Usability of Upper Limb Electromyogram Features as Muscle Fatigue Indicators for Better Adaptation of Human-Robot Interactions

Human-robot interaction (HRI) is the process of humans and robots working together to accomplish a goal with the objective of making the interaction beneficial to humans. Closed loop control and adaptability to individuals are some of the important acceptance criteria for human-robot interaction systems. While designing an HRI interaction scheme, it is important to understand the users of the system and evaluate the capabilities of humans and robots. An acceptable HRI solution is expected to be adaptable by detecting and responding to the changes in the environment and its users. Hence, an adaptive robotic interaction will require a better sensing of the human performance parameters. Human performance is influenced by the state of muscular and mental fatigue during active interactions. Researchers in the field of human-robot interaction have been trying to improve the adaptability of the environment according to the physical state of the human participants. Existing human-robot interactions and robot assisted trainings are designed without sufficiently considering the implications of fatigue to the users. Given this, identifying if better outcome can be achieved during a robot-assisted training by adapting to individual muscular status, i.e. with respect to fatigue, is a novel area of research. This has potential applications in scenarios such as rehabilitation robotics. Since robots have the potential to deliver a large number of repetitions, they can be used for training stroke patients to improve their muscular disabilities through repetitive training exercises. The objective of this research is to explore a solution for a longer and less fatiguing robot-assisted interaction, which can adapt based on the muscular state of participants using fatigue indicators derived from electromyogram (EMG) measurements. In the initial part of this research, fatigue indicators from upper limb muscles of healthy participants were identified by analysing the electromyogram signals from the muscles as well as the kinematic data collected by the robot. The tasks were defined to have point-to-point upper limb movements, which involved dynamic muscle contractions, while interacting with the HapticMaster robot. The study revealed quantitatively, which muscles were involved in the exercise and which muscles were more fatigued. The results also indicated the potential of EMG and kinematic parameters to be used as fatigue indicators. A correlation analysis between EMG features and kinematic parameters revealed that the correlation coefficient was impacted by muscle fatigue. As an extension of this study, the EMG collected at the beginning of the task was also used to predict the type of point-to-point movements using a supervised machine learning algorithm based on Support Vector Machines. The results showed that the movement intention could be detected with a reasonably good accuracy within the initial milliseconds of the task. The final part of the research implemented a fatigue-adaptive algorithm based on the identified EMG features. An experiment was conducted with thirty healthy participants to test the effectiveness of this adaptive algorithm. The participants interacted with the HapticMaster robot following a progressive muscle strength training protocol similar to a standard sports science protocol for muscle strengthening. The robotic assistance was altered according to the muscular state of participants, and, thus, offering varying difficulty levels based on the states of fatigue or relaxation, while performing the tasks. The results showed that the fatigue-based robotic adaptation has resulted in a prolonged training interaction, that involved many repetitions of the task. This study showed that using fatigue indicators, it is possible to alter the level of challenge, and thus, increase the interaction time. In summary, the research undertaken during this PhD has successfully enhanced the adaptability of human-robot interaction. Apart from its potential use for muscle strength training in healthy individuals, the work presented in this thesis is applicable in a wide-range of humanmachine interaction research such as rehabilitation robotics. This has a potential application in robot-assisted upper limb rehabilitation training of stroke patients

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

Adaptive Robot Mediated Upper Limb Training Using Electromyogram Based Muscle Fatigue Indicators

© 2020 Thacham Poyil et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Studies on improving the adaptability of upper limb rehabilitation training do not often consider the implications of muscle fatigue sufficiently. In this study, electromyogram features were used as fatigue indicators in a context of human-robot interaction, and were utilised for auto-adaptation of the task difficulty, which resulted in a prolonged training interaction.The electromyogram data was collected from three gross-muscles of the upper limb in 30 healthy participants.The experiment followed a protocol for increasing the muscle strength by progressive strength training, that was an implementation of a known method in sports science for muscle training, in a new domain of robotic adaptation in muscle training.The study also compared how the change in task difficulty levels was perceived by the participants, when the robot adjusted the difficulty, when the difficulty was manually adjusted, and also when there was no difficulty adjustment at all.Three experimental conditions were chosen, one benefiting from robotic adaptation (Intervention group) and the other two presenting control groups 1 and 2.The results indicated that the participants could perform a prolonged progressive strength training exercise with more repetitions with the help of a fatigue-based robotic adaptation, compared to the training interactions, which were based on manual/no adaptation.This study showed that using fatigue indicators, it is possible to alter the level of challenge, and thus, increase the interaction time.The results of the study are expected to be extended to stroke patients in the future by utilizing the potential for adapting the training difficulty according to the patient's muscular state, and also to have large number repetitions in a robot-assisted training environment.Peer reviewe

Study of Gross Muscle Fatigue During Human-Robot Interactions

Azeemsha Thacham Poyil, Farshid Amirabdollahian, and Volker Steuber, 'Study of Gross Muscle Fatigue During Human-Robot Interactions'. In Proceedings of the 10th International Conference on Advances in Computer-Human Interactions', Nice, France, 19 -23 March 2017, ISBN: 978-1-61208-538-8. Available online at:http://www.thinkmind.org/index.php?view=article&articleid=achi_2017_9_10_20028. Copyright © IARIA, 2017.This study explores the utility of Electromyogram (EMG) signals in the context of upper-limb exercises during human-robot interaction considering muscle fatigue of the participant. We hypothesise that the Electromyogram features from muscles and kinematic measurements from the robotic sensors can be used as indicators of fatigue and there is a potential to identify the muscle contribution during the activity where the Electromyogram data is correlated with the kinematic data. Electromyogram measurements were taken from four upper limb muscles of 10 healthy individuals. HapticMaster robot in active assisted mode together with a virtual environment was used to guide the participants for moving the robotic arm in a prescribed path in a horizontal plane consisting of four segments. The experiments were conducted until the participants reached a state of fatigue or until a defined maximum number of 6 trials were reached. Comparing the first and last trials indicated that the muscle fatigue had caused an increase in the average power and a decrease in the median frequency of EMG, which was more visible in Trapezius (TRP) and Anterior Deltoid (DLT) muscles in most of the analysed cases compared to Biceps Brachii (BB) and Triceps Brachii (TB) muscles. As the muscles came to a state of fatigue, the kinematic position also showed an increase in tracking error between the first and last trials. The ’near-thebody’ segment movements (S1 and S4 segments) were found to have less increase of tracking error compared to the ’away-frombody’ movements (S2 and S3 segments). A further analysis on this proved that the tracking error observed was mainly due to fatigue building up over the number of trials when performing ’away-from-body’ movements, and not a bi-product of perception errors. We identify that Deltoid and Trapezius muscles were fatigued more. These EMG fatigue indications can be mapped to kinematic indications of fatigue mainly in the segments S2 and S3, which required away from body movements because of the role of these two muscles in lifting the arm to the shoulder height in order to perform the activity. Our extracted features have shown the potential to identify the fatigued muscles as expected. The study also showed that the Electromyogram and kinematic features have a potential to be used to highlight the extent of muscle involvement