Influence of Muscle Fatigue on Electromyogram-Kinematic Correlation During Robot-Assisted Upper Limb Training

© The Author(s) 2020. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us. sagepub.com/en-us/nam/open-access-at-sage).Introduction: Studies on adaptive robot-assisted upper limb training interactions do not often consider the implications of muscle fatigue sufficiently. Methods: In order to explore this, we initially assessed muscle fatigue in 10 healthy subjects using electromyogram features (average power and median power frequency) during an assist-as-needed interaction with HapticMASTER robot. Spearman’s correlation study was conducted between EMG average power and kinematic force components. Since the robotic assistance resulted in a variable fatigue profile across participants, a completely tiring experiment, without a robot in the loop, was also designed to confirm the results. Results: A significant increase in average power and a decrease in median frequency were observed in the most active muscles. Average power in the frequency band of 0.8-2.5Hz and median frequency in the band of 20-450Hz are potential fatigue indicators. Also, comparing the correlation coefficients across trials indicated that correlation was reduced as the muscles were fatigued. Conclusions: Robotic assistance based on user’s performance has resulted in lesser muscle fatigue, which caused an increase in the EMG-force correlation. We now intend to utilize the electromyogram and kinematic features for the auto-adaptation of therapeutic human-robot interactions.Peer reviewedFinal Published versio

Monitoring muscle fatigue following continuous load changes

Department of Human Factors EngineeringPrevious studies related to monitoring muscle fatigue during dynamic motion have focused on detecting the accumulation of muscle fatigue. However, it is necessary to detect both accumulation and recovery of muscle fatigue in dynamic muscle contraction while muscle load changes continuously. This study aims to investigate the development and recovery of muscle fatigue in dynamic muscle contraction conditions following continuous load changes. Twenty healthy males conducted repetitive elbow flexion and extension using 2kg and 1kg dumbbell, by turns. They performed the two tasks of different intensity (2kg intensity task, 1kg intensity task) alternately until they felt they could no longer achieve the required movement range or until they experienced unacceptable biceps muscle discomfort. Meanwhile, using EMG signal of biceps brachii muscle, fatigue detections were performed from both dynamic measurements during each dynamic muscle contraction task and isometric measurements during isometric muscle contraction right before and after each task. In each of 2kg and 1kg intensity tasks, pre, post and change value of EMG amplitude (AEMG) and center frequency were computed respectively. They were compared to check the validity of the muscle fatigue monitoring method using Wavelet transform with EMG signal from dynamic measurements. As a result, a decrease of center frequency in 2kg intensity tasks and an increase of center frequency in 1kg intensity tasks were detected. It shows that development and recovery of muscle fatigue were detected in 2kg and 1kg intensity tasks, respectively. Also, the tendency of change value of center frequency from dynamic measurements were corresponded with that from isometric measurements. It suggests that monitoring muscle fatigue in dynamic muscle contraction conditions using wavelet transform was valid to detect the development and recovery of muscle fatigue continuously. The result also shows the possibility of monitoring muscle fatigue in real-time in industry and it could propose a guideline in designing a human-robot interaction system based on monitoring user's muscle fatigue.clos

Strength Training Prior to Endurance Exercise: Impact on the Neuromuscular System, Endurance Performance and Cardiorespiratory Responses

This study aimed to investigate the acute effects of two strength-training protocols on the neuromuscular and cardiorespiratory responses during endurance exercise. Thirteen young males (23.2 ± 1.6 years old) participated in this study. The hypertrophic strength-training protocol was composed of 6 sets of 8 squats at 75% of maximal dynamic strength. The plyometric strength-training protocol was composed of 6 sets of 8 jumps performed with the body weight as the workload. Endurance exercise was performed on a cycle ergometer at a power corresponding to the second ventilatory threshold until exhaustion. Before and after each protocol, a maximal voluntary contraction was performed, and the rate of force development and electromyographic parameters were assessed. After the hypertrophic strength-training and plyometric strength-training protocol, significant decreases were observed in the maximal voluntary contraction and rate of force development, whereas no changes were observed in the electromyographic parameters. Oxygen uptake and a heart rate during endurance exercise were not significantly different among the protocols. However, the time-to-exhaustion was significantly higher during endurance exercise alone than when performed after hypertrophic strength-training or plyometric strength-training (p <0.05). These results suggest that endurance performance may be impaired when preceded by strength-training, with no oxygen uptake or heart rate changes during the exercise

Effects of muscle atrophy on motor control

As a biological tissue, muscle adapts to the demands of usage. One traditional way of assessing the extent of this adaptation has been to examine the effects of an altered-activity protocol on the physiological properties of muscles. However, in order to accurately interpret the changes associated with an activity pattern, it is necessary to employ an appropriate control model. A substantial literature exists which reports altered-use effects by comparing experimental observations with those from animals raised in small laboratory cages. Some evidence suggests that small-cage-reared animals actually represent a model of reduced use. For example, laboratory animals subjected to limited physical activity have shown resistance to insulin-induced glucose uptake which can be altered by exercise training. This project concerned itself with the basic mechanisms underlying muscle atrophy. Specifically, the project addressed the issue of the appropriateness of rats raised in conventional-sized cages as experimental models to examine this phenomenon. The project hypothesis was that rats raised in small cages are inappropriate models for the study of muscle atrophy. The experimental protocol involved: 1) raising two populations of rats, one group in conventional (small)-sized cages and the other group in a much larger (133x) cage, from weanling age (21 days) through to young adulthood (125 days); 2) comparison of size- and force-related characteristics of selected test muscles in an acute terminal paradigm

Detection of intention level in response to task difficulty from EEG signals

We present an approach that enables detecting intention levels of subjects in response to task difficulty utilizing an electroencephalogram (EEG) based brain-computer interface (BCI). In particular, we use linear discriminant analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights. We observe that it is possible to classify tasks of varying difficulty based on EEG signals. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. Our experimental results suggest that it is possible to extract the intention level information from EEG signals in response to task difficulty and indicate some level of correlation between EEG and EMG. With a view towards detecting patients' intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies