A 184µW Real-Time Hand-Gesture Recognition System with Hybrid Tiny Classifiers for Smart Wearable Devices

📋 论文概要

本文提出了一种用于智能可穿戴设备的实时手势识别系统，采用混合小分类器架构，实现了184µW的超低功耗。解决了现有视觉手势识别系统功耗高、精度低和灵活性差的问题。

💡 主要创新点

• 提出混合小分类器（Hybrid Tiny Classifiers）架构，结合多种轻量级分类器实现高效手势识别。
• 实现184µW超低功耗的实时手势识别，适用于电池供电的可穿戴设备。
• 通过硬件-软件协同优化，在极低功耗下保持较高识别精度和实时性。

🏷 关键词

📄 原文摘要

Nations Innovation Technologies, Singapore, Singapore 1 2 Recently, vision-based hand gesture recognition (HGR) has emerged as a natural and flexible human-computer interaction (HCI) approach. Users can control smart devices by applying hand gestures to imagers. However, prior efforts suffer from various limitations, such as excessive power consumption, low accuracy, and poor flexibility. The 3D HGR processors [1-2] suffer from extremely large power overhead due to the employment of complex image processing, for example using Convolutional Neural Networks (CNNs). The grayscale sensor-based SoC [3] consumes less power. However, its accuracy is compromised, especially when the contrast between a hand gesture and the background is low. The infrared sensor-based SoC [4] can recognize 8 dynamic gestures with high accuracy (96%). Nevertheless, the over-simplified algorithm requires hand motion with a fixed gesture type, which limits the number of recognized dynamic

👥 作者与机构

Yuncheng Lu1, Van Loi Le2, Tony Tae-Hyoung Kim1

Nanyang Technological University, Singapore, Singapore