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Title:Exercise repetition detection for resistance training based on smartphones
Authors:ID Pernek, Igor (Author)
ID Hummel, Karin Anna (Author)
ID Kokol, Peter (Author)
Files:URL http://link.springer.com/article/10.1007/s00779-012-0626-y/fulltext.html
 
Language:English
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:FZV - Faculty of Health Sciences
Abstract:Regular exercise is one of the most important factors in maintaining a good state of health. In the past, different systems have been proposed to assist people when exercising. While most of those systems focus only on cardio exercises such as running and cycling, we exploit smartphones to support leisure activities with a focus on resistance training. We describe how off-the-shelf smartphones without additional external sensors can be leveragedto capture resistance training data and to give reliable training feedback. We introduce a dynamic time warping-based algorithm to detect individual resistance training repetitions from the smartphoneʼs acceleration stream. We evaluate the algorithm in terms of the number of correctly recognized repetitions. Additionally, for providing feedback about the qualityof repetitions, we use the duration of an individual repetition and analyze how accurately start and end times of repetitions can be detected by our algorithm. Our evaluations are based on 3,598 repetitions performed by tenvolunteers exercising in two distinct scenarios, a gym and a natural environment. The results show an overall repetition miscount rate of about 1 %and overall temporal detection error of about 11 % of individual repetition duration.
Keywords:wearable systems, resistance training, smartphone, accelerometer
Year of publishing:2012
Number of pages:str. 781-782
Numbering:Vol. 17, iss. 4
PID:20.500.12556/DKUM-50048 New window
UDC:621.39:004
ISSN on article:1617-4909
COBISS.SI-ID:1867684 New window
DOI:10.1007/s00779-012-0626-y New window
NUK URN:URN:SI:UM:DK:NPM6CB8H
Publication date in DKUM:10.07.2015
Views:1976
Downloads:105
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Personal and ubiquitous computing
Publisher:Springer London
ISSN:1617-4909
COBISS.SI-ID:515751705 New window

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