A feasibility study on smartphone accelerometer-based recognition of household activities and influence of smartphone position. (2nd October 2017)

• Record Type: Journal Article
• Authors: Della Mea, Vincenzo; Quattrin, Omar; Parpinel, Maria
• Is Part Of: Informatics for health & social care. Volume 42:Number 4(2017)
• Page Start: 321
• Page End: 334
• Publication Date: 2017-10-02
• Subjects: Activity recognition -- accelerometer -- household activities -- mobile applications -- physical activity -- smartphone; Medicine -- Information services -- Periodicals; Medical informatics -- Periodicals; Medicine -- Data processing -- Periodicals; 025.0661
• DOI: 10.1080/17538157.2016.1255214
• Languages: English
• ISSNs: 1753-8157

Abstract

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.