A 137-mu W 1.78-mm(2) 30-Frames/s Real-Time Gesture Recognition SoC for Smart Devices

Abstract

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 ultralow-power wearable devices. This article presents an accurate area-efficient and low-power real-time gesture recognition system for smart wearable devices. The proposed work utilizes an accurate 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. In addition, 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. Thus, the proposed system accomplishes accurate real-time gesture recognition for eight motion hand gestures with an average recognition accuracy of 90.6% and latency of 4.228 ms. Measurement results of a test chip fabricated in 65-nm CMOS demonstrate that the proposed system consumes 137.0 mu W at 30 frames/s while occupying only 1.78 mm(2), which achieves the lowest power and smallest area among the recently reported gesture recognition systems.