The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2023 Oct 8;23(19):8315. doi: 10.3390/s23198315.
Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human-machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system's volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.
由于使用方便、运动干扰小、成本低等优点，可穿戴系统在人机交互（HRI）领域得到了广泛应用。然而，复杂临床康复场景中的 HRI 对可穿戴传感器系统提出了更高的要求，这引起了许多研究人员的兴趣。然而，传统的可穿戴系统存在集成度低、测量数据类型有限、精度低等问题，与 HRI 的实际需求存在差距。本文将从四个方面介绍当前 HRI 可穿戴系统的最新进展。首先，介绍了系统集成当前研究的突破，包括处理芯片和柔性传感模块，以减小系统体积并延长电池寿命。之后，本文回顾了电化学测量可穿戴系统的最新进展，该系统可以从汗液等生物流体中提取单一或多种生物标志物。此外，介绍了非侵入式可穿戴系统的临床应用，解决了传统临床侵入式测量设备带来的疼痛和不适问题。最后，展示了当前可穿戴系统与最新机器学习方法相结合的进展，实现了更高的精度和对无法直接测量的数据的间接获取。从呈现的证据来看，我们相信 HRI 中可穿戴系统的发展趋势是朝着高集成度、多电化学测量数据以及临床和智能方向发展。
