequent decision (e.g. based on machine learning or Bayesian tools) within a finite alphabet of possible postures (e.g. static vs. walking, right arm ahead or naturally balancing, etc.). As an example, Fig. 5 represents uniquely the RSSI standard deviation for the same links Subsets as that of Fig. 3 and for different mobility regimes. We notice that high (resp. low) relative deviations generally account for mobile (resp. static) sequences, whatever the link Subset, but even more remarquably for the links involving peripheral nodes like in Subsets 2 and 4. Accordingly, implementing joint Bayesian decision rules relying on several combinations of links and/or Subsets seem feasible to reinforce mobility detection rates, while preventing from false alarms. One could also make easily the distinction between different arm postures, based on the absolute deviation levels observed for Subsets 2 (i.e. low in average and sensitive to mobility) and 5 (higher in average and almost insensitive to mobility), i.e. relying on links that involve the two wrists (except the links to the elbows, which obviously exhibit the same low deviations regardless of the posture).

B. Improved Personal Indoor Navigation

Under the conventional log-normal shadowing modeling assumption, the best achievable RSSI-based ranging precision is proportional to a ratio binding the shadowing standard deviation and the path loss exponent (e.g, [START_REF] Qi | On Relation among Time Delay and Signal Strength based Geolocation Methods[END_REF]). Intuitively, a high ratio implies that the deterministic power loss as a function of the distance is no longer significant nor dominating in comparison with the shadowing dispersion, thus making the interpretation of any RSSI readings hazardous from a ranging perspective. As discussed in [START_REF] Hamie | Contributions to Cooperative Localization Techniques within Mobile Wireless Body Area Networks[END_REF], path loss exponents are very low over off-body links in frank NLoS cases (typically <1), whereas the measured power dispersion is adversely large (by several dBm). But body obstructions cannot be classified so easily into binary cases (i.e. visibility or not) over these offbody links and there is a continuum of possible shadowing configurations, as a function of the relative subject orientation, depending whether the body partially or totally obstructs the propagation of direct radio waves (e.g. leading to power fluctuations of 25 dB over a body rotation of 360 o [START_REF] Rosini | Off-body Channel Modelling at 2.45 GHz for Two Different Antennas[END_REF]).

Thus, instead of considering the conventional representation, conditional distance-dependent path loss models could be derived (i.e. conditioned on prior NLoS detection), whose parameters depend on the body shadowing status (e.g. including a systematic and constant additional loss) and even possibly, on the body relative orientation with respect to the LoS path (i.e. with a deterministic and monotonic angular-dependent additional loss). Based on our experiments, the extraction of such model parameters would be possible, based on the motion capture information and body-cylinder approximation at any time (though not shown here for paper length constraints). In a future study, the residuals obtained after LS model fitting to experimental RSSI data will be compared for the different modeling assumptions mentioned above in order to highlight the impact of prior NLoS detection on off-body ranging quality and thus, on navigation. Finally, Fig. 4 indicates that the judicious placement of two on-body nodes only, respectively on the torso and back, is definitely relevant into navigation scenarios, leading to orthogonal obstruction conditions and spatial diversity with respect to the infrastructure under mobility.

V. CONCLUSION

This paper experimentally analyzed the RSSI behavior in full mesh WBANs under a mixing on-body and off-body cooperative scenario. Our results showed qualitative correlations between the RSSI evolution and the ground truth inter-node distances, but also the difficulty of unconditionally exploiting such power measurements as explicit (i.e. in absolute dBm scale) range-dependent radio metrics for further higher level localization algorithms in lack of additional information about the body shadowing status. Further studies shall investigate contextual body shadowing mitigation for RSSI-based ranging and navigation over off-body links, as well as mobility/posture detection algorithms based on on-body links. A rather similar analysis shall be also conducted on IR-UWB temporal radiolocation metrics such as Round Trip -Time of Flight (RT-ToF), still based on the same measurement campaign.

Fig. 1 .

 1 Fig. 1. Front and back pictures of the equipped subject, including 12 on-body HikoB nodes and tens of Vicon markers.

Fig. 2 .

 2 Fig. 2. Partial view of deployment scenario, including 4 fixed anchors (red spheres), 12 infrared cameras (big blue spheres), the followed trajectory (black ground path), and time-stamped snapshots of the body poses (articulated chains of light gray cylinders) with on-body radio nodes (green spheres).

Fig. 3 .

 3 Fig. 3. RSSI evolution during the 9 sequences (white regions for static positions and gray regions for walking) over different classes of links: (Subset 2) RW -nodes placed on the right upper part of the body; (Subset 3) RWnodes placed on the left upper part of the body; (Subset 4) RW -alternative peripheral nodes; (Subset 5) LW -mixed nodes.

Fig. 4 .

 4 Fig. 4. Comparison between measured RSSI over T T L -AP i / BC -AP i links and their corresponding distance-dependent path loss, where NLoS conditions between T T L/BC and AP 1 are represented by crosshatch (Zoom on the first 40 sec only).

Fig. 5 .

 5 Fig. 5. RSSI standard deviation for distinct links and mobility sequences.

ACKNOWLEDGMENT

This work has been carried out in the frame of the COR-MORAN project, which is funded by the French National Research Agency (ANR) under the contract number ANR-11-INFR-010. We thank all the involved researchers from ENS/IRISA and M2S Lab for their precious support during the measurement campaign.