Home automation and environmental control is a key ingredient of smart homes. While systems for home automation and control exist, there are few systems that interact with individuals suffering from paralysis, paresis, weakness and limited range of motion that are common sequels resulting from severe injuries such as stroke, brain injury, spinal cord injury and many chronic (guillian barre syndrome) and degenerative (amyotrophic lateral sclerosis) conditions. To address this problem, we present the design, implementation, and evaluation of Inviz, a low-cost gesture recognition system for paralysis patients that uses flexible textile-based capacitive sensor arrays for movement detection. The design of Inviz presents two novel research contributions. First, the system uses flexible textile-based capacitive arrays as proximity sensors that are minimally obtrusive and can be built into clothing for gesture and movement detection in patients with limited body motion. The proximity sensing obviates the need for touch-based gesture recognition that can cause skin abrasion in paralysis patients, and the array of capacitive sensors help provide better spatial resolution and noise cancellation. Second, Inviz uses a low-power hierarchical signal processing algorithm that breaks down computation into multiple low and high power tiers. The tiered approach provides maximal vigilance at minimal energy consumption. We have designed and implemented a fully functional prototype of Inviz and we evaluate it in the context of an end-to-end home automation system and show that it achieves high accuracy while maintaining low latency and low energy consumption.
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.
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