Statistical analysis of range of motion and surface electromyography data for a knee rehabilitation device

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

Implementation and evaluation of a 2.4 GHz multi-hop WSN: LoS, NLoS, different floors, and outdoor-to-indoor communications

In this paper, the communication reliability of a 2.4 GHz multi-hop wireless sensor network (WSN) in various test scenarios is evaluated through experiments. First, we implement an autonomous communication procedure for a multi-hop WSN on Tmote sky sensor nodes; 2.4 GHz, an IEEE 802.15.4 standard. Here, all nodes including a transmitter node (Tx), forwarder nodes (Fw), and a base station node (BS) can automatically work for transmitting and receiving data. The experiments have been tested in different scenarios including: i) in a room, ii) line-of-sight (LoS) communications on the 2nd floor of a building, iii) LoS and non-line-of-sight (NLoS) communications on the 1st floor to the 2nd floor, iv) LoS and NLoS communications from outdoor to the 1st and the 2nd floors of the building. The experimental results demonstrate that the communication reliability indicated by the packet delivery ratio (PDR) can vary from 99.89% in the case of i) to 14.40% in the case of iv), respectively. Here, the experiments reveal that multi-hop wireless commutations for outdoor to indoor with different floors and NLoS largely affect the PDR results, where the PDR more decreases from the best case (i.e., the case of a)) by 85.49%. Our research methodology and findings can be useful for users and researchers to carefully consider and deploy an efficient 2.4 GHz multi-hop WSN in their works, since different WSN applications require different communication reliability level

Reduction of RSSI variations for indoor position estimation in wireless sensor networks

In this paper, the reduction of RSSI (received signal strength indicator) variation for indoor position estimation in wireless sensor networks (WSNs) is studied through simulation. We demonstrate that using raw RSSI data (with high variation) to estimate a sensor position (i.e., an unknown position) is not appropriate due to a large estimation error. To cope with this problem, we propose a RSSI improvement method for reducing RSSI variation. The sum of the average RSSI value used at the previous step and the RSSI value measured at the current step are employed to determine the appropriate RSSI value (i.e., the smoothed RSSI value). The priority technique is also applied to such a function by assigning different weighted values. Simulation results show that using our proposed method with an optimal weighted value gives better estimation results than using raw RSSI data and a moving average method. With the proposed method, the position estimation by an original trilateration approach is more accurate

Double exponential smoothing and Holt-Winters methods with optimal initial values and weighting factors for forecasting lime, Thai chili and lemongrass prices in Thailand

In this paper, the Double Exponential Smoothing (DES), the Multiplicative Holt-Winters (MHW) and the Additive Holt-Winters (AHW) methods with optimal initial values and weighting factors are presented to forecast lime, Thai chili, and lemongrass prices in Thailand. Since these plants are important economic plants in Thailand, knowing their market prices or trends before selling them would be very useful for Thai agriculturists to appropriately plan their work and sales. In this paper, lime, Thai chili and lemongrass prices from January 2011 to September 2016 were gathered from the website’s database of Simummuang market used and as input data. This is one of the biggest markets in Thailand. Our study reveals that the DES method with optimal initial values and weighting factors provides the smallest forecasting error measured by the Mean Absolute Percentage Error (MAPE) when forecasting Thai chili and lemongrass prices, while the MHW and the AHW methods show better performance on forecasting lime price data which present seasonality patterns. The forecasted prices of lime, Thai chili and lemongrass during October 2016 to December 2016 are also provided

Vehicle Following Control via V2V SIMO Communications Using MBD Approach

Autonomous vehicle systems have been significantly increasing in design complexity, including precise control, reliable communications, and data security. This paper presents a Model-Based Design (MBD) framework on MATLAB/Simulink to integrate the vehicle model, Vehicle-to-Vehicle (V2V) communication model, and autonomous driving scenario model. A vehicle-following control model is demonstrated to maneuver a follower vehicle using locations and velocities of the leader vehicle sent via V2V. The vehicle model consists of Time to Collision (TTC), velocity decision control, path-following control, and vehicle dynamics. The follower vehicle decision is modeled by MathWorks Stateflow considering the important factors including velocities, positions, lanes, obstacles, and buildings that effect V2V communication efficiency. Simulink Design Verifier which is a formal verification tool was then used to verify the TTC, velocity decision, and path following control. The test coverage analysis and test harness were repeated to generate test patterns with 100% coverage results. The experiments were done under the following communications and environmental conditions: single-input-single-output (SISO) without buildings, SISO with buildings, and single-input-multiple-output (SIMO) with buildings. The resulting communication packet delivery ratios were 100%, 95.32%, and 99.91%, respectively. This reveals that the proposed method can effectively model the vehicle following control and autonomous driving scenario including the effects of V2V communications efficiencies

Implementation of a real-time automatic onset time detection for surface electromyography measurement systems using NI myRIO

For using surface electromyography (sEMG) in various applications, the process consists of three parts: an onset time detection for detecting the first point of movement signals, a feature extraction for extracting the signal attribution, and a feature classification for classifying the sEMG signals. The first and the most significant part that influences the accuracy of other parts is the onset time detection, particularly for automatic systems. In this paper, an automatic and simple algorithm for the real-time onset time detection is presented. There are two main processes in the proposed algorithm; a smoothing process for reducing the noise of the measured sEMG signals and an automatic threshold calculation process for determining the onset time. The results from the algorithm analysis demonstrate the performance of the proposed algorithm to detect the sEMG onset time in various smoothing-threshold equations. Our findings reveal that using a simple square integral (SSI) as the smoothing-threshold equation with the given sEMG signals gives the best performance for the onset time detection. Additionally, our proposed algorithm is also implemented on a real hardware platform, namely NI myRIO. Using the real-time simulated sEMG data, the experimental results guarantee that the proposed algorithm can properly detect the onset time in the real-time manner

Development of a Real-Time Knee Extension Monitoring and Rehabilitation System: Range of Motion and Surface EMG Measurement and Evaluation

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