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.

翻译：通过身体活动维持高质量生活以防止健康衰退至关重要。然而，个体健康状况、运动偏好及动作因素之间的关系较为复杂。关于身体活动的讨论始终显示其与健康老龄化体验呈正相关，但未明确指向特定类型的肌肉骨骼运动。鉴于智能手机（尤其在印度尼西亚）日益普及的优势，本研究利用嵌入式传感器进行人体活动识别(HAR)。基于25名参与者执行九种选定动作的数据，本研究成功识别出对左右手肌力运动具有重要影响的传感器关键属性，为基于LSTM算法的机器学习模型开发奠定基础。