title: Classification accuracies of physical activities using smartphone motion sensors.
abstract: BACKGROUND
  Over the past few years, the world has witnessed an unprecedented growth in smartphone use. With sensors such as accelerometers and gyroscopes on board, smartphones have the potential to enhance our understanding of health behavior, in particular physical activity or the lack thereof. However, reliable and valid activity measurement using only a smartphone in situ has not been realized.
  
  
  OBJECTIVE
  To examine the validity of the iPod Touch (Apple, Inc.) and particularly to understand the value of using gyroscopes for classifying types of physical activity, with the goal of creating a measurement and feedback system that easily integrates into individuals' daily living.
  
  
  METHODS
  We collected accelerometer and gyroscope data for 16 participants on 13 activities with an iPod Touch, a device that has essentially the same sensors and computing platform as an iPhone. The 13 activities were sitting, walking, jogging, and going upstairs and downstairs at different paces. We extracted time and frequency features, including mean and variance of acceleration and gyroscope on each axis, vector magnitude of acceleration, and fast Fourier transform magnitude for each axis of acceleration. Different classifiers were compared using the Waikato Environment for Knowledge Analysis (WEKA) toolkit, including C4.5 (J48) decision tree, multilayer perception, naive Bayes, logistic, k-nearest neighbor (kNN), and meta-algorithms such as boosting and bagging. The 10-fold cross-validation protocol was used.
  
  
  RESULTS
  Overall, the kNN classifier achieved the best accuracies: 52.3%-79.4% for up and down stair walking, 91.7% for jogging, 90.1%-94.1% for walking on a level ground, and 100% for sitting. A 2-second sliding window size with a 1-second overlap worked the best. Adding gyroscope measurements proved to be more beneficial than relying solely on accelerometer readings for all activities (with improvement ranging from 3.1% to 13.4%).
  
  
  CONCLUSIONS
  Common categories of physical activity and sedentary behavior (walking, jogging, and sitting) can be recognized with high accuracies using both the accelerometer and gyroscope onboard the iPod touch or iPhone. This suggests the potential of developing just-in-time classification and feedback tools on smartphones.
authors.affiliation: Center For Wireless & Population Health Systems, Department of Family & Preventive Medicine, University of California, San Diego, La Jolla, CA 92093-0811, United States.
authors.forename: Wanmin
authors.initials: W
authors.lastname: Wu
authors.forename: Sanjoy
authors.initials: S
authors.lastname: Dasgupta
authors.forename: Ernesto E
authors.initials: EE
authors.lastname: Ramirez
authors.forename: Carlyn
authors.initials: C
authors.lastname: Peterson
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Norman
mesh_terms: D000328:Adult; D040421:Cell Phone; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008838:Microcomputers; D008875:Middle Aged; D009068:Movement