A device-free human detection and tracking system using a received signal strength indicator (RSSI) for an indoor environment is presented in this paper. The proposed system has two major functions: a wireless communication system and a human detection and tracking system. The first function is developed for measuring and collecting RSSI signals affected by human presence and movement, while the second function is developed for detecting and tracking the human using a predefined threshold and a zone selection method. The novelty of our proposed system is that the communication protocol can avoid signal interference and packet loss in the network, and the detection and tracking method can specify an actual zone that the human is present by taking an optimal predefined threshold and a level of RSSI variation in each zone into consideration. The proposed system is verified by experiments, and various human movement patterns with different directions and speeds are tested. The experimental results show that the proposed communication protocol can significantly provide communication reliability, and the proposed method can properly detect and track human movements. The packet delivery ratio indicating communication reliability is almost 100%. Detection and tracking accuracy measured by the number of times the method can detect and track the human with the correct zone is almost 100% in all cases of one man movements.

In the well-known and widely used multi-lateration method, an unknown target position is estimated using reference node positions and received signal strength indicator (RSSI) levels which indicate the distances between the target and the reference nodes. Since the RSSI signal in physical environments is time-varying and fluctuates overtime due to multi-path effects, RSSI signal variation can significantly cause localization errors. Inaccurate results can lead to poor decisions in the overall system. In this paper, an extended multi-lateration method is proposed to increase the localization accuracy. The novelty of the proposed method is that the boundary consideration, the zone selection, and the estimated position compensation method based on virtual positions are developed and integrated with the traditional multi-lateration method. To verify the proposed method, experiments using a ZigBee, 2.4 GHz, IEEE 802.15.4 wireless sensor network deployed in a laboratory room (the area size of 4 m ×4 m) and a corridor of the building (22 m ×9.3 m) have been tested. Experimental results demonstrate that all estimated positions of targets provided by the proposed method are within the test area (or zone), while the traditional multi-lateration method very often provides estimated positions outside the test area. The results also indicate that, by the proposed method, the estimation errors of most targets are lower than the case the multi-lateration method, and only a few targets, the errors by both methods are the same. Finally, in average, the proposed method significantly provides estimation errors lower than the traditional method: 0.682 m and 1.603 m, respectively (for the laboratory room), and 1.776 m and 4.353 m, respectively (for the corridor of the building). Here, the proposed method outperforms the traditional method by 57.430% and 59.194%, respectively.

In a device-free human detection and tracking system using a received signal strength indicator (RSSI), the change in the RSSI pattern is monitored and analyzed to detect and track the human movement. The variation in measured RSSI signals is one of the major effects leading to significant detection and tracking error. To handle such a research problem, in this paper, we propose adaptive RSSI filtering methods designed by considering both the detection and tracking accuracy and the computational complexity. The novelty of our proposed filtering methods is that, to reduce the computational complexity, the measured RSSI input values are automatically filtered only when they have high variation levels; an appropriate threshold is set and used for the decision. Additionally, to increase the detection and tracking accuracy, the measured RSSI input values with different variation levels are filtered with different filtering levels adaptively. The proposed filtering methods are verified by the experiments, which have been carried out in an indoor environment. Various human movement patterns with different directions and speeds are tested. The experimental results show that, with our test scenarios, the proposed filtering methods can appropriately reduce the RSSI variation. They provide good detection and tracking accuracy, which is measured by the number of times the system can detect and track the human with the correct zone. The computational complexity measured by the number of mathematical operations, used by the proposed methods, is lower than comparative filtering methods.

In this paper, a real-time knee extension monitoring and rehabilitation system for people, such as patients, the elderly, athletes, etc., is developed and tested. The proposed system has three major functions. The first function is two-channel surface electromyography (EMG) signal measurement and processing for the vastus lateralis (VL) and vastus medialis (VM) muscles using a developed EMG device set. The second function is the knee extension range of motion (ROM) measurement using an angle sensor device set (i.e., accelerometer sensor). Both functions are connected and parallelly processed by the NI-myRIO embedded device. Finally, the third function is the graphical user interface (GUI) using LabVIEW, where the knee rehabilitation program can be defined and flexibly set, as recommended by physical therapists and physicians. Experimental results obtained from six healthy subjects demonstrated that the proposed system can efficiently work with real-time response. It can support multiple rehabilitation users with data collection, where EMG signals with mean absolute value (MAV) and root mean square value (RMS) results and knee extension ROM data can be automatically measured and recorded based on the defined rehabilitation program. Furthermore, the proposed system is also employed in the hospital for validation and evaluation, where bio-feedback EMG and ROM data from six patients, including (a) knee osteoarthritis, (b) herniated disc, (c) knee ligament injury, (d) ischemic stroke, (e) hemorrhagic stroke, and (f) Parkinson are obtained. Such data are also collected for one month for tracking, evaluation, and treatment. With our proposed system, results indicate that the rehabilitation people can practice themselves and know their rehabilitation progress during the time of testing. The system can also evaluate (as a primary treatment) whether the therapy training is successful or not, while experts can simultaneously review the progress and set the optimal treatment program in response to the rehabilitation users. This technology can also be integrated as a part of the Internet of Things (IoT) and smart healthcare systems.