Abstract
With the increasing widespread and popularity of internet connected smartphones, more and more people are becoming addicted to their mobile phones which has caused many health problems. Previous studies have proved that surface electromyographic (sEMG) signal can be used to monitor muscle fatigue in different situation such as driving environment or detect some cervical diseases such as muscle chronic pain. It inspired us an objective way to detect the fatigue status of phone users during a prolonged use of mobile phone. In this paper, an experiment was organized to collect phone users’ sEMG data and four classifiers were used with multiple sets of features for fatigue detection. Results show that the sEMG signal is an effective measure for detecting users’ neck fatigue, while the best classifier that achieved the highest accuracy compared to the other tested classifiers is the support vector machine (SVM).
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Nie, L., Ye, X., Yang, S., Ning, H. (2019). sEMG-Based Fatigue Detection for Mobile Phone Users. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_39
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DOI: https://doi.org/10.1007/978-981-15-1925-3_39
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