Wearable multi-sensor gesture recognition for paralysis patients

Quadriplegia and paraplegia are disabilities that result from injuries to the spinal cord and neuromuscular disorders such as cerebral palsy. Patients suffering from quadriplegia have varied levels of impaired motor movements, hence, performing quotidian tasks like controlling home appliances is challenging for quadriplegics. The use of hand and eye gestures to perform these tasks is a plausible remedy, but available solutions often assume considerable limb movement, are not fit for long-term use, and may not be applicable to quadriplegics with varied range of motor impairments. To address this problem, we present the design, implementation, and evaluation of a multi-sensor gesture recognition system that uses comfortable and low power wearable sensors. We have designed an EOG-based headband using textile electrodes and a glove that uses flex sensors and an accelerometer to detect eye and hand gestures. The gestures are used to control appliances remotely in a home setting and we show that they have good accuracy, latency, and energy consumption characteristics.