Motor Impairments

💡 We conduct a systematic literature review on wearable interactions for users with motor impairments and report results from a meta-analysis of 57 scientific articles identified in the ACM DL and IEEE Xplore databases. Our findings show limited research conducted on accessible wearable interactions (e.g., just four papers addressing smartwatch input), a disproportionate interest for hand gestures compared to other input modalities for wearable devices, and low numbers of participants with motor impairments involved in user studies about wearable interactions (a median of 6.0 and average of 8.2 participants per study). We compile an inventory of 92 finger, hand, head, shoulder, eye gaze, and foot gesture commands for smartwatches, smartglasses, headsets, earsets, fitness trackers, data gloves, and armband wearable devices extracted from the scientific literature that we surveyed. Based on our findings, we propose four directions for future research on accessible wearable interactions for users with motor impairments.

💡 WearSkill is our web-based application for personalized input with wearables that acts as a middleware to control connected devices that support Wi-Fi and WebSocket communications, e.g., control the smart lighting system with the smartwatch. WearSkill primarily addresses users with upper-body motor impairments, for whom it enables personalized recommendations for input modalities (touch, motion, and voice) and three categories of wearables (smartwatches, rings, and glasses) based on users' self-reported motor impairments.