Physical fatigue detection through EMG wearables and subjective user reports

Papakostas, Michalis; Kanal, Varun; Abujelala, Maher; Tsiakas, Konstantinos; Makedon, Fillia

doi:10.1145/3316782.3322772

Cited by 17 publications

(8 citation statements)

References 10 publications

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“…In order to evaluate the performance of the fatigue detection method, two datasets are involved, namely, a public sEMG dataset and a MYO dataset collected in this paper. The public dataset (Papakostas et al, 2019 ) contains sEMG data of three motions: shoulder flexion (SF), shoulder abduction (SA) and elbow extension (EE). All the signals were extracted in one channel with a sample rate of 1,926 Hz.…”

Section: Experiments and Protocolsmentioning

confidence: 99%

A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation

Yang

Wang

et al. 2023

Front. Neurorobot.

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IntroductionHemiparesis is a common consequence of stroke that severely impacts the life quality of the patients. Active training is a key factor in achieving optimal neural recovery, but current systems for wrist rehabilitation present challenges in terms of portability, cost, and the potential for muscle fatigue during prolonged use.MethodsTo address these challenges, this paper proposes a low-cost, portable wrist rehabilitation system with a control strategy that combines surface electromyogram (sEMG) and electroencephalogram (EEG) signals to encourage patients to engage in consecutive, spontaneous rehabilitation sessions. In addition, a detection method for muscle fatigue based on the Boruta algorithm and a post-processing layer are proposed, allowing for the switch between sEMG and EEG modes when muscle fatigue occurs.ResultsThis method significantly improves accuracy of fatigue detection from 4.90 to 10.49% for four distinct wrist motions, while the Boruta algorithm selects the most essential features and stabilizes the effects of post-processing. The paper also presents an alternative control mode that employs EEG signals to maintain active control, achieving an accuracy of approximately 80% in detecting motion intention.DiscussionFor the occurrence of muscle fatigue during long term rehabilitation training, the proposed system presents a promising approach to addressing the limitations of existing wrist rehabilitation systems.

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Section: Experiments and Protocolsmentioning

confidence: 99%

A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation

Yang

Wang

et al. 2023

Front. Neurorobot.

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“…et al, 2020) Comment on current/potential applications: The exploration of potential adaptive robotic system for rehabilitation using muscle fatigue as a trigger has been tested for improve engagement and performance (Meyer-Rachner et al, 2017;Mugnosso et al, 2018;Huang et al, 2019;Kanal et al, 2019). Novel methods for fatigue detection are continuously being developed, boosted by machine learning algorithms and wearables EMG sensors (Mugnosso et al, 2017;Papakostas et al, 2019;Wang W. et al, 2020;Liu et al, 2021) 6.…”

Section: Muscle Fatiguementioning

confidence: 99%

Neuromechanical Biomarkers for Robotic Neurorehabilitation

et al. 2021

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One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

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“…For instance, several studies have used ML algorithms, such as decision trees (DT), k-nearest-neighbors (KNN), and support vector machines (SVM), with physiological data such as Heart Rate Variability (HRV), body temperature, and behavior tracking sensors such as accelerometers, to detect mental workload, exertion, and stress in firefighters [ 29 , 30 , 31 ]. Similarly, other studies have used ML algorithms with other populations to detect task difficulty, mental workload, fatigue, engagement, enjoyment, and user performance [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. ML algorithms have also been used successfully to classify fNIRS data; for example, they have been used with fNIRS with an average accuracy of 73.79% to recognize positive emotions of participants after watching emotional videos [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

et al. 2021

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The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

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Physical fatigue detection through EMG wearables and subjective user reports

Cited by 17 publications

References 10 publications

A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation

A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation

Neuromechanical Biomarkers for Robotic Neurorehabilitation

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

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