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

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

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

Inter-rater and intra-rater reliability of isotonic exercise monitoring device for measuring active knee extension

Background The goal of this study was to assess the reliability of electromyography and range of motion measurements obtained using a knee exercise monitoring system. This device was developed to collect data on knee exercise activities. Methods Twenty healthy individuals performed isotonic quadriceps exercises in this study. The vastus medialis surface electromyography (sEMG) and range of motion (ROM) of the knee were recorded during the exercise using the isotonic knee exercise monitoring device, the Mobi6-6b, and a video camera system. Each subject underwent a second measuring session at least 24 h after the first session. To determine reliability, the intraclass correlation coefficients (ICCs) and standard error of measurement (SEM) at the 95% confidence interval were calculated, and a Bland–Altman analysis was performed. Results For inter-rater reliability, the ICCs of the mean absolute value (MAV) and root mean square (RMS) of sEMG were 0.73 (0.49, 0.86) and 0.79 (0.61, 0.89), respectively. ROM had an ICC of 0.93 (0.02, 0.98). The intra-rater reliability of the MAV of the sEMG was 0.89 (0.71, 0.96) and the intra-rater reliability of RMS of the sEMG was 0.88 (0.70, 0.95). The ROM between days had an intra-rater reliability of 0.82 (0.54, 0.93). The Bland–Altman analysis demonstrated no systematic bias in the MAV and RMS of sEMG, but revealed a small, systematic bias in ROM (−0.8311 degrees). Conclusion For sEMG and range of motion measures, the isotonic knee exercise monitoring equipment revealed moderate to excellent inter- and intra-rater agreement. However, the confidence interval of ROM inter-rater reliability was quite large, indicating a small agreement bias; hence, the isotonic knee exercise monitor may not be suitable for measuring ROM. This isotonic knee exercise monitor could detect and collect information on a patient’s exercise activity for the benefit of healthcare providers

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