Studies in health technology and informatics · 2025
Monitoring Respiratory Rehabilitation with YOLO Pose: Usability and Accuracy Assessment of a Mobile App for Spirometer Tracking.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ferrari Davide, Belotti Pietro, Forcella Andrea, Lecchi Lorenzo, Sana Nicola, Mariani Silvia, Marchetto Giovanni, Vitali Andrea
Аннотация
BACKGROUND: Adherence to home-based respiratory rehabilitation is a significant challenge for cardiac surgery patients. Few studies have attempted to address this problem.
METHODS: This research introduces a digital solution to improve adherence to respiratory rehabilitation by offering virtual supervision. It leverages the YOLOv11 pose detection algorithm to track the Voldyne® 2500 incentive spirometer and monitor rehabilitation sessions. Usability and accuracy were evaluated with nine healthy volunteers, combining objective performance metrics with the System Usability Scale (SUS) survey.
RESULTS: The models demonstrated high accuracy in spirometer tracking and efficient inference times. The app achieved an average SUS score of 83%, indicating good usability, though refinements to the monitoring algorithms are recommended.
CONCLUSION: This low-cost, non-invasive solution shows potential for clinical use. Future efforts will focus on enhancing usability and accuracy to prepare the app for clinical trials.
METHODS: This research introduces a digital solution to improve adherence to respiratory rehabilitation by offering virtual supervision. It leverages the YOLOv11 pose detection algorithm to track the Voldyne® 2500 incentive spirometer and monitor rehabilitation sessions. Usability and accuracy were evaluated with nine healthy volunteers, combining objective performance metrics with the System Usability Scale (SUS) survey.
RESULTS: The models demonstrated high accuracy in spirometer tracking and efficient inference times. The app achieved an average SUS score of 83%, indicating good usability, though refinements to the monitoring algorithms are recommended.
CONCLUSION: This low-cost, non-invasive solution shows potential for clinical use. Future efforts will focus on enhancing usability and accuracy to prepare the app for clinical trials.