Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,a...Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.展开更多
Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-in...Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.展开更多
Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artific...Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artificial intelligence(AI)have enabled the development of systems capable of mental health monitoring using multimodal data.However,existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings.This paper addresses these challenges by proposing TRANS-HEALTH,a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention(BDI)reasoning for real-time psychological distress detection.The framework utilizes a multimodal dataset containing EEG,GSR,heart rate,and activity data to predict distress while adapting to individual contexts.The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making.The novelty of the approach lies in its seamless integration of transformermodelswith BDI reasoning,providing both high accuracy and contextual relevance in real time.Performance metrics such as accuracy,precision,recall,and F1-score are employed to evaluate the system’s performance.The results show that TRANS-HEALTH outperforms existing models,achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts,with an enhanced ability to engage users,making it suitable for deployment in wearable and remote healthcare environments.展开更多
We present a multimodal ferrule-top sensor designed to perform the integrated epidetection of Optical Coberence Tomognphy(OCT)depth-profiles and micron-scale indentation by all-optical detection.By scarning a sample u...We present a multimodal ferrule-top sensor designed to perform the integrated epidetection of Optical Coberence Tomognphy(OCT)depth-profiles and micron-scale indentation by all-optical detection.By scarning a sample under the probe,we can obtain structural crosse soction images and identify a region of interest in a nonhomogencous sample.Then,with the same probe and setup,we can immediately target that area with a series of spherical indentation measurements,in which the applied load is known with aμN precision,the indentation depth with sub-/m precision and a maximum contact radius of 100 pm.Thanks to the visualization of the internal structure of the sample,we can gain a better insi ght into the observed mechanical behavior.The ability to impart a small,confined load,and perfomn OCT A scans at the same time,could lead to an altemative,high transverse resolution,Optical Coherence Elastography(OCE)sensor.展开更多
The issue of sensitivity attenuation in high-pressure region has been a persistent concern for pressure-sensitive electronic skins.In order to tackle such trade-off between sensitivity and linear range,herein,a hybrid...The issue of sensitivity attenuation in high-pressure region has been a persistent concern for pressure-sensitive electronic skins.In order to tackle such trade-off between sensitivity and linear range,herein,a hybrid piezoresistive-supercapacitive(HRSC)strategy is proposed via introducing a piezoresistive porous aerogel layer between the charge collecting electrodes and iontronic films of the pressure sensors.Surprisingly,the HRSC-induced impedance regulation and supercapacitive behavior contribute to significant mitigation in sensitivity attenuation,achieving high sensitivity across wide linear range(44.58 kPa^(−1)from 0 to 3 kPa and 23.6 kPa^(−1)from 3 to 12 kPa).The HRSC pressure sensor exhibits a low detection limit of 1 Pa,fast responsiveness(~130 ms),and excellent cycling stability,allowing to detect tiny pressure of air flow,finger bending,and human respiration.Meanwhile,the HRSC sensor exhibits exceptional perception capabilities for proximity and temperature,broadening its application scenarios in prosthetic perception and electronic skin.The proposed HRSC strategy may boost the ongoing research on structural design of high-performance and multimodal electronic sensors.展开更多
By using a graded-index multimode fiber （GI-MMF） with a relatively flat index profile and high refractive index of the fiber core, a microextrinsic fiber-optic Fabry Prot interferometric （MEFPI） strain sensor is f...By using a graded-index multimode fiber （GI-MMF） with a relatively flat index profile and high refractive index of the fiber core, a microextrinsic fiber-optic Fabry Prot interferometric （MEFPI） strain sensor is fabricated through chemical etching and fusion splicing. Higher reflectance of the microcavity is obtained due to the less-curved inner wall in the center of the fiber core after etching and higher index contrast between the GI-MMF core and air. The maximum reflection of the sensor is enhanced 12 dB than that obtained by etching of the Er- or B-doped fibers. High fringe contrast of 22 dB is obtained. The strain and temperature responses of the MEFPI sensors are investigated in this experiment. Good linearity and high sensitivity are achieved, with wavelength-strain and wavelength-temperature sensitivities of 7.82 pm/με and 5.01 pm/°C, respectively.展开更多
Real-time monitoring of multimodal vital signs including electrocardiography(ECG)and photoplethysmography(PPG)on wearable devices are attracting increasing interests.Motion artifacts,ambient light interference and sen...Real-time monitoring of multimodal vital signs including electrocardiography(ECG)and photoplethysmography(PPG)on wearable devices are attracting increasing interests.Motion artifacts,ambient light interference and sensor-skin contact variability affect signal quality significantly,demanding a multi-channel sensor interface chip with high dynamic range yet low power.A PPG/ECG interface chip is proposed for robust signal optimization.Time-division multiplexing,ambient double sampling and DC current compensation together enhance the dynamic range.Fabricated in a 0.18μm process,the chip features a 5.63-pArms direct digitized input-referred noise for PPG readout and 365-nVrms for ECG.A cross-scale dynamic range of 133 dB is achieved,providing saturation-free usage for sport wearable devices such as smart watches and rings.展开更多
基金supported by the National Natural Science Foundation of China(No.62404111)Natural Science Foundation of Jiangsu Province(No.BK20240635)+2 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.24KJB510025)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY223157 and NY223156)Opening Project of Advanced Inte-grated Circuit Package and Testing Research Center of Jiangsu Province(No.NTIKFJJ202303).
