A4 Article in conference proceedings
Research for JYU : An AI-Driven, Fully Remote Mobile Application for Functional Exercise Testing (2024)

Cronin, N., Lehtiö, A., & Talaskivi, J. (2024). Research for JYU : An AI-Driven, Fully Remote Mobile Application for Functional Exercise Testing. In M. Särestöniemi, P. Keikhosrokiani, D. Singh, E. Harjula, A. Tiulpin, M. Jansson, M. Isomursu, M. van Gils, S. Saarakkala, & J. Reponen (Eds.), Digital Health and Wireless Solutions : First Nordic Conference​, NCDHWS 2024, Oulu, Finland, May 7-8, 2024, Proceedings, Part I (2083, pp. 279-287). Springer. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-031-59091-7_18

JYU authors or editors

Publication details

All authors or editors: Cronin, Neil; Lehtiö, Ari; Talaskivi, Jussi

Parent publication: Digital Health and Wireless Solutions : First Nordic Conference​, NCDHWS 2024, Oulu, Finland, May 7-8, 2024, Proceedings, Part I

Parent publication editors: Särestöniemi, Mariella; Keikhosrokiani, Pantea; Singh, Daljeet; Harjula, Erkki; Tiulpin, Aleksei; Jansson, Miia; Isomursu, Minna; van Gils, Mark; Saarakkala, Simo; Reponen, Jarmo

Place and date of conference: Oulu, Finland, 7.-8.5.2024

ISBN: 978-3-031-59090-0

eISBN: 978-3-031-59091-7

Journal or series: Communications in Computer and Information Science

ISSN: 1865-0929

eISSN: 1865-0937

Publication year: 2024

Volume: 2083

Pages range: 279-287

Number of pages in the book: 433

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-031-59091-7_18

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/95125

Abstract

As people live longer, the incidence and severity of health problems increases, placing strain on healthcare systems. There is an urgent need for resource-wise approaches to healthcare. We present a system built using open-source tools that allows health and functional capacity data to be collected remotely. The app records performance on functional tests using the phone’s built-in camera and provides users with immediate feedback. Pose estimation is used to detect the user in the video. The x, y coordinates of key body landmarks are then used to compute further metrics such as joint angles and repetition durations. In a proof-of-concept study, we collected data from 13 patients who had recently undergone knee ligament or knee replacement surgery. Patients performed the sit-to-stand test twice, with an average difference in test duration of 1.12 s (range: 1.16–3.2 s). Y-coordinate locations allowed us to automatically identify repetition start and end times, while x, y coordinates were used to compute joint angles, a common rehabilitation outcome variable. Mean difference in repetition duration was 0.1 s (range: −0.4–0.4 s) between trials 1 and 2. Bland-Altman plots confirmed general test-retest consistency within participants. We present a mobile app that enables functional tests to be performed remotely and without supervision. We also demonstrate real-world feasibility, including the ability to automate the entire process, from testing to analysis and the provision of real-time feedback. This approach is scalable, and could form part of national health strategies, allowing healthcare providers to minimise the need for in-person appointments whilst yielding cost savings.

Keywords: computer vision; remote access; rehabilitation; mobile apps

Free keywords: computer vision; remote rehabilitation; mobile health app

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2024

Preliminary JUFO rating: 1