Sensor-Based Data Acquisition via Ubiquitous Device to Detect Muscle Strength Training Activities
This work addresses the need for monitoring specific musculoskeletal exercises to support healthy aging, but it is incremental as it applies existing HAR and LSTM methods to a new domain.
The research tackled the problem of detecting muscle strength training activities by using smartphone sensors for Human Activity Recognition, successfully identifying key sensor attributes from 25 participants performing nine motion types to develop LSTM-based machine learning models.
Maintaining a high quality of life through physical activities (PA) to prevent health decline is crucial. However, the relationship between individuals health status, PA preferences, and motion factors is complex. PA discussions consistently show a positive correlation with healthy aging experiences, but no explicit relation to specific types of musculoskeletal exercises. Taking advantage of the increasingly widespread existence of smartphones, especially in Indonesia, this research utilizes embedded sensors for Human Activity Recognition (HAR). Based on 25 participants data, performing nine types of selected motion, this study has successfully identified important sensor attributes that play important roles in the right and left hands for muscle strength motions as the basis for developing machine learning models with the LSTM algorithm.