Towards a Complete Set of Gym Exercises Detection Using Smartphone Sensors

Abstract

Smartphones with gym exercises predictors can act as trainers for the gym-goers. However, various available solutions do not have the complete set of most practiced exercises. Therefore, in this research, a complete set of most practiced 26 exercises was identified from the literature. Among the exercises, 14 were unique and 12 were common to the existing literature. Furthermore, finding suitable smartphone attachment position(s) and the number of sensors to predict exercises with the highest possible accuracy were also the objectives of the research. Besides, this study considered the most number of participants (20) as compared to the existing literature (maximum 10). The results indicate three key lessons: (a) the most suitable classifier to predict a class (exercise) from the sensor-based data was found to be KNN (K-nearest neighbors); (b) the sensors placed at the three positions (arm, belly, and leg) could be more accurate than other positions for the gym exercises; and (c) accelerometer and gyroscope when combined can provide accurate classification up to 99.72% (using KNN as classifier at all 3 positions).