Nanomaterial-assisted wearable glucose biosensors for noninvasive real-time monitoring:Pioneering point-of-care and beyond

This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable.

A wearable real-time telemonitoring electrocardiogram device compared with traditional Holter monitoring

Arrhythmias are very common in the healthy populations as well as patients with cardiovascular diseases.Among them,atrial fibrillation(AF)and malignant ventricular arrhythmias are usually associated with some clinical events.Early diagnosis of arrhythmias,particularly AF and ventricular arrhythmias,is very important for the treatment and prognosis of patients.Holter is a gold standard commonly recommended for noninvasive detection of paroxysmal arrhythmia.However,it has some shortcomings such as fixed detection timings,delayed report and inability of remote real-time detection.To deal with such problems,we designed and applied a new wearable 72-hour triple-lead H3-electrocardiogram(ECG)device with a remote cloud-based ECG platform and an expertsupporting system.In this study,31 patients were recruited and 24-hour synchronous ECG data by H3-ECG and Holter were recorded.In the H3-ECG group,ECG signals were transmitted using remote real-time modes,and confirmed reports were made by doctors in the remote expert-supporting system,while the traditional modes and detection systems were used in the Holter group.The results showed no significant differences between the two groups in 24-hour total heart rate(HR),averaged HR,maximum HR,minimum HR,premature atrial complexes(PACs)and premature ventricular complexes(PVCs)(P>0.05).The sensitivity and specificity of capture and remote automatic cardiac events detection of PACs,PVCs,and AF by H3-ECG were 93%and 99%,98%and 99%,94%and 98%,respectively.Therefore,the long-term limb triple-lead H3-ECG device can be utilized for domiciliary ECG self-monitoring and remote management of patients with common arrhythmia under medical supervision.

Semi-implantable device based on multiplexed microfilament electrode cluster for continuous monitoring of physiological ions

Modern medicine is increasingly interested in advanced sensors to detect and analyze biochemical indicators.Ion sensors based on potentiometric methods are a promising platform for monitoring physiological ions in biological subjects.Current semi-implantable devices are mainly based on single-parameter detection.Miniaturized semi-implantable electrodes for multiparameter sensing have more restrictions on the electrode size due to biocompatibility considerations,but reducing the electrode surface area could potentially limit electrode sensitivity.This study developed a semi-implantable device system comprising a multiplexed microfilament electrode cluster(MMEC)and a printed circuit board for real-time monitoring of intra-tissue K^(+),Ca^(2+),and Na^(+)concentrations.The electrode surface area was less important for the potentiometric sensing mechanism,suggesting the feasibility of using a tiny fiber-like electrode for potentiometric sensing.The MMEC device exhibited a broad linear response(K^(+):2–32 mmol/L;Ca^(2+):0.5–4 mmol/L;Na^(+):10–160 mmol/L),high sensitivity(about 20–45 mV/decade),temporal stability(>2weeks),and good selectivity(>80%)for the above ions.In vitro detection and in vivo subcutaneous and brain experiment results showed that the MMEC system exhibits good multi-ion monitoring performance in several complex environments.This work provides a platform for the continuous real-time monitoring of ion fluctuations in different situations and has implications for developing smart sensors to monitor human health.

Wearable Healthcare and Continuous Vital Sign Monitoring with IoT Integration

Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care,which is essential for independent living,especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common.Recent advances in the Internet of Things(IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition,gaining significant attention in personalized healthcare.This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring.Relevant papers were extracted and analyzed using a systematic numerical review method,covering various aspects such as sports monitoring,disease detection,patient monitoring,and medical diagnosis.The review highlights the transformative impact of IoTenabled wearable devices in healthcare,facilitating real-time monitoring of vital signs,including blood pressure,temperature,oxygen levels,and heart rate.Results from the reviewed papers demonstrate high accuracy and efficiency in predicting health conditions,improving sports performance,enhancing patient care,and diagnosing diseases.The integration of IoT in wearable healthcare devices enables remote patient monitoring,personalized care,and efficient data transmission,ultimately transcending traditional boundaries of healthcare and leading to better patient outcomes.

