Gao Xiaoxiang, Chen Xiangjun, Lin Muyang, Yue Wentong, Hu Hongjie, Qin Siyu, Zhang Fangao, Lou Zhiyuan, Yin Lu, Huang Hao, Zhou Sai, Bian Yizhou, Yang Xinyi, Zhu Yangzhi, Mu Jing, Wang Xinyu, Park Geonho, Lu Chengchangfeng, Wang Ruotao, Wu Ray S, Wang Joseph, Li Jinghong, Xu Sheng
Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA, USA.
These authors contributed equally: Xiaoxiang Gao, Xiangjun Chen, Muyang Lin, Wentong Yue.
Nat Electron. 2024 Nov;7(11):1035-1046. doi: 10.1038/s41928-024-01271-4. Epub 2024 Oct 31.
Wearable electromyography devices can detect muscular activity for health monitoring and body motion tracking, but this approach is limited by weak and stochastic signals with a low spatial resolution. Alternatively, echomyography can detect muscle movement using ultrasound waves, but typically relies on complex transducer arrays, which are bulky, have high power consumption and can limit user mobility. Here we report a fully integrated wearable echomyography system that consists of a customized single transducer, a wireless circuit for data processing and an on-board battery for power. The system can be attached to the skin and provides accurate long-term wireless monitoring of muscles. To illustrate its capabilities, we use this system to detect the activity of the diaphragm, which allows the recognition of different breathing modes. We also develop a deep learning algorithm to correlate the single-transducer radio-frequency data from forearm muscles with hand gestures to accurately and continuously track 13 hand joints with a mean error of only 7.9°.
可穿戴肌电图设备能够检测肌肉活动以进行健康监测和身体运动追踪，但这种方法受到微弱且随机的信号以及低空间分辨率的限制。另外，超声肌电图可以利用超声波检测肌肉运动，但通常依赖于复杂的换能器阵列，这些阵列体积庞大、功耗高且会限制用户的活动能力。在此，我们报告一种完全集成的可穿戴超声肌电图系统，它由一个定制的单换能器、用于数据处理的无线电路以及用于供电的板载电池组成。该系统可以附着在皮肤上，并提供对肌肉的准确长期无线监测。为了展示其功能，我们使用这个系统来检测膈肌的活动，从而识别不同的呼吸模式。我们还开发了一种深度学习算法，将来自前臂肌肉的单换能器射频数据与手势相关联，以精确且连续地追踪13个手部关节，平均误差仅为7.9°。
