Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle
TLDR
A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set, and could be implemented in real time on mobile devices with only 4-s latency.Abstract:
AB Purpose: Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities. Methods: Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. Results: With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. Conclusions: A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.read more
Citations
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Sedentary Behavior and Cardiovascular Morbidity and Mortality: A Science Advisory From the American Heart Association.
Deborah Rohm Young,Sofiya Alhassan,Sarah M. Camhi,Jane F. Ferguson,Peter T. Katzmarzyk,Cora E. Lewis,Neville Owen,Cynthia K. Perry,Juned Siddique,Celina M. Yong +9 more
TL;DR: This American Heart Association science advisory reviews the current evidence on sedentary behavior in terms of assessment methods, population prevalence, determinants, associations with cardiovascular disease incidence and mortality, potential underlying mechanisms, and interventions.
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Window Size Impact in Human Activity Recognition
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TL;DR: A review of a range of objective measures of PA based on EE or oxygen uptake including DLW, activity energy expenditure, physical activity level, and metabolic equivalent to provide information on the utility and limitations of these measures.
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Reliability and validity of ten consumer activity trackers
Thea J. M. Kooiman,Manon L. Dontje,Siska R. Sprenger,Wim P. Krijnen,Cees P. van der Schans,Martijn de Groot +5 more
TL;DR: The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low, and test-retest analysis revealed high reliability for most trackers except for the Omron, Moves app (ICC).
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The current state of physical activity assessment tools.
TL;DR: Physical activity (PA) is a behavior that involves bodily movements resulting in energy expenditure and the goal is to identify the frequency, duration, intensity, and types of behaviors performed during a period of time.
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