A fully integrated electronic fabric-enabled multimodal flexible sensors for real-time wireless pressure-humidity-temperature monitoring

Real-time physiological information monitoring can predict and prevent disease, or improve treatment by early diagnosis. A comprehensive and continuous monitoring of human health requires highly integrated wearable and comfortable sensing devices. To address this need, we propose a low-cost electronic fabric-enabled multifunctional flexible sensing integration platform that includes a flexible pressure sensor for monitoring postural pressure, a humidity sensor for monitoring the humidity of the skin surface, and a flexible temperature sensor for visualizing the ambient temperature around the human body. Thanks to the unique rough surface texture, hierarchical structure, and robust electromechanical features of the MXene-modified nonwoven fabrics, the flexible pressure sensor can achieve a monitoring sensitivity of 1529.1 kPa~(-1) and a pressure range of 150 kPa, which meets the demand for human pressure detection. In addition, the unique porous structure of the fabric and the stacked multilayer structure of MXene enable the humidity sensor to exhibit extremely high monitoring sensitivity, even through clothing, and still be able to detect the humidity on the skin surface.Temperature sensors based on screen-printed thermochromic liquid crystals enable visual monitoring in the range of 0℃–65℃. Through further integration with flexible printed circuit board circuits, we demonstrate a proof-of-concept device that enables real-time monitoring of human physiological information such as physical pressure, humidity, and ambient temperature environment, suggesting that the device provides an excellent platform for the development of commercially viable wearable healthcare monitors.

Highly sensitive, wearable piezoresistive methylcellulose/chitosan@MXene aerogel sensor array for real-time monitoring ofphysiological signals of pilots

Sensors with enhanced biocompatibility, highsensitivity, and stable output have gained prominence with therapid advancement of piezoresistive sensor technologies.However, conventional piezoresistive sensors struggle to balance sensitivity and output stability. Here, we fabricated synergistic methylcellulose/chitosan MXene-based (MC/CS@MXene) aerogels through physical blending and freeze-drying,emulating the hollow bamboo structure. The aerogels formsynergistic interconnection via electrostatic adsorption andhydrogen bonding, endowing the aerogel-assembled resistivesensor with high sensitivity (2.90 kPa^(−1)), exceptional mechanical stability (8000 compression cycles at 10 kPa), andrapid response and recovery times (119 and 91 ms, respectively). A piezoresistive sensor array based on MC/CS/@MXene shows considerable potential for human–computerinteractions and wearable technologies. Furthermore, thesensor array can monitor real-time physiological signals ofcivil aviation pilots.

Breathable Electronic Skins for Daily Physiological Signal Monitoring

With the aging of society and the increase in people’s concern for personal health,long-term physiological signal monitoring in daily life is in demand.In recent years,electronic skin(e-skin)for daily health monitoring applications has achieved rapid development due to its advantages in high-quality physiological signals monitoring and suitability for system integrations.Among them,the breathable e-skin has developed rapidly in recent years because it adapts to the long-term and high-comfort wear requirements of monitoring physiological signals in daily life.In this review,the recent achievements of breathable e-skins for daily physiological monitoring are systematically introduced and discussed.By dividing them into breathable e-skin electrodes,breathable e-skin sensors,and breathable e-skin systems,we sort out their design ideas,manufacturing processes,performances,and applications and show their advantages in long-term physiological signal monitoring in daily life.In addition,the development directions and challenges of the breathable e-skin are discussed and prospected.

一种穿戴式心肺监测系统的设计及其试验研究

目的:设计一种穿戴式心肺监测系统,并通过人体初步试验验证该系统的性能。方法:该系统由数据采集器、穿戴胸衣以及信息管理平台组成。其中,数据采集器以EFM32GG330单片机作为主控单元(microcontroller unit,MCU),其内部主要包括呼吸调制模块、心电调制模块、体位调制模块、无线通信模块(含蓝牙模块和Wi-Fi模块)、存储模块、电源管理模块等。穿戴胸衣采用开衫式结构设计,内侧设计有心电传感器和呼吸运动传感器。信息管理平台采用客户端/服务器(Client/Server,C/S)架构,通过Java/JavaScript实现软件编程。通过人体初步试验,将该系统与医院常规使用的迈瑞IPM10多参数监护仪进行对比,验证该系统监测人体心率及呼吸率的有效性。结果:该系统可长时间连续监测人体的心率和呼吸率,且监测结果与迈瑞IPM10多参数监护仪的监测结果具有很好的一致性。结论:该系统可用于训练或运动中的心肺指标医学监测,为健康管理提供精准的生理信息。

