A Wireless Surface Electromyogram Monitoring System Using Smartphone and Its Application to Maintain Biceps Muscle

Widasari, Edita Rosana; Miyauchi, Ryoichi; Tamura, H.; Tanno, Koichi

doi:10.1109/smc.2015.416

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Of note, in 2014, Richer [11] developed a real-time electromyogram (EMG) and electrocardiogram (ECG) that could calculate the real time heart rate and rhythm of cyclists on mobile Android devices. In 2016, Widasari [12] proposed a similar smartphone-based sEMG monitoring system capable of alerting users before muscle fatigue occurs. Most recently, in 2018, Yamaguchi [13] created a wearable sEMG monitoring device that recognizes bruxism via analog values obtained from the masseter sEMG signal then alerts users.…”

Section: Introductionmentioning

confidence: 99%

Muscle fatigue detection and treatment system driven by internet of things

et al. 2019

BMC Med Inform Decis Mak

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BackgroundInternet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many.MethodsTo facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network.ResultsWith the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state.DiscussionEnvironmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system.ConclusionsExperiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable.

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Section: Introductionmentioning

confidence: 99%