Recognising household activities with the smartphone: paper finally published.
A feasibility study on smartphone accelerometer-based recognition of household activities and influence of smartphone position.
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.
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.
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.
Smartphones can be used to recognize household activities. A further development is to measure metabolic equivalent tasks starting from accelerometer data only.