A Low-Cost Wearable Hand Gesture Detecting System Based on IMU and Convolutional Neural Network
A Low-Cost Wearable Hand Gesture Detecting System Based on IMU and Convolutional Neural Network
复制标题
基于IMU和卷积神经网络的低成本可穿戴手势检测系统
DOI：
10.1109/embc46164.2021.9630686
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Hai
中科院分区：
文献类型：
--
作者：
Pufan Xu;Zihui Liu;Fei Li;Hai
提出了一种基于分布式多节点惯性测量单元(IMU)和中心节点微控制器的低成本可穿戴手势检测系统。它可以获取手部运动信息，并将数据无线传输到远程处理终端。为了更全面地理解手部运动学，提出了一种在终端上使用卷积神经网络(CNN)模型对手势进行识别和分类，并使用改进的Denavit-Hartenberg符号来获取手指的空间位置。该实验不仅完成了多种手势识别，还捕获并显示了单指的姿态和姿势。该样机可用于手康复评估、人机交互等多种场合。
In this paper, a low-cost wearable hand gesture detecting system based on distributed multi-node inertial measurement units (IMUs) and central node microcontroller is presented. It can obtain hand kinematic information and transmit data to the remote processing terminal wirelessly. To have a comprehensive understanding of hand kinematics, a convolutional neural network (CNN) model on the terminal is proposed to recognize and classify gestures and the modified Denavit-Hartenberg notation is used to acquire finger spatial locations. The experiment has not only completed a variety of gesture recognitions, but also captured and displayed the orientation and posture of a single finger. The prototype can be used in various occasions such as hand rehabilitation evaluation and human-computer interaction.