Classification and monitoring of arm exercises using machine learning and wrist-worn band

Abstract

Exercise is essential for a healthy lifestyle, thus it is important to consider how to keep proper posture when performing arm exercises at home. This work uses wrist-worn bands with the MPU6050 sensor to address these issues, which collects motion data using acceleration measurements. The individuals in the dataset are completing a variety of activities at varying ranges of motion. Machine learning-based classification methods are then applied after the pre-processing and feature extraction of the gathered data. An App prototype integrated with a WiFi module and Cloud infrastructure is created to enable real-time data collecting and storage. The Arduino IDE is used to send the collected data to the ThingSpeak platform, where it is subsequently sent to MATLAB for additional analysis. The studied data is then returned to ThingSpeak, where the program displays the findings. This approach reduces the risk of injuries caused by bad posture by enabling people to continue regular workouts at home without requiring a personal trainer or a particular environment. The findings of this work shed important light on the performance of Boosted Trees, Quadratic SVM, Subspace KNN, and Fine KNN algorithms for arm exercises employing a wrist-worn band with an MPU6050 sensor. The Fine KNN has the highest accuracy of 91.3% among all implemented algorithms.