A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity

Bouten, Cvc Carlijn; Koekkoek, K.T.M.; Verduin, M.; Kodde, R.; Janssen, JD Jan

doi:10.1109/10.554760

Cited by 757 publications

(497 citation statements)

References 26 publications

Supporting

Mentioning

472

Contrasting

Unclassified

Order By: Relevance

“…Triaxial accelerometers provide greater precision for quantifying walking. Of these, the Tracmor Unit has been most widely validated (Bouten et al, 1994(Bouten et al, , 1997aPannemans et al, 1995;Westerterp & Bouten, 1997;Westerterp et al, 1996). Against a motor-driven rotating arm, the Tracmor Unit shows test-to-test repeatability of 0.5% and when applied acceleration is plotted against Tracmor output, rE0.99 (Bouten et al, 1997a).…”

Section: Discussionmentioning

confidence: 99%

“…The triaxial accelerometer (Bouten et al, , 1997a comprised three uniaxial piezoresistive accelerometers (ICSensors 3031-010) mounted orthogonally in a resin block (50 Â 30 Â 8 mm, 16 g) whereby each axis is independently sensed. The accelerometer was worn on the lower back attached using a 70 Â 85 mm piece of adhesive plastic (Tegaderm, 3 M, MN, USA).…”

Section: Tracmor Unitmentioning

confidence: 99%

“…Hence, because free-living individuals ambulate throughout the day, walking is likely to represent a major component of nonexercise activity thermogenesis (NEAT) (Levine et al, 1999b). One of the most precise and reproducible tools for quantifying body displacement/acceleration in free-living individuals is the triaxial accelerometer for movement registration (Tracmor) (Bouten et al, 1994(Bouten et al, , 1997aPannemans et al, 1995). In previous studies (Levine et al, 2001), Tracmor output has been shown to be highly reproducible, stable over 10 days and there are strong linear relations between Tracmor output and velocity regardless of whether subjects walk on ground or a treadmill.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tracmor system for measuring walking energy expenditure

et al. 2003

View full text Add to dashboard Cite

Objective: Walking is an important mode of exercise and is likely to represent a major component of nonexercise activity thermogenesis. The question arises, how best to quantify walking-energy expenditure (EE) in free-living individuals. The triaxial accelerometer for movement registration (Tracmor) is a valid measure of body displacement and so we wanted to evaluate this tool for quantifying walking-EE. Hypothesis: In this study, we test the hypothesis that walking-EE, measured in a Room Calorimeter, can be predicted from Tracmor output using a regression equation derived from a brief Tracmor/treadmill/Metabolic Cart protocol. Design: First, 11 healthy subjects completed a 40-min procedure whereby they wore a Tracmor unit and walked on a treadmill at 0, 1, 2 and 3 mph while EE was measured using a Metabolic Cart. This allowed a regression equation to be defined for each subject to convert Tracmor output to EE. Each subject then entered a Room Calorimeter wearing the Tracmor Unit and walked at two self-selected velocities ('slow', 'fast') while EE was measured. 'Tracmor/regression equation' predictions of walking-EE were compared with Room Calorimeter measurements of walking-EE for the two velocities. Results: The 'Tracmor/regression equation' prediction of EE for walking slowly was 6.3671.67 kJ/min, and for walking fast it was 11.072.60 kJ/min. Room Calorimeter measurements were 6.4371.85 and 10.973.03 kJ/min, respectively. The intraclass correlation coefficient for slow-paced walking was 0.93 (Po0.001), and for fast-paced walking it was 0.82 (Po0.005).

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Tracmor Unitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tracmor system for measuring walking energy expenditure

et al. 2003

View full text Add to dashboard Cite

show abstract

“…Other studies on semantic place labeling so far [Reddy et al, 2010, Consolvo et al, 2008, Arase et al, 2010, Bouten et al, 1997, Perrin et al, 2000, Junker et al, 2004, Preece et al, 2009, Berchtold et al, 2010, Ravi et al, 2005, Bao and Intille, 2004, Chang et al, 2007, Farringdon et al, 1999, Kern et al, 2003, Mantyjarvi et al, 2001, Stikic et al, 2008, Zinnen et al, 2009, Lester et al, 2005, Siewiorek et al, 2003 are mostly based on unlabeled data or on a small number of sensor and state data. The field of physical activity recognition based on accelerometer sensor data is well researched [Consolvo et al, 2008, Arase et al, 2010, Berchtold et al, 2010, Bao and Intille, 2004, Farringdon et al, 1999, Kern et al, 2003].…”

Section: Related Workmentioning

confidence: 99%

Sensor Fusion for Semantic Place Labeling

Roor

Hess²,

Saveriano³

et al. 2017

Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems

View full text Add to dashboard Cite

“…These coefficients can be obtained by several statistical and geometrical formulas, depending on whether the situation involves 6 simple statistical values or mathematical vectors. The most commonly used is Pearson's product-moment coefficient (ρ x,y ) [45] also known as the sample correlation coefficient, calculated as the ratio of the covariance of the signals along the x-axis and y-axis to the product of their standard deviations:…”

Section: Signal Correlation and Correlation-coefficientmentioning

confidence: 99%

Preprocessing techniques for context recognition from accelerometer data

Figo

Diniz

Ferreira

et al. 2010

Pers Ubiquit Comput

505

350

View full text Add to dashboard Cite

The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care, and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers it is possible to determine if a user is walking or running at a specific time of day. By correlating this knowledge with GPS data it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

show abstract

A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity

Cited by 757 publications

References 26 publications

Tracmor system for measuring walking energy expenditure

Tracmor system for measuring walking energy expenditure

Sensor Fusion for Semantic Place Labeling

Preprocessing techniques for context recognition from accelerometer data

Contact Info

Product

Resources

About