Classification of human physical activity and energy expenditure estimation by accelerometry and barometry

Αναστασοπούλου, Παναγιώτα; Tansella, Michael; Stumpp, Jürgen; Shammas, Layal; Hey, Stefan

doi:10.1109/embc.2012.6347471

Cited by 27 publications

(20 citation statements)

References 7 publications

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“…In the first step the activity was classified in intervals of 4 s. For further explanation of the activity recognition process see the publication of Anastasopoulou et al (2012). The classification algorithm differentiated between the following seven activities: lying, rest (sitting/standing), cycling, uphill, downhill, level walking, and jogging.…”

Section: Methodsmentioning

confidence: 99%

Characteristics of the Activity-Affect Association in Inactive People: An Ambulatory Assessment Study in Daily Life

Haaren¹,

Loeffler²,

Haertel³

et al. 2013

Front. Psychol.

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Acute and regular exercise as well as physical activity (PA) is related to well-being and positive affect. Recent studies have shown that even daily, unstructured physical activities increase positive affect. However, the attempt to achieve adherence to PA or exercise in inactive people through public health interventions has often been unsuccessful. Most studies analyzing the activity-affect association in daily life, did not report participants’ habitual activity behavior. Thus, samples included active and inactive people, but they did not necessarily exhibit the same affective reactions to PA in daily life. Therefore the present study investigated whether the association between PA and subsequent affective state in daily life can also be observed in inactive individuals. We conducted a pilot study with 29 inactive university students (mean age 21.3 ± 1.7 years) using the method of ambulatory assessment. Affect was assessed via electronic diary and PA was measured with accelerometers. Participants had to rate affect every 2 h on a six item bipolar scale reflecting the three basic mood dimensions energetic arousal, valence, and calmness. We calculated activity intensity level [mean Metabolic Equivalent (MET) value] and the amount of time spent in light activity over the last 15 min before every diary prompt and conducted within-subject correlations. We did not find significant associations between activity intensity and the three mood dimensions. Due to the high variability in within-subject correlations we conclude that not all inactive people show the same affective reactions to PA in daily life. Analyzing the PA-affect association of inactive people was difficult due to little variance and distribution of the assessed variables. Interactive assessment and randomized controlled trials might help solving these problems. Future studies should examine characteristics of affective responses of inactive people to PA in daily life. General assumptions considering the relation between affect and PA might not be suitable for this target group.

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Section: Methodsmentioning

confidence: 99%

Characteristics of the Activity-Affect Association in Inactive People: An Ambulatory Assessment Study in Daily Life

Haaren¹,

Loeffler²,

Haertel³

et al. 2013

Front. Psychol.

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“…Different sensors such as barometer were used in these studies [34,1,36,33]. Voleno et al [34] developed an EE system based on barometric pressure sensor and triaxial accelerometer.…”

Section: Introductionmentioning

confidence: 99%

“…They showed that barometric pressure sensor helps to improve the performance. Anastasopoulou et al [1] presented a method to recognize the physical activity and predict the EE based on barometry and accelerometry. Not only the recognition step, but also the EE estimation step achieved high performance.…”

Section: Introductionmentioning

confidence: 99%

Automatic energy expenditure measurement for health science

Çatal

Akbulut

2018

Computer Methods and Programs in Biomedicine

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“…After entering the participant's physical characteristics (age, height, mass and sex) the software can estimate EE. The output sampling rate was set to 1 sec [21].…”

Section: Methodsmentioning

confidence: 99%

Energy Expenditure During Incline Walking – Benefits of Integrating a Barometer into Activity Monitors

Manuel¹,

Αναστασοπούλου²,

Stefan³

et al. 2018

AJSS

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Objective: This study aimed to compare different methods to determine energy expenditure (EE) on incline walking. Approach: The methods tested were a conventional triaxial accelerometer (GT3X), a versatile system (SenseWear), both utilizing single regression models, and a device equipped with a triaxial accelerometer and an air pressure sensor (move II). Twenty-five healthy participants wore the activity monitors and a portable indirect calorimeter (IC) as reference while walking up-and downhill as well as up-and downstairs. The accuracy of the three devices for estimating EE was assessed based on Pearson correlation, ICC, and Bland-Altman analysis. Main results: For GT3X and SenseWear the ICCs showed a weak correlation (between 0.42 and 0.08) and for move II a strong correlation (between 0.97 and 0.84) between the prediction of energy cost and the output from IC, respectively. Overall, the differences absolute to the IC values were 11 to 35 (12 to 30) times higher for the GT3X (SenseWear) than for the move II devices. Significance: The study showed that a device equipped with an accelerometer and an air pressure sensor had higher accuracy in predicting EE during incline walking than a conventional accelerometer or a versatile system.

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Classification of human physical activity and energy expenditure estimation by accelerometry and barometry

Cited by 27 publications

References 7 publications

Characteristics of the Activity-Affect Association in Inactive People: An Ambulatory Assessment Study in Daily Life

Characteristics of the Activity-Affect Association in Inactive People: An Ambulatory Assessment Study in Daily Life

Automatic energy expenditure measurement for health science

Energy Expenditure During Incline Walking – Benefits of Integrating a Barometer into Activity Monitors

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