Co-producing gesture-based AT – a case study

Abstract

Typically, people with motor impairments use Assistive Technology (AT) to maintain a reasonable level of independence in their day-to-day activities. However, when movement disorders are characterised by involuntary muscle contractions, finding a commercially available AT that is not position-dependent becomes more challenging. We present a case study where a gesture-based AT was co-produced with an individual living with dystonia over a period of five months, where the individual was an active participant in a collaborative design exercise. Initially, the team discussed with and observed the participant at her home while using her current AT. This informed the initial direction of the arising technology, which was subsequently developed through a series of in-situ sessions. Machine learning approaches were leveraged to support gesture-based interactions resilient to involuntary muscle contractions while affording real-time capabilities required by scanningbased interfaces. A summative evaluation was conducted to assess the resultant technology’s applicability in the context of use with no intervention from the research team. In this phase, a diary study approach was adopted. Results show that the emerging AT has the potential to provide an enhanced method of interaction with a higher level of independence; however, we also present challenges faced as well as insights into the participant’s lived experience.