A method to compare new and traditional accelerometry data in physical activity monitoring

Hees, Vincent T. van; Pias, Marcelo; Taherian, Salman; Ekelund, Ulf; Brage, Sören

doi:10.1109/wowmom.2010.5534986

Cited by 38 publications

(40 citation statements)

References 22 publications

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“…Using the raw acceleration signal, activity counts can be computed in an analogous fashion to commercial devices (Van Hees et al, 2012), however there is a clear area for growth in developing beyond simple overall activity quantification, potentially using time-series analysis of raw acceleration to highlight the fundamental differences in similar movements.…”

Section: Resultsmentioning

confidence: 99%

“…Raw acceleration data was uploaded into MatLab (MATLAB version R2016a), where the subsequent movement characteristic; integrated acceleration was derived. The integrated acceleration was determined using an integration of the rectified raw acceleration signal in the radial axis and correspondent to the computation used to derive the standard 'activity counts' by other commercial devices (van Hees et al, 2010).…”

Section: Memsmentioning

confidence: 99%

“…Accelerometers are the de facto standard in objectively measuring physical activity (Mathie et al, 2004, Van Hees et al, 2012. Commercial devices (such as; ActiGraph, ActiCal) measure activity in the form of 'activity counts', which summarize data over a user-specified epoch, reducing the burden of data management, analysis, and interpretation (Brond and Arvidson, 2015).…”

Section: Introductionmentioning

confidence: 99%

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A Kinematic Analysis of Fundamental Movement Skills

Clark

Barnes

Holton

et al. 2016

Sport Science Review

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Fundamental movement skills are considered the basic building blocks for movement and provide the foundation for specialized and sport-specific movement skills required for participation in a variety of physical activities. However, kinematic analyses of fundamental movement has not been performed. The aims of this study were to, (1) characterise the relationship between facets of fundamental movement and, (2) characterise the relationship between overall integrated acceleration and three-dimensional kinematic variables whilst performing fundamental movement skills. Eleven participants (10±0.8y, 1.41±0.07m, 33.4±8.6kg, body mass index; 16.4±3.1 kg·m2) took part in this study, had anthropometric variables recorded and performed a series of fundamental movement tasks, whilst wearing a tri-axial accelerometer and were recorded using a three-dimensional motion capture system. Maximum shoulder external rotation (°) and maximum shoulder internal rotation velocity (°.s−1) (r=0.86, p<0.001), mediolateral centre of mass range (cm) and centre of mass coefficient of variation (%) (r=0.83, p<0.001), maximum stride angle (°) in the jog and walk (r=0.74, p=0.01) and maximum sprint stride angle and maximum shoulder internal rotation velocity (°.s−1) (r=0.67, p<0.02) were significantly correlated. Maximum sprint stride angle (hip: r=0.96, p<0.001, ankle: r=0.97, p<0.001) and maximum internal rotation velocity (ankle: r=0.6, p=0.05) were significantly correlated to overall integrated acceleration. Overall integrated acceleration was comparable between participants (CV: 10.5), whereas three-dimensional variables varied by up to 65%. Although overall integrated acceleration was comparable between participants, three-dimensional variables were much more varied. Indicating that although overall activity may be correspondent, the characteristics of a child’s movement may be highly varied.

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Section: Resultsmentioning

confidence: 99%

Section: Memsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Kinematic Analysis of Fundamental Movement Skills

Clark

Barnes

Holton

et al. 2016

Sport Science Review

View full text Add to dashboard Cite

show abstract

“…Due to relation between PA and movement, accelerometers can be used to measure EE [2]. They present several advantages: low cost, low battery consumption, small size or integration into wearable devices, making them ideal to measure activities of daily living [3,4]. Nevertheless, they present several disadvantages: they underestimate EE at higher intensities, they generally do not identify the type of physical activity and they cannot directly derive energy consumption or activity levels.…”

Section: Introductionmentioning

confidence: 99%

“…In [24] a study was carried out to get filter characteristics from the action of Actilife software on raw acceleration signals. In [4] a mechanical oscillator was used to find the filter shape. In other studies, activity counts were extracted by a low pass filter and a calculation of the area under the curve [14].…”

Section: Introductionmentioning

confidence: 99%