可穿戴生理信息监测的自供电系统研究

设计与制作了一种应用于可穿戴生理信息监测的自供电系统,利用压电-光电复合能量收集装置采集人体运动时足底的机械能和室内光能。复合能量收集装置以运动鞋为载体,设计了多种结构压电能量收集器固定在鞋底,光电能量收集器粘接在鞋面,收集到的能量存储在超级电容中,同时设计了一套能够定时、按需供电的能量管理控制电路,控制生理信息监测系统每隔20 min上电一次,并且测量结束即刻断电。经测试,光电能量收集装置在室内从早上7点至晚上7点的平均输出功率达到590μW,压电能量收集装置在1.2 m/s的匀速步行状态驱动下的平均输出功率达540μW以上,复合能量输出功率能够稳定在1.124 mW以上,即20 min产电1.349 J。对系统各单元进行数据采集测试以及功耗测试,得出系统一个工作周期耗能1.04 J。从能量收支平衡来看,利用运动机械能与光能复合供电的生理信息采集系统可实现自我维持定时工作,具有一定的应用参考价值。

蚕丝基柔性可穿戴传感器在人体健康监测中的研究进展

传感器是智能可穿戴器件的关键组成部分之一,通过信号转换机制能够全天候、实时连续采集人体生命体征和生理信息,被广泛应用于实时健康监测、医疗诊断及科学化运动训练领域。蚕丝材料具有易再生成型、富含功能化基团、结构可控及生物相容等特点,被广泛研究并应用在柔性可穿戴传感器中。本文结合蚕丝的组成、结构与性能特点,聚焦蚕丝基可穿戴传感器的构筑及在健康监测领域的应用,围绕其构筑方法及应用领域,系统地阐述了蚕丝基柔性可穿戴传感器的研究进展,并对蚕丝基可穿戴传感器面临的挑战和未来的发展趋势进行了探讨。

基于可穿戴系统的体位/体动信息量化及其与心力衰竭患者NYHA分级关联分析

背景日常活动量减少和运动功能受限是心力衰竭患者的特征性表现之一,体位/体动信息与心衰患者疾病严重程度和预后密切相关。通过可穿戴生理监测系统量化体位/体动信息或可作为一种潜在的心衰病情严重程度定量评价手段,其与纽约心脏病协会(New York Heart Association,NYHA)心功能分级的关系需进一步研究。目的探讨心衰患者体位/体动信息定量分析结果与NYHA分级的相关性。方法纳入2021年5月—2022年11月在四川大学华西医院心内科住院的心衰患者,通过可穿戴生理监测系统采集患者入院当天和出院前1 d各24 h的连续生理监测数据,同步收集临床数据。通过对可穿戴生理监测系统内的三轴加速传感器信息进行处理分析,计算卧床时间、活动时间、步数、睡眠翻身次数4个体位/体动指标。基于患者入院时NYHA分级、入院和出院情况、出院时NYHA分级改善与否进行分组,分析体位/体动指标与NYHA分级的关联性。结果纳入心衰患者69例,平均年龄(60.90±14.24)岁,其中男性40例,NYHAⅡ、Ⅲ、Ⅳ级的患者分别有9例、24例、36例。随着NYHA分级的升高,心衰患者全天的卧床时间占比逐渐增多,而全天的活动时间占比、平均每小时步数逐渐降低,以上3个指标在NYHAⅡ、Ⅲ、Ⅳ级间均有统计学差异(P均<0.05);其中卧床时间占比(r_(s)=0.319,P=0.008)与NYHA分级呈正相关,活动时间占比(r_(s)=-0.312,P=0.009)、平均每小时步数(r_(s)=-0.309,P=0.010)与NYHA分级存在负相关。出院时的卧床时间占比显著低于入院时(96.25%vs 97.63%,P=0.026);出院时的活动时间占比显著高于入院时(3.32%vs 1.78%,P<0.001);出院时的平均每小时步数显著高于入院时(97.17步/h vs 35.58步/h,P<0.001);其中出院时NYHA改善组患者的体位/体动指标变化趋势同上,未改善组仅出院时的平均每小时步数显著高于入院时,NYHA改善组的出入院平均每小时步数变化值显著高于未改善组(71.21步/h vs 21.31步/h,P=0.003)。结论可穿戴生理监测系统能够对心衰患者的体位/体动信息进行客观长程的监测,心衰患者的卧床时间与NYHA分级呈正相关关系;活动时间、步数与NYHA分级呈负相关关系,这些体位/体动指标或可作为心衰患者疾病严重程度分级和状态监测评估的有用指标,未来可进一步延伸到对患者的居家和长程监测。

