The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during the...The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.展开更多
Background:Many healthcare workers were infected by coronavirus disease 2019(COVID-19)early in the epidemic posing a big challenge for epidemic control.Hence,this study aims to explore perceived infection routes,influ...Background:Many healthcare workers were infected by coronavirus disease 2019(COVID-19)early in the epidemic posing a big challenge for epidemic control.Hence,this study aims to explore perceived infection routes,influencing factors,psychosocial changes,and management procedures for COVID-19 infected healthcare workers.Methods:This is a cross-sectional,single hospital-based study.We recruited all 105 confirmed COVID-19 healthcare workers in the Zhongnan Hospital of Wuhan University from February 15 to 29,2020.All participants completed a validated questionnaire.Electronic consent was obtained from all participants.Perceived causes of infection,infection prevention,control knowledge and behaviour,psychological changes,symptoms and treatment were measured.Results:Finally,103 professional staff with COVID-19 finished the questionnaire and was included(response rate:98.1%).Of them,87 cases(84.5%)thought they were infected in working environment in hospital,one(1.0%)thought their infection was due to the laboratory environment,and 5(4.9%)thought they were infected in daily life or community environment.Swab of throat collection and physical examination were the procedures perceived as most likely causing their infection by nurses and doctors respectively.Forty-three(41.8%)thought their infection was related to protective equipment,utilization of common equipment(masks and gloves).The top three first symptoms displayed before diagnosis were fever(41.8%),lethargy(33.0%)and muscle aches(30.1%).After diagnosis,88.3%staff experienced psychological stress or emotional changes during their isolation period,only 11.7%had almost no emotional changes.Arbidol(Umifenovir;an anti-influza drug;69.2%)was the drug most commonly used to target infection in mild and moderate symptoms.Conclusion:The main perceived mode of transmission was not maintaining protection when working at a close distance and having intimate contact with infected cases.Positive psychological intervention is necessary.展开更多
Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which pro...Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.展开更多
Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and th...Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members.The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S.military healthcare records from fiscal year 2018(FY2018).Methods:Medical diagnosis information and deployability profiles(e-Profiles)were queried for all active-duty U.S.Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018.Nondeployability was predicted from medical reasons for having an e-Profile(categorized as sleep,behavioral health,musculoskeletal,cardiometabolic,injury,or accident)using binomial logistic regression.Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability.Results:Out of 582,031 soldiers,48.4%(n=281,738)had a sleep-related diagnosis in their healthcare records,9.7%(n=56,247)of soldiers had e-Profiles,and 1.9%(n=10,885)had a sleep e-Profile.Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident(p OR(prevalence odds ratio)=4.7,95%CI 2.63–8.39,P≤0.001)or work/duty-related injury(p OR=1.6,95%CI 1.32–1.94,P≤0.001).The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile(p OR=4.25,95%CI 3.75–4.81,P≤0.001)or work/dutyrelated injury(p OR=2.62,95%CI 1.63–4.21,P≤0.001).Conclusion:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018,but their sleep problems are largely not profiled as limitations to medical readiness.Musculoskeletal issues and physical injury predict nondeployability,and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues.Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.展开更多
BACKGROUND: Stroke is a time-sensitive neurological disease and a life-threatening medical condition. Providing timely management for stroke patients is a crucial issue in healthcare settings. The primary objective of...BACKGROUND: Stroke is a time-sensitive neurological disease and a life-threatening medical condition. Providing timely management for stroke patients is a crucial issue in healthcare settings. The primary objective of this study is to evaluate the effectiveness of an evidence-based educational program on healthcare providers'(HCPs) overall knowledge of stroke.METHODS: A randomized block design with post-test only was used. A total of 189 HCPs(physicians, registered nurses, and paramedics) involved with treating stroke patients in the emergency were recruited. Participants were randomly assigned to either the intervention or waiting list control group. A one-session, stroke educational program was offered to the HCPs followed by a post-test designed to assess knowledge about stroke. RESULTS: A significant main effect on the profession type was found, with physicians having higher mean scores of stroke knowledge compared with nurses and paramedics(F [2, 183]=48.55, P<0.001). The implemented educational program had a positive effect on increasing the level of stroke knowledge among HCPs(F [1, 183]=43.31, P<0.001). The utilization of any evidence-based assessment tools for patients with suspected stroke was denied by 36% of the total sample.CONCLUSIONS: The implemented intervention can increase HCP's knowledge regarding stroke. Stroke education should be considered as one of the essential requirements for professional development for all HCPs in the emergency.展开更多
