A Multipurpose Wearable Sensor-Based System for Weight Training

In recent years, there has been growing interest in automated tracking and detection of sports activities. Researchers have shown that providing activity information to individuals during their exercise routines can greatly help them achieving goals. particular, such would maximize workout efficiency prevent overreaching overtraining. This paper presents the development a novel multipurpose wearable device for automatic weight detection, type recognition, count repetition activities as training. The monitors weights by using an inertial measurement unit (IMU), accelerometer, three force sensors mounted glove, classifies utilizing developed machine learning models. For purposes, different classifiers including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Multi-layer Perceptron Neural Networks (MLP) were investigated. K nearest neighbor (KNN), Decision Tree (DT), Random Forest (RF), SVM models trained examined. Experimental results indicate classifier achieve highest accuracy whereas RF outperform other recognition. feasibility developing provide in-situ accurate regarding lifted with minimum physical intervention.