智能臂带式多生理参数监测设备的研制

目的:为更全面地获取人体的基本体征参数,研制一种可实时采集心电、血氧饱和度、血压、体温、体位/体动信号的智能臂带式多生理参数监测设备。方法:该监测设备硬件主要由主控模块、传感器采集模块、蓝牙通信模块、数据显示存储模块、外围控制模块组成,其中主控模块采用STM32L452单片机,传感器采集模块包括心电和体温模块、加速度模块、血氧模块和压力模块,外围控制模块主要包括按键控制模块和电源管理模块;软件包括下位机软件和上位机软件,其中下位机软件借助嵌入式开发平台Keil MDK以C语言进行编写,上位机软件基于LabVIEW平台进行开发。为验证该监测设备的功能和性能,对该监测设备无线传输和数据存储的稳定性及各生理参数和信号的采集功能进行测试。结果:测试结果表明,该监测设备可满足对传输可靠性和低功耗的需求,便携性和可穿戴性较好;体温、心率、血氧饱和度、收缩压、舒张压测量误差绝对值的最大值分别为0.08℃、2次/min、1%、4 mmHg(1 mmHg=133.32 Pa)、5 mmHg,具有较高的准确率。结论:该设备体积小、质量轻、功耗低,可实现对多种生理信号的监测,且能满足监测设备可穿戴性的需求。

基于生理监控的智能可穿戴服装研究

文章综述国内外智能可穿戴服装在生理监测方面的研究和进展,对实例中所应用的智能可穿戴技术进行总结。详细解释可穿戴2.0智能服装系统的构成及工作原理,并提出其通用架构。对智能服装系统的模块构成进行剖析,对其所搭载的Arduino等关键技术进行阐述。该研究旨在为相关人员在智能可穿戴服装的可持续数据监测开发与生理监测、生活环境辅助、物联网交互等方面的设计提供参考。

Comparison of feature learning methods for non-invasive interstitial glucose prediction using wearable sensors in healthy cohorts:a pilot study

Background Alterations in glucose metabolism,especially the postprandial glucose response(PPGR),are cru-cial contributors to metabolic dysfunction,which underlies the pathogenesis of metabolic syndrome.Personalized low-glycemic diets have shown promise in reducing postprandial glucose spikes.However,current methods such as invasive continuous glucose monitoring(CGM)or multi-omics data integration to assess PPGR have limita-tions,including cost and invasiveness that hinder the widespread adoption of these methods in primary disease prevention.Our aim was to assess machine learning algorithms for predicting individual PPGR using non-invasive wearable devices,thereby,circumventing the limitations associated with the existing approaches.By identifying the most accurate model,we sought to provide a more accessible and efficient method for managing glucose metabolic dysfunction.Methods This data-driven analysis used the experimental dataset from the SENSE(“Systemische Ernährungsmedizin”)study.Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for 10 days.Blood volume pulse,electrodermal activity,heart rate,skin temperature,and the corre-sponding CGM values were measured.Subsequently,four data-driven deep learning(DL)models-convolutional neural network,lightweight transformer,long short-term memory with attention,and Bi-directional LSTM(BiL-STM)were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs.Results The proposed BiLSTM achieved the best interstitial glucose prediction performance,with an average root mean squared error of 13.42 mg/dL,an average mean absolute percentage error of 0.12,and only 3.01%values falling within area D in Clarke error grid analysis,incorporating the leave-one-out cross-validation strategy for a five-minute prediction horizon.Conclusion The findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches.Furthermore,it could emphasize the promising prospects of combining DL with wearable technologies to predict glucose levels in healthy individuals.

