Young people using powered wheelchairs have limited access to engaging leisure activities. We address this issue through a two-stage project; 1) the participatory development of a set of wheelchair-controlled, movement-based games (with 9 participants at a school that provides education for young people who have special needs) and 2) three case studies (4 participants) exploring player perspectives on a set of three wheelchair-controlled casual games. Our results show that movement-based playful experiences are engaging for young people using powered wheelchairs. However, the participatory design process and case studies also reveal challenges for game accessibility regarding the integration of movement in games, diversity of abilities among young people using powered wheelchairs, and the representation of disability in games. In our paper, we explore how to address those challenges in the development of accessible, empowering movement-based games, which is crucial to the wider participation of young people using powered wheelchairs in play.

A large majority of population spend substantial amount of time each day in sedentary positions, which make the chairs potentially rich source of information and insight about people daily activities and behavior patterns. These information, which often gets under-utilized, can reveal valuable knowledge about the users's wellness level and quality of life. Considering this we have designed Care-Chair, a simple and cost effective smart sensing system with just four pressure sensors on the backrest of a chair, equipped with intelligent data analytics. Our proposed Care-Chair system has been able to classify among large number of 19 fine-grained and complex user sedentary activities. To best of our knowledge this is the first work to detect user functional activities and user emotion based activities (in addition to static and movement based sedentary activities and postures) with just 4 pressure sensors on chair and sensor data analytics. The performance is validated with 5 users combined dataset (thus considering user specific variations) with 86% overall accuracy. Our system is also validated to achieve precise measurement in user breathing rate during relatively static sedentary postures.