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.

This paper presents a processing system of vehicle detection and counting for a camera on the city street using a heterogeneous ARM/FPGA processor and Xilinx SDSoC (Software-Defined System on Chip). An adaptive background method for reducing the impact of environment have been developed by analyzing luminance approximation changes over time and luminance approximation changes suddenly to improve background image of the object and to eliminate shadows so that the process of vehicles detecting and counting worked effectively.