文摘Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.
文摘Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artificial intelligence(AI)have enabled the development of systems capable of mental health monitoring using multimodal data.However,existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings.This paper addresses these challenges by proposing TRANS-HEALTH,a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention(BDI)reasoning for real-time psychological distress detection.The framework utilizes a multimodal dataset containing EEG,GSR,heart rate,and activity data to predict distress while adapting to individual contexts.The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making.The novelty of the approach lies in its seamless integration of transformermodelswith BDI reasoning,providing both high accuracy and contextual relevance in real time.Performance metrics such as accuracy,precision,recall,and F1-score are employed to evaluate the system’s performance.The results show that TRANS-HEALTH outperforms existing models,achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts,with an enhanced ability to engage users,making it suitable for deployment in wearable and remote healthcare environments.
基金supported by the Dutch Technology Foundation (STW) under the OMNE program (13183)funding from LASERLABEUROPE under the EC's Seventh Framework Program (Grant agreement No.284464)the European Research Council (615170).
文摘We present a multimodal ferrule-top sensor designed to perform the integrated epidetection of Optical Coberence Tomognphy(OCT)depth-profiles and micron-scale indentation by all-optical detection.By scarning a sample under the probe,we can obtain structural crosse soction images and identify a region of interest in a nonhomogencous sample.Then,with the same probe and setup,we can immediately target that area with a series of spherical indentation measurements,in which the applied load is known with aμN precision,the indentation depth with sub-/m precision and a maximum contact radius of 100 pm.Thanks to the visualization of the internal structure of the sample,we can gain a better insi ght into the observed mechanical behavior.The ability to impart a small,confined load,and perfomn OCT A scans at the same time,could lead to an altemative,high transverse resolution,Optical Coherence Elastography(OCE)sensor.
基金the National Natural Science Foundation of China(Nos.22104021,52303075,and 22309105)Natural Science Foundation of Shandong Province(No.ZR2023QB227)+1 种基金Department of Science and Technology of Guangdong Province(No.2022A1515110014)Taishan Young Scholar Program(Nos.tsqn202306267 and tsqnz20231235).
文摘The issue of sensitivity attenuation in high-pressure region has been a persistent concern for pressure-sensitive electronic skins.In order to tackle such trade-off between sensitivity and linear range,herein,a hybrid piezoresistive-supercapacitive(HRSC)strategy is proposed via introducing a piezoresistive porous aerogel layer between the charge collecting electrodes and iontronic films of the pressure sensors.Surprisingly,the HRSC-induced impedance regulation and supercapacitive behavior contribute to significant mitigation in sensitivity attenuation,achieving high sensitivity across wide linear range(44.58 kPa^(−1)from 0 to 3 kPa and 23.6 kPa^(−1)from 3 to 12 kPa).The HRSC pressure sensor exhibits a low detection limit of 1 Pa,fast responsiveness(~130 ms),and excellent cycling stability,allowing to detect tiny pressure of air flow,finger bending,and human respiration.Meanwhile,the HRSC sensor exhibits exceptional perception capabilities for proximity and temperature,broadening its application scenarios in prosthetic perception and electronic skin.The proposed HRSC strategy may boost the ongoing research on structural design of high-performance and multimodal electronic sensors.
基金supported by the State Key Laboratory of Advanced Optical Communication Systems and Networks,China
文摘By using a graded-index multimode fiber （GI-MMF） with a relatively flat index profile and high refractive index of the fiber core, a microextrinsic fiber-optic Fabry Prot interferometric （MEFPI） strain sensor is fabricated through chemical etching and fusion splicing. Higher reflectance of the microcavity is obtained due to the less-curved inner wall in the center of the fiber core after etching and higher index contrast between the GI-MMF core and air. The maximum reflection of the sensor is enhanced 12 dB than that obtained by etching of the Er- or B-doped fibers. High fringe contrast of 22 dB is obtained. The strain and temperature responses of the MEFPI sensors are investigated in this experiment. Good linearity and high sensitivity are achieved, with wavelength-strain and wavelength-temperature sensitivities of 7.82 pm/με and 5.01 pm/°C, respectively.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB4400800the Natural Science Foundation of China under Grant 62434006。
文摘Real-time monitoring of multimodal vital signs including electrocardiography(ECG)and photoplethysmography(PPG)on wearable devices are attracting increasing interests.Motion artifacts,ambient light interference and sensor-skin contact variability affect signal quality significantly,demanding a multi-channel sensor interface chip with high dynamic range yet low power.A PPG/ECG interface chip is proposed for robust signal optimization.Time-division multiplexing,ambient double sampling and DC current compensation together enhance the dynamic range.Fabricated in a 0.18μm process,the chip features a 5.63-pArms direct digitized input-referred noise for PPG readout and 365-nVrms for ECG.A cross-scale dynamic range of 133 dB is achieved,providing saturation-free usage for sport wearable devices such as smart watches and rings.
