TY  - JOUR
JF  - Sensors
IS  - 19
KW  - telephysiotherapy; PoseNet; exercise classification; machine learning; ensemble models; healthcare technology; Google MediaPipe
UR  - http://doi.org/10.3390/s24196325
VL  - 24
TI  - Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models
AV  - public
Y1  - 2024/09//
A1  - Hussain, Shahzad
A1  - Siddiqui, Hafeez Ur Rehman
A1  - Saleem, Adil Ali
A1  - Raza, Muhammad Amjad
A1  - Alemany Iturriaga, Josep
A1  - Velarde-Sotres, Álvaro
A1  - Díez, Isabel De la Torre
A1  - Dudley, Sandra
ID  - uninimx14482
SN  - 1424-8220
N2  - Telephysiotherapy has emerged as a vital solution for delivering remote healthcare, particularly in response to global challenges such as the COVID-19 pandemic. This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose estimation model. A dataset was collected from 49 participants (35 males, 14 females) performing seven distinct exercises, with twelve anatomical landmarks then extracted using the Google MediaPipe library. Each landmark was represented by four features, which were used for classification. The core challenge addressed in this research involves ensuring accurate and real-time exercise classification across diverse body morphologies and exercise types. Several tree-based classifiers, including Random Forest, Extra Tree Classifier, XGBoost, LightGBM, and Hist Gradient Boosting, were employed. Furthermore, two novel ensemble models called RandomLightHist Fusion and StackedXLightRF are proposed to enhance classification accuracy. The RandomLightHist Fusion model achieved superior accuracy of 99.6%, demonstrating the system?s robustness and effectiveness. This innovation offers a practical solution for providing real-time feedback in telephysiotherapy, with potential to improve patient outcomes through accurate monitoring and assessment of exercise performance.
ER  -