Abstract
Children with cerebral palsy must perform daily exercise which is a tedious and energy consuming task. Exergames can make this routine more engaging, which can increase the compliance of the patient. This research explores the feasibility of an exergame device called the Squid Monster. The device is the result of a research through design process, and it is designed to be played on smartphones in the home environment. It operates on the smartphone's integrated sensors and two external squeeze sensors, making it accessible and cost-effective. We conceptualize how the design can be supported using a machine learning adaptive difficulty system, aiming to increase flow and therapeutic adherence of the device. Ultimately, guidelines are provided to designers for future work in this field.
| Original language | English |
|---|---|
| Title of host publication | CHI PLAY '22 |
| Subtitle of host publication | extended abstracts of the 2022 annual symposium on computer-human interaction in play |
| Editors | Kathrin Gerling, Jo Iacovides, Reiner Malaka |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery |
| Pages | 183-188 |
| DOIs | |
| Publication status | Published - Nov 2022 |
| Event | ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play (CHI Play) - Maritim Hotel & Congress Centrum, Bremen, Germany Duration: 2 Nov 2022 → 5 Nov 2022 https://chiplay.acm.org/2022/ |
Conference
| Conference | ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play (CHI Play) |
|---|---|
| Abbreviated title | CHI Play '22 |
| Country/Territory | Germany |
| City | Bremen |
| Period | 2/11/22 → 5/11/22 |
| Internet address |
Funding
The authors would like to thank Ben Schouten, Tamara Pinos Cis-neros, Kayleigh Schoorl and Albert Salah for their contributions to the project and many insightful discussions. Thanks to Roessingh Rehabilitation Centre and Dolium Health centre for their contribution in shaping the design. This study was funded by The Dutch Research Council (NWO) grant #652.001.005 and the Human-AI fund (Utrecht the Netherlands), and conducted in collaboration with Eindhoven University of Technology, Amsterdam University of Applied Sciences, University of Twente and Utrecht University.
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