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.

通过利用嵌入式传感器进行人体活动识别以及 LSTM 算法，针对印度尼西亚的使用普及率日益增加的智能手机，本研究分析了 25 位参与者的数据，对九种运动类型进行了机器学习模型开发基础上的肌肉力量动作的重要传感器属性的研究，旨在维持高生活质量以预防健康衰退。