eprintid: 14207 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/42/07 datestamp: 2024-09-16 23:30:07 lastmod: 2024-09-16 23:30:09 status_changed: 2024-09-16 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_id: creators_id: creators_id: creators_id: creators_id: josep.alemany@uneatlantico.es creators_id: alvaro.velarde@uneatlantico.es creators_id: title: Therapeutic Exercise Recognition Using a Single UWB Radar with AI-Driven Feature Fusion and ML Techniques in a Real Environment ispublished: pub subjects: uneat_dp subjects: uneat_eng divisions: uniromana_produccion_cientifica full_text_status: public keywords: physiotherapy; ultrawide band (UWB) radar; therapeutic exercise; machine learning; opto-electronic sensors; ensemble method abstract: Physiotherapy plays a crucial role in the rehabilitation of damaged or defective organs due to injuries or illnesses, often requiring long-term supervision by a physiotherapist in clinical settings or at home. AI-based support systems have been developed to enhance the precision and effectiveness of physiotherapy, particularly during the COVID-19 pandemic. These systems, which include game-based or tele-rehabilitation monitoring using camera-based optical systems like Vicon and Microsoft Kinect, face challenges such as privacy concerns, occlusion, and sensitivity to environmental light. Non-optical sensor alternatives, such as Inertial Movement Units (IMUs), Wi-Fi, ultrasound sensors, and ultrawide band (UWB) radar, have emerged to address these issues. Although IMUs are portable and cost-effective, they suffer from disadvantages like drift over time, limited range, and susceptibility to magnetic interference. In this study, a single UWB radar was utilized to recognize five therapeutic exercises related to the upper limb, performed by 34 male volunteers in a real environment. A novel feature fusion approach was developed to extract distinguishing features for these exercises. Various machine learning methods were applied, with the EnsembleRRGraBoost ensemble method achieving the highest recognition accuracy of 99.45%. The performance of the EnsembleRRGraBoost model was further validated using five-fold cross-validation, maintaining its high accuracy. date: 2024-08 publication: Sensors volume: 24 number: 17 pagerange: 5533 id_number: doi:10.3390/s24175533 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s24175533 access: open language: en citation: Artículo Materias > Educación física y el deporte Materias > Ingeniería Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Physiotherapy plays a crucial role in the rehabilitation of damaged or defective organs due to injuries or illnesses, often requiring long-term supervision by a physiotherapist in clinical settings or at home. AI-based support systems have been developed to enhance the precision and effectiveness of physiotherapy, particularly during the COVID-19 pandemic. These systems, which include game-based or tele-rehabilitation monitoring using camera-based optical systems like Vicon and Microsoft Kinect, face challenges such as privacy concerns, occlusion, and sensitivity to environmental light. Non-optical sensor alternatives, such as Inertial Movement Units (IMUs), Wi-Fi, ultrasound sensors, and ultrawide band (UWB) radar, have emerged to address these issues. Although IMUs are portable and cost-effective, they suffer from disadvantages like drift over time, limited range, and susceptibility to magnetic interference. In this study, a single UWB radar was utilized to recognize five therapeutic exercises related to the upper limb, performed by 34 male volunteers in a real environment. A novel feature fusion approach was developed to extract distinguishing features for these exercises. Various machine learning methods were applied, with the EnsembleRRGraBoost ensemble method achieving the highest recognition accuracy of 99.45%. The performance of the EnsembleRRGraBoost model was further validated using five-fold cross-validation, maintaining its high accuracy. metadata Hussain, Shahzad; Siddiqui, Hafeez Ur Rehman; Saleem, Adil Ali; Raza, Muhammad Amjad; Alemany Iturriaga, Josep; Velarde-Sotres, Álvaro y Díez, Isabel De la Torre mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, josep.alemany@uneatlantico.es, alvaro.velarde@uneatlantico.es, SIN ESPECIFICAR (2024) Therapeutic Exercise Recognition Using a Single UWB Radar with AI-Driven Feature Fusion and ML Techniques in a Real Environment. Sensors, 24 (17). p. 5533. ISSN 1424-8220 document_url: http://repositorio.uniromana.edu.do/id/eprint/14207/1/sensors-24-05533.pdf