Accelerometer Profile Recognition of Swimming Strokes (P17)

Slawson, Sian; Justham, Laura; West, Andrew A.; Conway, Paul; Caine, Michael; Harrison, Robert

doi:10.1007/978-2-287-09411-8_10

Cited by 24 publications

(11 citation statements)

References 2 publications

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“…These allow to automate the tracking process, but investigation so far has only regarded identification of areas instead of single points, and motion on the sagittal plane. Alternative approaches based on accelerometric sensing units have also been adopted (Ohgi, 2002;Slawson et al, 2008;Callaway et al, 2009). This technology is relatively cheap and provides higher sampling rates; however, processing and interpretation of measured data is not straight-forward.…”

Section: Introductionmentioning

confidence: 99%

Markerless analysis of front crawl swimming

Ceseracciu

Sawacha

Fantozzi

et al. 2011

Journal of Biomechanics

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Section: Introductionmentioning

confidence: 99%

Markerless analysis of front crawl swimming

Ceseracciu

Sawacha

Fantozzi

et al. 2011

Journal of Biomechanics

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“…Davey et al used a sensor on the swimmer's hip to extract the arm strokes of front crawl swimming [5,6]. Chan has patented a method using a magnetic field sensor to count the number of lanes swum [7] and Slawson et al investigated how the four competition swim styles can be detected with wrist worn accelerometer sensors [8].…”

Section: Related Workmentioning

confidence: 99%

Pervasive computing in swimming: A model describing acceleration data of body worn sensors in crawl swimming

Bächlin

Tröster

2009

2009 Joint Conferences on Pervasive Computing (JCPC)

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We envision a pervasive, wearable computing system that can support swimmers in achieving their desired exercise goals by constantly monitoring their swim performance and providing the necessary feedback. In this paper we describe the underlying SwimModel to get a full understanding of the recorded acceleration data during swimming.This SwimModel consists of a body motion model based on classical mechanics. The model inputs are the arm and leg forces as well as the body torques. Based on the input forces and torques the model calculates the swimmer's acceleration, velocity and position as well as the angular accelerations, angular velocities and angles. Internally, the model considers the buoyancyforce and the dragforces. The transformation ofthe body motion into the sensor coordinate system and its sensing modalities is done by a sensor model. We validate our SwimModel by comparing the simulated sensor data with real sensor data recorded during swimming.

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“…The athlete or coach can use the information provided by the analysis as a supplement in daily training, as an aid to improve performance. In swimming, video analysis is a widespread method (Slawson et al, 2008). However, to determine swimming velocities, this technique requires post-processing, including manual digitization, which is time-consuming.…”

Section: Introductionmentioning

confidence: 99%

On the development of inexpensive speed and position tracking system for swimming

2015

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A semi-automated tracking system was developed for the analysis of swimming, using cameras, an LED diode marker, and a red swim cap. Four experienced young swimmers were equipped with a marker and a swim cap and their position and speed was tracked throughout above water and underwater swimming sequences, using post image processing in MATLAB. The results showed that this technique can be used to track a swimmer both under and above water, and identify speed fluctuations.

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Accelerometer Profile Recognition of Swimming Strokes (P17)

Cited by 24 publications

References 2 publications

Markerless analysis of front crawl swimming

Markerless analysis of front crawl swimming

Pervasive computing in swimming: A model describing acceleration data of body worn sensors in crawl swimming

On the development of inexpensive speed and position tracking system for swimming

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