Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Autoren

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

Organisationseinheiten

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Details

OriginalspracheEnglisch
Titel des SammelwerksPROCEEDINGS OF MLSP2018
Herausgeber/-innenNelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
Seitenumfang6
ISBN (elektronisch)9781538654774
PublikationsstatusVeröffentlicht - 31 Okt. 2018
Veranstaltung28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Aalborg, Dänemark
Dauer: 17 Sept. 2018 → 20 Sept. 2018

Publikationsreihe

NameIEEE Workshop on Machine Learning for Signal Processing
ISSN (elektronisch)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.

ASJC Scopus Sachgebiete

Zitieren

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

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › 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 (Hrsg.), PROCEEDINGS OF MLSP2018. IEEE Workshop on Machine Learning for Signal Processing, 28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018, Aalborg, Dänemark, 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 (Hrsg.), 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, Hrsg., 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. Hrsg. / 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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Download

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T1 - Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation

AU - Alcaraz, Javier Conte

AU - Moghaddamnia, Sanam

AU - Poschadel, Nils

AU - Peissig, Jurgen

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N2 - 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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