Intelligent Private Fitness System Based on ARM and Hybrid Internet of Things

Wang, Zihao; Yuan, Zhengnan; Xu, He; Eghan, Caleb

doi:10.1007/978-3-319-60753-5_10

Cited by 2 publications

(1 citation statement)

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“…In terms of recognition algorithms, classical machine learning models are currently the most commonly used. For example, Wang et al [20] used SVM and artificial neural nets as classifiers to achieve the recognition of six upper limb movements by fusing several MEMS posture modules. Martin and Gavey [21] used SVM and BP neural network to achieve the recognition of horizontal, vertical, diagonal, and closed lines drawn on the human arm.…”

Section: Related Workmentioning

confidence: 99%

Research on Intelligent Bodybuilding System Based on Machine Learning

Chen

2022

Journal of Sensors

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In recent years, people’s health is facing many challenges as their workload is increasing and their lives are becoming more and more stressful. In this context, healthy living has become a topic of concern and more and more people are choosing to promote their bodies through fitness. To address these existing problems in action recognition research, this paper designs and implements a machine learning-based intelligent fitness system to monitor three important parameters in physical activity: the type of action, the number of actions, and the period of action. Through the action recognition algorithm and the period calculation method, the three important parameters of action type, number of actions, and action period are calculated to generate a more comprehensive description of the limb actions. Experiments are conducted to show that the proposed deep neural network learns well on small datasets, achieving 97.61% action recognition accuracy and SVM achieving over 96% recognition accuracy.

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Section: Related Workmentioning

confidence: 99%