Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation

Javier Conte Alcaraz; Sanam Moghaddamnia; Nils Poschadel; Jurgen Peissig

doi:10.1109/MLSP.2018.8517005

Details

Original language	English
Title of host publication	PROCEEDINGS OF MLSP2018
Editors	Nelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
Number of pages	6
ISBN (electronic)	9781538654774
Publication status	Published - 31 Oct 2018
Event	28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Aalborg, Denmark Duration: 17 Sept 2018 → 20 Sept 2018

Publication series

Name	IEEE Workshop on Machine Learning for Signal Processing
ISSN (electronic)	1551-2541

Abstract

A novel real-time acoustic feedback (RTAF) based on machine learning to reduce the duration and to improve the progress in the rehabilitation is presented. Wearable technology (WT) has emerged as a viable means to provide low-cost digital healthcare and therapy course outside the medical environment like hospitals and clinics. In this paper we show that the RTAF together with WTs can offer an excellent solution to be used in rehabilitation. The method of RTAF based on machine learning as well as a study for proving its effectiveness are presented below. The results show a faster recovery time using RTAF. The proposed RTAF shows a great potential to be used and deployed to support digital healthcare, therapy and rehabilitation.

Keywords

Acoustic Feedback, Android, Gait, Machine Learning, Real-Time, Rehabilitation, Wearable Technology

ASJC Scopus subject areas

Computer Science(all)
Human-Computer Interaction
Computer Science(all)
Signal Processing

Cite this

Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation. / Alcaraz, Javier Conte; Moghaddamnia, Sanam; Poschadel, Nils et al.
PROCEEDINGS OF MLSP2018. ed. / Nelly Pustelnik; Zheng-Hua Tan; Zhanyu Ma; Jan Larsen. 2018. (IEEE Workshop on Machine Learning for Signal Processing).

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Alcaraz, JC, Moghaddamnia, S, Poschadel, N & Peissig, J 2018, Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation. in N Pustelnik, Z-H Tan, Z Ma & J Larsen (eds), PROCEEDINGS OF MLSP2018. IEEE Workshop on Machine Learning for Signal Processing, 28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018, Aalborg, Denmark, 17 Sept 2018. https://doi.org/10.1109/MLSP.2018.8517005

Alcaraz, J. C., Moghaddamnia, S., Poschadel, N., & Peissig, J. (2018). Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation. In N. Pustelnik, Z.-H. Tan, Z. Ma, & J. Larsen (Eds.), PROCEEDINGS OF MLSP2018 (IEEE Workshop on Machine Learning for Signal Processing). https://doi.org/10.1109/MLSP.2018.8517005

Alcaraz JC, Moghaddamnia S, Poschadel N, Peissig J. Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation. In Pustelnik N, Tan ZH, Ma Z, Larsen J, editors, PROCEEDINGS OF MLSP2018. 2018. (IEEE Workshop on Machine Learning for Signal Processing). doi: 10.1109/MLSP.2018.8517005

Alcaraz, Javier Conte ; Moghaddamnia, Sanam ; Poschadel, Nils et al. / Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation. PROCEEDINGS OF MLSP2018. editor / Nelly Pustelnik ; Zheng-Hua Tan ; Zhanyu Ma ; Jan Larsen. 2018. (IEEE Workshop on Machine Learning for Signal Processing).

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title = "Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation",

abstract = "A novel real-time acoustic feedback (RTAF) based on machine learning to reduce the duration and to improve the progress in the rehabilitation is presented. Wearable technology (WT) has emerged as a viable means to provide low-cost digital healthcare and therapy course outside the medical environment like hospitals and clinics. In this paper we show that the RTAF together with WTs can offer an excellent solution to be used in rehabilitation. The method of RTAF based on machine learning as well as a study for proving its effectiveness are presented below. The results show a faster recovery time using RTAF. The proposed RTAF shows a great potential to be used and deployed to support digital healthcare, therapy and rehabilitation.",

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AU - Peissig, Jurgen

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Research@Leibniz University