Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore.
Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
Nat Commun. 2022 Sep 9;13(1):5311. doi: 10.1038/s41467-022-33021-5.
Wearable strain sensors that detect joint/muscle strain changes become prevalent at human-machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable TiCT MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.
可穿戴应变传感器可检测关节/肌肉应变变化，在人机界面中广泛用于全身运动监测。然而，大多数可穿戴设备无法提供定制机会，使传感器特性与关节/肌肉的特定变形范围相匹配，导致性能不佳。需要进行充分的可穿戴应变传感器设计，以在不牺牲高灵敏度的情况下实现用户指定的工作窗口，并结合实时数据处理。在此，采用具有传感器内机器学习 (ML) 模型的可穿戴 TiCT MXene 传感器模块，通过无线流或边缘计算进行工作，用于全身运动分类和头像重建。通过在压阻纳米层上进行形貌设计，可穿戴应变传感器模块在满足所有关节变形范围的工作窗口内表现出超高灵敏度。通过将可穿戴传感器与 ML 芯片集成，制造出边缘传感器模块，能够在传感器内重建高精度的头像动画，模拟连续的全身运动，平均头像确定误差为 3.5 厘米，无需额外的计算设备。
