A 137-μW Area-Efficient Real-Time Gesture Recognition System for Smart Wearable Devices

Gesture recognition has increasingly become one of the most popular human-machine interaction techniques for smart devices. Existing gesture recognition systems suffer from either excessive power consumption or large size, limiting their applications for ultra-low power IoT and wearable devices. This paper presents an accurate, area-efficient, and ultra-low power real-time gesture recognition system for smart wearable devices. The proposed work utilizes a peak-based gesture classification engine with less memory and a low-resolution and low-power on-chip image sensor for achieving high area efficiency and low power. The feature extraction architecture removes fixed-pattern noises from the low-power on-chip image sensor for accuracy improvement and employs parallelism for recognition speed enhancement. The proposed system requires only 3.2 KB on-chip memory for processing 32×32 pixel data. Measurement results of a test chip fabricated in 65nm CMOS demonstrate that the proposed system consumes 137.0 μW at 0.8 V and 30fps while occupying only 1.78mm
²
, which achieves the lowest power and smallest area among existing gesture recognition systems.