TY - JOUR
T1 - Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models
AU - Hussain, Shahzad
AU - Siddiqui, Hafeez Ur Rehman
AU - Saleem, Adil Ali
AU - Raza, Muhammad Amjad
AU - Iturriaga, Josep Alemany
AU - Velarde-Sotres, Álvaro
AU - Díez, Isabel De la Torre
AU - Dudley, Sandra
PY - 2024/9/29
Y1 - 2024/9/29
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.
AB - 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.
KW - machine learning
KW - ensemble models
KW - PoseNet
KW - Google MediaPipe
KW - healthcare technology
KW - exercise classification
KW - telephysiotherapy
UR - https://www.mdpi.com/1424-8220/24/19/6325
U2 - 10.3390/s24196325
DO - 10.3390/s24196325
M3 - Article
SN - 1424-8220
VL - 24
JO - Sensors
JF - Sensors
IS - 19
M1 - 6325
ER -