Human pose estimation in physiotherapy fitness exercise correction using novel transfer learning approach.

ABSTRACT:

Objective

To introduce and evaluate an efficient neural network approach for human pose estimation and correction during physical therapy exercises using wearable sensor data.

Methods

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 pe

SUBMITTER: Naseer A 

PROVIDER: S-EPMC12193033 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature