title: Machine learning for activity recognition: hip versus wrist data.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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).
  
  
  RESULTS
  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.
authors.affiliation: Institute of Health and Biomedical Innovation, School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove QLD 4059, Australia.
authors.forename: Stewart G
authors.initials: SG
authors.lastname: Trost
authors.forename: Yonglei
authors.initials: Y
authors.lastname: Zheng
authors.forename: Weng-Keen
authors.initials: WK
authors.lastname: Wong
mesh_terms: D000054:Acceleration; D061725:Accelerometry / Q000379:methods*; D000293:Adolescent; D001185:Artificial Intelligence; D002648:Child; D005260:Female; D006615:Hip; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity; D010363:Pattern Recognition, Automated / Q000379:methods*; D014953:Wrist; D055815:Young Adult