(Peer-Reviewed) Smart photonic wristband for pulse wave monitoring
Renfei Kuang 邝任飞 ¹ ³, Zhuo Wang 王茁 ¹, Lin Ma 马林 ², Heng Wang 王珩 ², Qingming Chen 陈庆明 ³, Arnaldo Leal Junior ⁴, Santosh Kumar ⁵, Xiaoli Li 李小俚 ¹, Carlos Marques ⁶, Rui Min 闵锐 ¹
¹ Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai 519087, China
中国 珠海 北京师范大学 认知神经科学与学习国家重点实验室 认知神经工效研究中心
² College of Science, Shenyang Aerospace University, Shenyang 110136, China
中国 沈阳 沈阳航空航天大学理学院
³ School of Microelectronics Science and Technology, Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems, Sun Yat-Sen University, Zhuhai 519082, China
中国 珠海 中山大学微电子科学与技术学院 广东省光电信息处理芯片与系统重点实验室
⁴ Graduate Program in Electrical Engineering, Federal University of Espírito Santo, Fernando Ferrari Avenue, Vitoria 29075-910, Brazil
⁵ Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522302, India
⁶ CICECO-Aveiro Institute of Materials, Physics Department, University of Aveiro, Aveiro 3810-193, Portugal
Opto-Electronic Science, 2024-08-20
Abstract
Real-time acquisition of human pulse signals in daily life is clinically important for cardiovascular disease monitoring and diagnosis. Here, we propose a smart photonic wristband for pulse signal monitoring based on speckle pattern analysis with a polymer optical fiber (POF) integrated into a sports wristband.
Several different speckle pattern processing algorithms and POFs with different core diameters were evaluated. The results indicated that the smart photonic wristband had a high signal-to-noise ratio and low latency, with the measurement error controlled at approximately 3.7%. This optimized pulse signal could be used for further medical diagnosis and was capable of objectively monitoring subtle pulse signal changes, such as the pulse waveform at different positions of Cunkou and pulse waveforms before and after exercise.
With the assistance of artificial intelligence (AI), functions such as gesture recognition have been realized through the established prediction model by processing pulse signals, in which the recognition accuracy reaches 95%. Our AI-assisted smart photonic wristband has potential applications for clinical treatment of cardiovascular diseases and home monitoring, paving the way for medical Internet of Things-enabled smart systems.
Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu, Jingming Zhang, Xingwei Chen, Feihong Yu, Deyu Xu, Shuaiqi Liu, Weihao Lin, Xiaobing Shi, Zixing Huang, Xiongji Yang, Qingchang Hu, Liyang Shao
Opto-Electronic Advances
2025-11-25
