An analytic approach to decipher usable gestures for quadriplegic users

With the advent of new gaming technologies, hand gestures are gaining popularity as an effective communication channel for human computer interaction (HCI). This is particularly relevant for patients recovering from mobility-related injuries or debilitating conditions who use gesture-based gaming for rehabilitation therapy. Unfortunately, most gesture-based gaming systems are designed for able-bodied users and are difficult and costly to adapt to people with upper extremity mobility impairments. While interface customization is an active area of work in assistive technologies (AT), there is no existing formal and analytical grounded methodology to adapt gesture-based control systems for quadriplegics. The goal of this work is to solve this hurdle by developing a mathematical framework to project the patterns of gestural behavior designed for existing gesture systems to those exhibited by quadriplegic subjects due to spinal cord injury (SCI). A key component of our framework relied on Laban movement analysis (LMA) theory, and consisted of four steps: acquiring and preprocessing gesture trajectories, extracting feature vectors, training transform functions, and generating constrained gestures. The feasibility of this framework was validated through user-based experimental paradigms and subject validation. It was found that 100% of gestures selected by subjects with high-level SCIs came from the constrained gesture set. Even for the low-level quadriplegic subject, the alternative gestures were preferred.