An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer.
By John W. Staudenmayer, D. Pober, S. Crouter, David Bassett, Patty S. Freedson. Journal of applied physiology, 2009
391 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Complementary and alternative medicine, Physiology
View Paper: https://www.semanticscholar.org/paper/393f75566fc90724852c4d259159c9ed1438c8dd
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In Biomedical Engineering
Citations in the corpus
- Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “Previous research efforts have used mobile sensors to recognize small sets of specific targeted activities, such as body movement, modes of transportation, gymnasium activities or eating [3], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], and se”
- BEST PRACTICES FOR USING PHYSICAL ACTIVITY MONITORS IN POPULATION-BASED RESEARCH (2012, neutral): “, raw acceleration or 1-s epochs) recently have been used to identify specific types of behavior (6,25,29,40) and energy expenditure (6,26,29,39).”
- Advanced internet of things for personalised healthcare systems: A survey (2017, methodology): “Sub-Categories Algorithms References Data-driven approaches Supervised learning ANNs [23],[90], [150] HMMs [93]–[100] SVM [34], [96], [42], [111], [123], [124] Decision tree [36] , [126]–[130] Semi-supervised learning Co-training [133], [38], [109], [110] Unsupervised learning Expectation maximum…”
- Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. (2014, neutral): “Alternatively, other methods for analyzing output from accelerometers are likely to improve the estimation of intensity from acceleration (6,25,32).”