Multimodal fiber antenna for proximity and stress sensing

Fiber sensors are commonly used to detect environmental,physiological,optical,chemical,and biological factors.Thermally drawn fibers offer numerous advantages over other commercial products,including enhanced sensitivity,accuracy,improved functionality,and ease of manufacturing.Multimaterial,multifunctional fibers encapsulate essential internal structures within a microscale fiber,unlike macroscale sensors requiring separate electronic components.The compact size of fiber sensors enables seamless integration into existing systems,providing the desired functionality.We present a multimodal fiber antenna monitoring,in real time,both the local deformation of the fiber and environmental changes caused by foreign objects in proximity to the fiber.Time domain reflectometry propagates an electromagnetic wave through the fiber,allowing precise determination of spatial changes along the fiber with exceptional resolution and sensitivity.Local changes in impedance reflect fiber deformation,whereas proximity is detected through alterations in the evanescent field surrounding the fiber.The fiber antenna operates as a waveguide to detect local deformation through the antisymmetric mode and environmental changes through the symmetric mode.This multifunctionality broadens its application areas from biomedical engineering to cyber-physical interfacing.In antisymmetric mode,the device can sense local changes in pressure,and,potentially,temperature,pH,and other physiological conditions.In symmetric mode,it can be used in touch screens,environmental detection for security,cyber-physical interfacing,and human-robot interactions.

柔性可穿戴生理监测设备的研究与应用现状

阐述了国内外柔性可穿戴生理监测设备研究现状。介绍了该设备在疾病监测、情绪监测、康复训练监测、运动训练监测、特殊环境作业安全监测与防护、灾害与战争伤员搜救、老年人与儿童监护等方面的应用现状,分析了该设备的性能特点。针对该设备存在的材料成本高、数据质量不理想、行业标准缺失、实际应用效能不明确等问题提出了相关对策,对柔性生理传感技术研发及应用前景进行了展望。

一种可穿戴多生理信号采集系统

为了长期连续监测心血管慢性疾病患者的关键生理参数,设计了一种高性能小型化可穿戴多生理信号采集系统,从硬件、固件和软件3个方面实现了人体心冲击信号(ballistocardiogram,BCG)、心电信号(electrocardiogram,ECG)和脉搏血氧饱和度的长时间连续监测、联合采集和移动端APP上的实时显示。通过提取3种信号相应的特征参数并进行联合分析,可以反映重要心脏生理指标。对该系统的穿戴舒适性、功耗和信号强度进行实验测试,结果表明:该采集系统舒适性强,能精确采集人体心冲击信号、心电信号和脉搏血氧信号,最大工作电流仅为12.512 mA,可以应用于家庭中对于心血管慢性疾病患者的长期监护。

可穿戴随行监护系统在军事作业医学中的应用

目的探索穿戴式生理参数监测系统在军事作业医学中的应用价值。方法在某新兵训练基地搭建基于随行监护系统的中央监测系统,并分别于2018年和2019年采集新兵入伍3个月军事训练中30人(210人次)、50人(600人次)的连续(≥24 h)生理数据,包括两晚的夜间睡眠、日间训练、业余活动等,通过信号质量算法和人工判读筛选可用信号。随机选取2019年新兵40例,按3 km跑的考核成绩进行排序,将排名前20名和后20名的新兵分为成绩高组与成绩低组,比较两组夜间睡眠和运动指标的差异,探索训练成绩与生理指标的关系。结果采集数据的平均时长为(36.2±12.0)h,信号质量评估结果显示,心电和呼吸信号质量好和中等所占的比例均大于80%,系统能够有效采集新兵军事训练过程中心电、呼吸、体位/体动等数据,实现实时监测和作业效能评估等功能。监测数据显示,成绩高组相比于成绩低组,睡眠分期中的深睡比例更多(16.24%±5.62%vs 12.19%±4.95%,P=0.042),运动后心率恢复差值(1 min、2 min)下降更快[(37.89±5.53)/min vs(31.97±6.20)/min,P=0.003]、[(60.89±6.61)/min vs(49.30±4.76)/min,P=0.000]。结论可穿戴随行监护系统能够在军事作业环境下有效采集新兵的生理数据,该系统通过连续生理监测与分析能够捕捉到更多新兵身体状态变化的信息,为评估军事作业效能提供了新的技术手段。

面向远程医疗的生理监护系统

本文介绍了一种面向远程医疗的生理监护系统,系统结构主要包括生理参数监护终端、通讯网络和数据服务网站。生理监护终端包括心电、心率、血压、血氧饱和度、体温和呼吸等生理测量模块,每个模块都通过临床验证。通讯网络有基于CDMA/GPRS、基于互联网及基于机顶盒等方式。该系统先后进行了产品演示、远程通讯测试,结果显示该系统可以作为一个有效的远程医疗平台。

智能头带式高原生理信息监测系统

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