COPDTrainer : a smartphone-based motion rehabilitation training system with real-time acoustic feedback

G. Spina, G. Huang, A. Vaes, M.A. Spruit, O.D. Amft

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

39 Citations (Scopus)
1 Downloads (Pure)

Abstract

Patient motion training requires adaptive, personalized exercise models and systems that are easy to handle. In this paper, we evaluate a training system based on a smartphone that integrates in clinical routines and serves as a tool for therapist and patient. Only the smartphone's build-in inertial sensors were used to monitor exercise execution and providing acoustic feedback on exercise performance and exercise errors. We used a sinusoidal motion model to exploit the typical repetitive structure of motion exercises. A Teach-mode was used to personalize the system by training under the guidance of a therapist and deriving exercise model parameters. Subsequently, in a Train-mode, the system provides exercise feedback. We validate our approach in a validation with healthy volunteers and in an intervention study with COPD patients. System performance, trainee performance, and feedback efficacy were analysed. We further compare the therapist and training system performances and demonstrate that our approach is viable.
Original languageEnglish
Title of host publicationProceedings of the ACM Internetional Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp 2013), 8-12 September 2013, Zurich, Switzerland
PublisherAssociation for Computing Machinery, Inc
Pages597-606
DOIs
Publication statusPublished - 2013
Event2013ACM Internetional Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013) - Zurich, Switzerland
Duration: 8 Sep 201312 Sep 2013

Conference

Conference2013ACM Internetional Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013)
Abbreviated titleUbicomp 2013
CountrySwitzerland
CityZurich
Period8/09/1312/09/13

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