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.

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This demonstration presents Inviz, a low-cost gesture recognition system that uses flexible textile-based capacitive sensors. Gestures are recognized using proximity-based movement detection using flexible capacitive sensor arrays that can be built into the environment or placed on to the body or be integrated into clothing. Inviz provides an innovative interface to home automation systems to simplify environmental control for individuals with limited-mobility resulting from paralysis, paresis, and degenerative diseases. Proximity-based sensing obviates the need for physical contact which can result in skin abrasion which is particularly deleterious to people with limited-to-no sensitivity in their extremities. A custom-designed wireless module maintains a small form factor facilitating placement based on an individual's needs. Our system leverages a hierarchical sensing technique which facilitates learning gestures based on the individual and placement of the sensors. Classification uses just-in-time embedded computational resources to provide accurate responses while maintaining a low average power consumption, in turn reducing the impact of batteries on the form factor. To illustrate the use of Inviz in a smart home environment, we demonstrate an endto-end home automation system that controls small appliances. We will interface our system with a home automation gateway to demonstrate a subset of potential applications. This interactive demonstration highlights the intuitiveness and extensibility of the Inviz prototype.