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Power wheelchair users often use and carry multiple mobile computing devices. Many power wheelchair users have some upper body motor impairment that can make using these devices difficult. We believe that mobile device accessibility could be improved through designs that take into account users' functional abilities and take advantage of available space around the wheelchair itself. In this paper we present findings from multiple design sessions and interviews with 13 power wheelchair users and 30 clinicians, exploring the placement and form factor possibilities for input and output on a power wheelchair. We found that many power wheelchair users could benefit from chairable technology that is designed to work within the workspace of the wheelchair, whether worn on the body or mounted on he wheelchair frame. We present participants' preferences for chairable input and output devices, and identify possible design configurations for wearable and chairable devices.

If applications were able to detect a user's expertise, then software could automatically adapt to better match exper-tise. Detecting expertise is difficult because a user's skill changes as the user interacts with an application and differs across applications. This means that expertise must be sensed dynamically, continuously, and unobtrusively so as not to burden the user. We present an approach to this prob-lem that can operate without a task model based on low-level mouse and menu data which can typically be sensed across applications at the operating systems level. We have implemented and trained a classifier that can detect "nov-ice" or "skilled" use of an image editing program, the GNU Image Manipulation Program (GIMP), at 91% accuracy, and tested it against real use. In particular, we developed and tested a prototype application that gives the user dy-namic application information that differs depending on her performance.