Improved and Secured Electromyography in the Internet of Health Things

Usman, Muhammad; Kamal, Mohsin; Tariq, Muhammad

doi:10.1109/jbhi.2021.3118810

Cited by 7 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Abid et al ( Minhas et al, 2022 ) collected the driver’s facial image and assessed the driver’s fatigue level to prevent the occurrence of traffic accidents. Muhammad et al ( Usman et al, 2022 ) used an IoT system to collect EMG and observe muscle contraction and fatigue analysis. It can be seen that the more signal information, the better the monitoring effect will be.…”

Section: Introductionmentioning

confidence: 99%

Exercise fatigue diagnosis method based on short-time Fourier transform and convolutional neural network

Zhu

Ji²,

Wang³

et al. 2022

Front. Physiol.

View full text Add to dashboard Cite

Reasonable exercise is beneficial to human health. However, it is difficult for ordinary athletes to judge whether they are already in a state of fatigue that is not suitable for exercise. In this case, it is easy to cause physical damage or even life-threatening. Therefore, to health sports, protecting the human body in sports not be injured by unreasonable sports, this study proposes an exercise fatigue diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN). The method analyzes and diagnoses the real-time electrocardiogram, and obtains whether the current exerciser has exercise fatigue according to the electrocardiogram. The algorithm first performs short-time Fourier transform on the electrocardiogram (ECG) signal to obtain the time spectrum of the signal, which is divided into training set and validation set. The training set is then fed into the convolutional neural network for learning, and the network parameters are adjusted. Finally, the trained convolutional neural network model is applied to the test set, and the recognition result of fatigue level is output. The validity and feasibility of the method are verified by the ECG experiment of exercise fatigue degree. The experimental recognition accuracy rate can reach 97.70%, which proves that the constructed sports fatigue diagnosis model has high diagnostic accuracy and is feasible for practical application.

show abstract

Section: Introductionmentioning

confidence: 99%