Localization of mobile sensors is an important research issue in wireless sensor networks. Most indoor localization schemes focus on determining the exact position of these sensors. This paper presents a zoning-based tracking technique that works efficiently in indoor environments. The targeted area is composed of several zones, the objective being to determine the zone of the mobile sensor in a real time tracking process. The proposed method creates a belief functions framework that combines evidence using the sensors mobility and observations. To do this, a mobility model is proposed by using the previous state of the sensor and its assumed maximum speed. Also, an observation model is constructed based on fingerprints collected as Wi-Fi signals strengths received from surrounding access points. This model can be extended via hierarchical clustering and access point selection. Real experiments demonstrate the effectiveness of this approach and its competence compared with state-of-the-art methods.

Walking tests can provide an indicator of balance and fragility of people. They measure the gait speed and compare it to reference values related to various pathologies. We propose a radar sensor system that allows the evaluation of the gait speed in these tests in an automatic manner. The proposed approach consists of three phases; a first phase where the system automatically distinguishes the three sections of the test, namely walk #1, turn, and walk #2. Then, a second phase where the obtained signal is processed to evaluate the gait speed of the person. This is done through time-frequency analysis of the radar signal. In order to deal with the non-stationary nature of the radar signals, we consider the short-time Fourier transfer in our time-frequency analysis. The third phase consists in segmenting each walk into three segments, namely an acceleration zone, a measured-gait zone, and a deceleration zone. We provide a segmentation of the walking in order to automatically distinguish these zones. The proposed approach is validated using a Vicon motion-capture system, with a mean RMSE equal to 0.076 m/s. As compared to traditional techniques, our proposed system is automatic and does not require acceleration and deceleration zones.

Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

Fixed Motion Capture systems (Mocap) are well-established nowadays and are available in indoor and outdoor environments. Here, we introduce RobCap, a Moving Motion Capture System designed for acquiring accurate three-dimensional kinematics of human motion with large displacements. RobCap consists of an optoelectronic Mocap (Codamotion©) mounted on a robotic arm, to track the human motion in the local moving frame. This paper presents this device and expresses the local kinematics of the markers in a global coordinate system. We have acquired a set of 560 measurements, by controlling the Robotic arm to follow the subject’s movement and to track the markers attached to his wrist. The performance of the RobCap is evaluated through comparison with state-of-the-art technologies. The 3D error is found to be 9.7±4.1 mm and 3.9±0.8 mm, respectively without and with calibration. RobCap increases the nominal capture volume of the fixed Mocap from 75 m3 to 523 m3 and the operational volume from 105 m3 to 1436 m3. Our findings show that the RobCap is accurate and precise enough to be used as a mobile Mocap system for large displacements.

In this paper, we propose a novel zoning-based tracking technique that combines the sensors' mobility with a WiFi-based observation model in the belief functions framework to track the sensors in real time. The next possible destinations of the sensors are predicted, leading to a mobility model. The belief functions framework is used to propagate the previous step evidence till the current one. The mobility of the sensors, along with information from the network, are used to obtain an accurate estimation of their position. The contributions of this paper are two-fold. Firstly, it proposes new mobility models based on the transition between zones and hidden Markov models, to generate evidence on the zones of the sensors without the use of inertial measurement units. Secondly, it explores the fusion of evidence generated by the mobility models on one hand, and the observation model on the other hand. The efficiency of the proposed method is demonstrated through experiments conducted on real data in two experimental scenarios.