Automated and unobtrusive measurement of physical activity in an interactive playground. Issue 129 (September 2019)

• Record Type: Journal Article
• Authors: Moreno, Alejandro; Poppe, Ronald; Gibson, Jenny L.; Heylen, Dirk
• Is Part Of: International journal of human-computer studies. Issue 129(2019)
• Page Start: 55
• Page End: 63
• Publication Date: 2019-09
• Subjects: Play -- Interactive playgrounds -- Automated behavior analysis -- Physical activity -- Exertion measurement -- Depth cameras; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2019.03.010
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: Introduction of an automated, unobtrusive measure for player exertion based on top-down depth camera video. Experimental validation of the measure and comparison with other, unobtrusive and sensor-based, exertion measures. Demonstration that player exertion levels can be manipulated by tuning a single parameter in an interactive playground. Abstract: Promoting physical activity is one of the main goals of interactive playgrounds. To validate whether this goal is met, we need to measure the amount of physical player activity. Traditional methods of measuring activity, such as observations or annotations of game sessions, require time and personnel. Others, such as heart rate monitors and accelerometers, need to be worn by the player. In this paper, we investigate whether physical activity can be measured unobtrusively by tracking players using depth cameras and applying computer vision algorithms. In a user study with 32 players, we measure the players' speed while playing a game of tag, and demonstrate that our measures correlate well with exertion measured using heart rate sensors. This makes the method an attractive alternative to either manual coding or the use of worn devices. We also compare our approach to other exertion measurement methods. Finally, we demonstrate and discuss its potential for automated, unobtrusive measurements and real-time game adaptation.