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 sp...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.展开更多
In a recent study,Prof.Rui Min and collaborators published their paper in the journal of Opto-Electronic Science that is entitled"Smart photonic wristband for pulse wave monitoring".The paper introduces nove...In a recent study,Prof.Rui Min and collaborators published their paper in the journal of Opto-Electronic Science that is entitled"Smart photonic wristband for pulse wave monitoring".The paper introduces novel realization of a sensor that us-es a polymer optical multi-mode fiber to sense pulse wave bio-signal from a wrist by analyzing the specklegram mea-sured at the output of the fiber.Applying machine learning techniques over the pulse wave signal allowed medical diag-nostics and recognizing different gestures with accuracy rate of 95%.展开更多
Flexible wearable electronics have garnered substantial attention as promising alternatives to traditional rigid metallic conductors,particularly for personal health monitoring and bioinspired skin applications.Howeve...Flexible wearable electronics have garnered substantial attention as promising alternatives to traditional rigid metallic conductors,particularly for personal health monitoring and bioinspired skin applications.However,these technologies face persistent challenges,including low sensitivity,limited mechanical strength,and difficulty in capturing weak signals.To address these issues,this study developed a hierarchical sandwich-structured piezoresistive foam sensor using phase inversion and NaCl sacrificial templating methods.The sensor exhibits an exceptional sensitivity of up to 83.4 kPa⁻1 under an ultralow detection pressure of 2.43 Pa.By optimizing the foam porosity,its mechanical performance was significantly enhanced,reaching a tensile fracture elongation of 257.3%at 73.42%porosity.The hierarchical sandwich structure provided mechanical buffering and layer-enhancement functionalities for dynamic responses,whereas the nanostructure further refined signal acquisition and interference resistance.Signal analysis using discrete wavelet transform(DWT)and continuous wavelet transform(CWT)enables multiscale and multifrequency characterization of arterial resistance signals under varying applied pressures.These findings underscore the sensor’s ability to capture weak signals and analyze complex pulse dynamics.This advancement paves the way for the extensive application of multifunctional sensors in smart devices and health care.This method offers a robust scientific basis for further understanding and quantifying arterial pulse characteristics.展开更多
基金financial supports from National Key R&D Program of China (2022YFE0140400)National Natural Science Foundation of China(62003046, 62111530238)+7 种基金Guangdong Basic and Applied Basic Research Foundation (2021A1515011997)The Supplemental Funds for Major Scientific Research Projects of Beijing Normal University,Zhuhai(ZHPT2023007)Special project in key field of Guangdong Provincial Department of Education (2021ZDZX1050)The Innovation Team Project of Guangdong Provincial Department of Education (2021KCXTD014)Fundação para a Ciência e a Tecnologia (FCT) through the 2021.00667CEECIND (iAqua project)PTDC/EEI-EEE/0415/2021 (DigiAqua project)The project i3N,UIDB/50025/2020 n&UIDP/50025/2020, financed by national funds through the FCT/MEC
文摘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.
文摘In a recent study,Prof.Rui Min and collaborators published their paper in the journal of Opto-Electronic Science that is entitled"Smart photonic wristband for pulse wave monitoring".The paper introduces novel realization of a sensor that us-es a polymer optical multi-mode fiber to sense pulse wave bio-signal from a wrist by analyzing the specklegram mea-sured at the output of the fiber.Applying machine learning techniques over the pulse wave signal allowed medical diag-nostics and recognizing different gestures with accuracy rate of 95%.
文摘Flexible wearable electronics have garnered substantial attention as promising alternatives to traditional rigid metallic conductors,particularly for personal health monitoring and bioinspired skin applications.However,these technologies face persistent challenges,including low sensitivity,limited mechanical strength,and difficulty in capturing weak signals.To address these issues,this study developed a hierarchical sandwich-structured piezoresistive foam sensor using phase inversion and NaCl sacrificial templating methods.The sensor exhibits an exceptional sensitivity of up to 83.4 kPa⁻1 under an ultralow detection pressure of 2.43 Pa.By optimizing the foam porosity,its mechanical performance was significantly enhanced,reaching a tensile fracture elongation of 257.3%at 73.42%porosity.The hierarchical sandwich structure provided mechanical buffering and layer-enhancement functionalities for dynamic responses,whereas the nanostructure further refined signal acquisition and interference resistance.Signal analysis using discrete wavelet transform(DWT)and continuous wavelet transform(CWT)enables multiscale and multifrequency characterization of arterial resistance signals under varying applied pressures.These findings underscore the sensor’s ability to capture weak signals and analyze complex pulse dynamics.This advancement paves the way for the extensive application of multifunctional sensors in smart devices and health care.This method offers a robust scientific basis for further understanding and quantifying arterial pulse characteristics.
