Utilization of Wearable Sensors Based on Artificial Intelligence in Motion Monitoring

Conventional sports monitoring methods are often hampered by inconvenient data collection and by accuracy issues. This paper explores the potential incorporation of artificial intelligence technology with wearable sensors to identify a low-cost, interpretable method of human motion monitoring. A single accelerometer is used to highlight applications of the rest of the method, which derives the vertical component and uses the pre-peak threshold, the pre-trough threshold, and the time lapse between the peak and trough of this component signal as feature values. The hardware components are simpler and fewer than in past applications, but better feature values are chosen to decrease the processing complexity for classification calculations and to enable the development of a decision tree algorithm to recognize and classify human motions. Together these approaches drastically reduce problems with inconvenience and reliability in comparison with conventional methods. The results show that the proposed method reaches a 93.89% recognition accuracy rate, which allows for accurate identification and classification of simple motion categories.