title: A feasibility study on smartphone accelerometer-based recognition of household activities and influence of smartphone position.
abstract: BACKGROUND
  Obesity and physical inactivity are the most important risk factors for chronic diseases. The present study aimed at (i) developing and testing a method for classifying household activities based on a smartphone accelerometer; (ii) evaluating the influence of smartphone position; and (iii) evaluating the acceptability of wearing a smartphone for activity recognition.
  
  
  METHODS
  An Android application was developed to record accelerometer data and calculate descriptive features on 5-second time blocks, then classified with nine algorithms. Household activities were: sitting, working at the computer, walking, ironing, sweeping the floor, going down stairs with a shopping bag, walking while carrying a large box, and climbing stairs with a shopping bag. Ten volunteers carried out the activities for three times, each one with a smartphone in a different position (pocket, arm, and wrist). Users were then asked to answer a questionnaire.
  
  
  RESULTS
  1440 time blocks were collected. Three algorithms demonstrated an accuracy greater than 80% for all smartphone positions. While for some subjects the smartphone was uncomfortable, it seems that it did not really affect activity.
  
  
  CONCLUSIONS
  Smartphones can be used to recognize household activities. A further development is to measure metabolic equivalent tasks starting from accelerometer data only.
authors.affiliation: a Department of Mathematics, Computer Science and Physics , University of Udine , Italy.
authors.forename: Vincenzo
authors.identifier: http://orcid.org/0000-0002-0144-3802
authors.initials: V
authors.lastname: Della Mea
authors.affiliation: a Department of Mathematics, Computer Science and Physics , University of Udine , Italy.
authors.forename: Omar
authors.initials: O
authors.lastname: Quattrin
authors.affiliation: b Department of Medical and Biological Sciences , University of Udine , Udine , Italy.
authors.forename: Maria
authors.initials: M
authors.lastname: Parpinel
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000203:Activities of Daily Living; D000328:Adult; D000465:Algorithms; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009043:Motor Activity / Q000502:physiology*; D000068997:Smartphone / Q000706:statistics & numerical data*
keywords: Activity recognition; accelerometer; household activities; mobile applications; physical activity; smartphone