Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle

• DOI: 10.1249/mss.0b013e31829736d6
• Publication Date: 2013-04-19T14:21:42Z
• Authors: ANDREA MANNINI; STEPHEN S. INTILLE; MARY ROSENBERGER; ANGELO M. SABATINI; WILLIAM HASKELL
• Keywords: inertial sensing; mobile health; leave-one-subject-out validation; activity measurement; energy expenditure; Human activity classification; Adult; Male; Electronic Data Processing; Adolescent; Walking; Middle Aged; Motor Activity; Wrist; Bicycling; Young Adult; 03 medical and health sciences; 0302 clinical medicine; Accelerometry; Activities of Daily Living; Humans; Female; Ankle; Algorithms; Aged

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.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.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%.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.