A 184-μW Error-Tolerant Real-Time Hand Gesture Recognition System With Hybrid Tiny Classifiers Utilizing Edge CNN Y

Abstract

This article proposes a low-power real-time hand gesture recognition (HGR) system with high recognition accuracy for smart edge devices. This design balances accuracy and power consumption by utilizing computation-efficient hybrid classifiers assisted with a majority voting scheme. By combining the recognition results of consecutive frames, the HGR system shows improved immunity to misclassification. In addition, the compressed input data before high-level processing dramatically reduce the on-chip memory and computational load. The pro- posed Edge-convolutional neural network (CNN) core with inter- actable processing engines reduces the memory accessing and the feature register toggling rate by 27% and 50%, respectively. The sequence analyzer based on majority voting improves the static and dynamic gesture recognition accuracy by ∼7% and ∼8% only with 9.4% hardware overhead. The test chip was fabricated in 65-nm CMOS technology, occupying the area of 1 × 1.5 mm 2. It consumes the lowest power of 184 µWa t2 5M H za n d0 . 6V . The proposed HGR system can recognize six static gestures and 24 dynamic hand gestures with an average accuracy of 87.25%– 95% and 85.4%–94.9%, respectively.