Exercise repetition detection for resistance training based on smartphones

Igor Pernek (Author), Karin Anna Hummel (Author), Peter Kokol (Author)

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;

Data

Language:	English
Year of publishing:	2012
Typology:	1.01 - Original Scientific Article
Organization:	UM FZV - Faculty of Health Sciences
UDC:	621.39:004
COBISS:	1867684
ISSN:	1617-4909
Views:	1203
Downloads:	87
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
URN:	URN:SI:UM:
Type (COBISS):	Not categorized
Pages:	str. 781-782
Volume:	ǂVol. ǂ17
Issue:	ǂiss. ǂ4
Chronology:	apr. 2013
DOI:	10.1007/s00779-012-0626-y
ID:	1437325