eprintid: 14482 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/44/82 datestamp: 2024-10-01 23:30:07 lastmod: 2024-10-01 23:30:09 status_changed: 2024-10-01 23:30:07 type: article metadata_visibility: show creators_name: Hussain, Shahzad creators_name: Siddiqui, Hafeez Ur Rehman creators_name: Saleem, Adil Ali creators_name: Raza, Muhammad Amjad creators_name: Alemany Iturriaga, Josep creators_name: Velarde-Sotres, Álvaro creators_name: Díez, Isabel De la Torre creators_name: Dudley, Sandra creators_id: creators_id: creators_id: creators_id: creators_id: josep.alemany@uneatlantico.es creators_id: alvaro.velarde@uneatlantico.es creators_id: creators_id: title: Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models ispublished: pub subjects: uneat_bm subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: telephysiotherapy; PoseNet; exercise classification; machine learning; ensemble models; healthcare technology; Google MediaPipe abstract: 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. date: 2024-09 publication: Sensors volume: 24 number: 19 pagerange: 6325 id_number: doi:10.3390/s24196325 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s24196325 access: open language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Hussain, Shahzad; Siddiqui, Hafeez Ur Rehman; Saleem, Adil Ali; Raza, Muhammad Amjad; Alemany Iturriaga, Josep; Velarde-Sotres, Álvaro; Díez, Isabel De la Torre y Dudley, Sandra mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, josep.alemany@uneatlantico.es, alvaro.velarde@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models. Sensors, 24 (19). p. 6325. ISSN 1424-8220 document_url: http://repositorio.unib.org/id/eprint/14482/1/sensors-24-06325.pdf