Li Tianliang, Wang Qian'ao, Cao Zichun, Zhu Jianglin, Wang Nian, Li Run, Meng Wei, Liu Quan, Yu Shifan, Liao Xinqin, Song Aiguo, Tan Yuegang, Zhou Zude
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.
School of Information, Wuhan University of Technology, Wuhan, Hubei, 430070, China.
Adv Sci (Weinh). 2025 Jan;12(4):e2410395. doi: 10.1002/advs.202410395. Epub 2024 Dec 4.
Flexible strain monitoring of hand and joint muscle movement is recognized as an effective method for the diagnosis and rehabilitation of neurological diseases such as stroke and Parkinson's disease. However, balancing high sensitivity and large strain, improving wearing comfort, and solving the separation of diagnosis and treatment are important challenges for further building tele-healthcare systems. Herein, a hydrogel-based optical waveguide stretchable (HOWS) sensor is proposed in this paper. A double network structure is adopted to allow the HOWS sensor to exhibit high stretchability of the tensile strain up to 600% and sensitivity of 0.685 mV %. Additionally, the flexible smart bionic fabric embedding the HOWS sensor, produced through the warp and weft knitting, significantly enhances wearing comfort. A small circuit board is prepared to enable wireless signal transmission of the designed sensor, thereby improving the daily portability. A speech recognition and human-machine interaction system, based on sensor signal acquisition, is constructed, and the convolutional neural network algorithm is integrated for disease assessment. By integrating sensing, wireless transmission, and artificial intelligence (AI), a smart tele-healthcare system based on HOWS sensors is demonstrated to hold great potential for early warning and rehabilitation monitoring of neurological diseases.
对手部和关节肌肉运动进行灵活的应变监测被认为是诊断和康复中风、帕金森病等神经疾病的有效方法。然而,在高灵敏度和大应变之间取得平衡、提高佩戴舒适度以及解决诊断与治疗分离的问题,是进一步构建远程医疗系统面临的重要挑战。在此,本文提出了一种基于水凝胶的光波导可拉伸(HOWS)传感器。采用双网络结构,使HOWS传感器在拉伸应变高达600%时表现出高拉伸性,灵敏度为0.685 mV %。此外,通过经纬编织生产的嵌入HOWS传感器的柔性智能仿生织物显著提高了佩戴舒适度。制备了一个小电路板,以实现所设计传感器的无线信号传输,从而提高日常便携性。构建了基于传感器信号采集的语音识别和人机交互系统,并集成卷积神经网络算法进行疾病评估。通过集成传感、无线传输和人工智能(AI),基于HOWS传感器的智能远程医疗系统被证明在神经疾病的早期预警和康复监测方面具有巨大潜力。