Machine learning for activity recognition : hip versus wrist data

Trost, Stewart G., Zheng, Yonglei, & Wong, Weng-Keen (2014) Machine learning for activity recognition : hip versus wrist data. Physiological Measurement, 35(11), pp. 2183-2189.

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Problem addressed

Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip.


52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1).


Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%).

Potential Impact

Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Impact and interest:

13 citations in Scopus
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12 citations in Web of Science®

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ID Code: 78778
Item Type: Journal Article
Refereed: Yes
DOI: 10.1088/0967-3334/35/11/2183
ISSN: 1361-6579
Subjects: Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > HUMAN MOVEMENT AND SPORTS SCIENCE (110600)
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Exercise & Nutrition Sciences
Copyright Owner: Copyright 2014 Institute of Physics and Engineering in Medicine
Deposited On: 19 Nov 2014 23:03
Last Modified: 20 Nov 2014 21:52

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