Naseer Aisha, Raza Ali, Afzal Hadeeqa, Smerat Aseel, Fitriyani Norma Latif, Gu Yeonghyeon, Syafrudin Muhammad
Institute of Information Technology, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan, Pakistan.
Department of Software Engineering, University of Lahore, Lahore, Pakistan.
PeerJ Comput Sci. 2025 Apr 29;11:e2854. doi: 10.7717/peerj-cs.2854. eCollection 2025.
To introduce and evaluate an efficient neural network approach for human pose estimation and correction during physical therapy exercises using wearable sensor data.
We leveraged benchmark data consisting of 276,625 records from wearable inertial and magnetic sensors. A novel method termed Random Forest Long Short-Term Memory (RFL), which integrates long short-term memory and Random Forest neural networks, was implemented for transfer feature engineering. The smartphone sensor data was used to generate new temporal and probabilistic features. These features were then utilized in machine learning methods to classify physical therapy exercises. Rigorous experiments, including k-fold validation and hyperparameter optimization, were conducted to validate the performance of the RFL approach.
The RFL approach demonstrated superior performance, achieving a remarkable 99% accuracy with the Random Forest method. The rigorous experiments confirmed the efficacy and reliability of the method in classifying physical therapy exercises.
The proposed RFL method introduces a novel feature generation approach enhancing the accuracy of physical therapy exercise classification and correction. This innovative integration not only improves rehabilitation monitoring but also paves the way for more adaptive and intelligent physiotherapy assistance systems. By leveraging sensor data and advanced machine learning techniques, it has the potential to mitigate risks associated with disabilities and major diseases, thereby offering a feasible alternative to frequent clinic visits for consistent therapist guidance.
介绍并评估一种高效的神经网络方法，用于利用可穿戴传感器数据在物理治疗练习期间进行人体姿势估计和校正。
我们利用了由可穿戴惯性和磁传感器的276,625条记录组成的基准数据。实施了一种称为随机森林长短期记忆（RFL）的新方法，该方法整合了长短期记忆和随机森林神经网络，用于转移特征工程。智能手机传感器数据用于生成新的时间和概率特征。然后将这些特征用于机器学习方法中以对物理治疗练习进行分类。进行了包括k折验证和超参数优化在内的严格实验，以验证RFL方法的性能。
RFL方法表现出卓越的性能，使用随机森林方法实现了高达99%的显著准确率。严格的实验证实了该方法在对物理治疗练习进行分类方面的有效性和可靠性。
所提出的RFL方法引入了一种新颖的特征生成方法，提高了物理治疗练习分类和校正的准确性。这种创新的整合不仅改善了康复监测，还为更具适应性和智能性的物理治疗辅助系统铺平了道路。通过利用传感器数据和先进的机器学习技术，它有可能降低与残疾和重大疾病相关的风险，从而为频繁前往诊所接受持续治疗师指导提供了一种可行的替代方案。
