Inviz: Low-power personalized gesture recognition using wearable textile capacitive sensor arrays

Abstract

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.

Finding Common Ground

“…Flexible and stretchable interfaces enable new types of interaction [60,77] and support ubiquitous deployments, e.g., in fabric and clothing [8,30,152,185] or directly on the human body [98,103,217]. …”

“…More specialized deployments recognize proximity-based gestures in cars [17,46] or on clothing [185]. Proximity-sensing surfaces deployed on or near displays [86,119,167,168,190] enable smartphones to show context-dependent menus before the touch contact [86], launch apps based on grasp [25], or detect 3D gestures above the surface [119].…”

“…These systems work by sensing changes in the proximity of the skin to a wearable during muscle flexion. Capacitive textile arrays have also been embedded into clothing to sense gestures and movement detection in patients with limited mobility [185] and other health-related applications [200].…”

“…Researchers have since used this approach to implement health applications [8,30,185] and ubiquitous touch interfaces [43,76,92,143,152]. Sensors have thereby been placed on pants [172,185], belts [43,196], shoes [145], gloves [104,209], and jackets [152]. However, manufacturing techniques and long-term deployments (i.e., surviving wash cycles) remain open research areas for such systems [152].…”

Evaluating touchless capacitive gesture recognition as an assistive device for upper extremity mobility impairment

“…These sensors can be applied to many medical and rehabilitative applications, including physiological monitoring, 1 telemedicine, 2 rehabilitation compliance, 3 and assistive input. 4 The prevalence of such systems has increased to the point that wearable sensors and systems have become a major fixture in medical rehabilitative and assistive devices and are poised to change the way that medical practitioners interact with patients. However, wearable sensors face issues with maintaining patient compliance.…”