Individuals with high spinal cord injuries are unable to operate a keyboard and mouse with their hands. In this experiment, we compared two systems using surface electromyography (sEMG) recorded from facial muscles to control an onscreen keyboard to type five-letter words. Both systems used five sEMG sensors to capture muscle activity during five distinct facial gestures that were mapped to five cursor commands: move left, move right, move up, move down, and “click”. One system used a discrete movement and feedback algorithm in which the user produced one quick facial gesture, causing a corresponding discrete movement to an adjacent letter. The other system was continuously updated and allowed the user to control the cursor’s velocity by relative activation between different sEMG channels. Participants were trained on one system for four sessions on consecutive days, followed by one crossover session on the untrained system. Information transfer rates (ITRs) were high for both systems compared to other potential input modalities, both initially and with training (Session 1: 62.1 bits/min, Session 4: 105.1 bits/min). Users of the continuous system showed significantly higher ITRs than the discrete users. Future development will focus on improvements to both systems, which may offer differential advantages for users with various motor impairments.
Many individuals with minimal movement capabilities use AAC to communicate. These individuals require both an interface with which to construct a message (e.g., a grid of letters) and an input modality with which to select targets. This study evaluated the interaction of two such systems: (a) an input modality using surface electromyography (sEMG) of spared facial musculature, and (b) an onscreen interface from which users select phonemic targets. These systems were evaluated in two experiments: (a) participants without motor impairments used the systems during a series of 8 training sessions, and (b) one individual who uses AAC used the systems for two sessions. Both the phonemic interface and the electromyographic cursor show promise for future AAC applications.
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