Stuart Tucker, Yin Xiaoyang, Chen Shengjian Jammy, Farley Max, McGuire Dylan Thomas, Reddy Nikhil, Thien Ryan, DiMatteo Sam, Fumeaux Christophe, Gutruf Philipp
Department of Biomedical Engineering, University of Arizona, Tucson, AZ, 85721, USA.
School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, 5005, Australia.
Biosens Bioelectron. 2023 May 15;228:115218. doi: 10.1016/j.bios.2023.115218. Epub 2023 Mar 14.
Imperceptible wireless wearable devices are critical to advance digital medicine with the goal to capture clinical-grade biosignals continuously. Design of these systems is complex because of unique interdependent electromagnetic, mechanic and system level considerations that directly influence performance. Typically, approaches consider body location, related mechanical loads, and desired sensing capabilities, however, design for real world application context is not formulated. Wireless power casting eliminates user interaction and the need to recharge batteries, however, implementation is challenging because the use case influences performance. To facilitate a data-driven approach to design, we demonstrate a method for personalized, context-aware antenna, rectifier and wireless electronics design that considers human behavioral patterns and physiology to optimize electromagnetic and mechanical features for best performance across an average day of the target user group. Implementation of these methods result in devices that enable continuous recording of high-fidelity biosignals over weeks without the need for human interaction.
难以察觉的无线可穿戴设备对于推动数字医学发展至关重要，其目标是持续捕捉临床级生物信号。由于存在直接影响性能的独特的相互依存的电磁、机械和系统层面的考量因素，这些系统的设计很复杂。通常，设计方法会考虑身体位置、相关机械负荷和所需传感能力，然而，针对实际应用场景的设计并未形成。无线电力传输消除了用户交互以及电池充电的需求，然而，由于用例影响性能，其实施具有挑战性。为了促进数据驱动的设计方法，我们展示了一种用于个性化、情境感知天线、整流器和无线电子设备设计的方法，该方法考虑人类行为模式和生理特征，以优化电磁和机械特性，从而在目标用户群体的平均一天中实现最佳性能。实施这些方法会产生能够在数周内持续记录高保真生物信号且无需人工干预的设备。
