Applied Mechatronics and Biomedical Engineering Research (AMBER) Group, School of Mechanical, Materials, Mechatronic and Biomedical Engineering, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia.
Sensors (Basel). 2024 Sep 22;24(18):6124. doi: 10.3390/s24186124.
Wearable technologies represent a significant advancement in facilitating communication between humans and machines. Powered by artificial intelligence (AI), human gestures detected by wearable sensors can provide people with seamless interaction with physical, digital, and mixed environments. In this paper, the foundations of a gesture-recognition framework for the teleoperation of infrared consumer electronics are established. This framework is based on force myography data of the upper forearm, acquired from a prototype novel soft pressure-based force myography (pFMG) armband. Here, the sub-processes of the framework are detailed, including the acquisition of infrared and force myography data; pre-processing; feature construction/selection; classifier selection; post-processing; and interfacing/actuation. The gesture recognition system is evaluated using 12 subjects' force myography data obtained whilst performing five classes of gestures. Our results demonstrate an inter-session and inter-trial gesture average recognition accuracy of approximately 92.2% and 88.9%, respectively. The gesture recognition framework was successfully able to teleoperate several infrared consumer electronics as a wearable, safe and affordable human-machine interface system. The contribution of this study centres around proposing and demonstrating a user-centred design methodology to allow direct human-machine interaction and interface for applications where humans and devices are in the same loop or coexist, as typified between users and infrared-communicating devices in this study.
可穿戴技术代表了在促进人机通信方面的重大进展。由人工智能（AI）驱动，可穿戴传感器检测到的人体手势可以为人们提供与物理、数字和混合环境的无缝交互。本文为红外消费电子产品的遥操作建立了手势识别框架的基础。该框架基于从新型软压式力肌电（pFMG）臂带获得的上臂力肌电数据。在此，详细介绍了框架的子流程，包括红外和力肌电数据的获取、预处理、特征构建/选择、分类器选择、后处理以及接口/致动。使用 12 名受试者在执行五类手势时获得的力肌电数据对手势识别系统进行了评估。我们的结果表明，在会话间和试验间，手势的平均识别准确率分别约为 92.2%和 88.9%。该手势识别框架成功地能够作为一种可穿戴、安全且经济实惠的人机接口系统，对几种红外消费电子产品进行遥操作。本研究的贡献集中在提出和展示一种以用户为中心的设计方法，以允许直接的人机交互和接口，适用于人类和设备在同一回路或共存的应用，如本研究中用户与红外通信设备之间的典型应用。
