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 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 work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that real-time signals and related statistical data on the knee’s performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users.