BACKGROUND: Ensuring about the patient's safety is the f irst vital step in improving the quality of care and the emergency ward is known as a high-risk area in treatment health care. The present study was conduct...BACKGROUND: Ensuring about the patient's safety is the f irst vital step in improving the quality of care and the emergency ward is known as a high-risk area in treatment health care. The present study was conducted to evaluate the selected risk processes of emergency surgery department of a treatment-educational Qaem center in Mashhad by using analysis method of the conditions and failure effects in health care.METHODS: In this study, in combination(qualitative action research and quantitative crosssectional), failure modes and effects of 5 high-risk procedures of the emergency surgery department were identified and analyzed according to Healthcare Failure Mode and Effects Analysis(HFMEA). To classify the failure modes from the "nursing errors in clinical management model(NECM)", the classification of the effective causes of error from "Eindhoven model" and determination of the strategies to improve from the "theory of solving problem by an inventive method" were used. To analyze the quantitative data of descriptive statistics(total points) and to analyze the qualitative data, content analysis and agreement of comments of the members were used.RESULTS: In 5 selected processes by "voting method using rating", 23 steps, 61 sub-processes and 217 potential failure modes were identifi ed by HFMEA. 25(11.5%) failure modes as the high risk errors were detected and transferred to the decision tree. The most and the least failure modes were placed in the categories of care errors(54.7%) and knowledge and skill(9.5%), respectively. Also, 29.4% of preventive measures were in the category of human resource management strategy.CONCLUSION: "Revision and re-engineering of processes", "continuous monitoring of the works", "preparation and revision of operating procedures and policies", "developing the criteria for evaluating the performance of the personnel", "designing a suitable educational content for needs of employee", "training patients", "reducing the workload and power shortage", "improving team communication" and "preventive management of equipment's" were on the agenda as the guidelines.展开更多
Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both we...Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges.Here, a flexible triboelectric patch(FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porousreinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa^(-1) under a pressure range of 0–5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa^(-1). The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare(Io H) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring.展开更多
This paper reviews recent advances in radar sensor design for low-power healthcare,indoor real-time positioning and other applications of IoT.Various radar front-end architectures and digital processing methods are pr...This paper reviews recent advances in radar sensor design for low-power healthcare,indoor real-time positioning and other applications of IoT.Various radar front-end architectures and digital processing methods are proposed to improve the detection performance including detection accuracy,detection range and power consumption.While many of the reported designs were prototypes for concept verification,several integrated radar systems have been demonstrated with reliable measured results with demo systems.A performance comparison of latest radar chip designs has been provided to show their features of different architectures.With great development of IoT,short-range low-power radar sensors for healthcare and indoor positioning applications will attract more and more research interests in the near future.展开更多
BACKGROUND:Healthcare professionals are expected to have knowledge of current basic and advanced cardiac life support(BLS/ACLS) guidelines to revive unresponsive patients.METHODS:Across-sectional study was conducted t...BACKGROUND:Healthcare professionals are expected to have knowledge of current basic and advanced cardiac life support(BLS/ACLS) guidelines to revive unresponsive patients.METHODS:Across-sectional study was conducted to evaluate the current practices and knowledge of BLS/ACLS principles among healthcare professionals of North-Kerala using pretested self-administered structured questionnaire.Answers were validated in accordance with American Heart Association's BLS/ACLS teaching manual and the results were analysed.RESULTS:Among 461 healthcare professionals,141(30.6%) were practicing physicians,268(58.1%) were nurses and 52(11.3%) supporting staff.The maximum achievable score was 20(BLS15/ACLS 5).The mean score amongst all healthcare professionals was 8.9±4.7.The mean score among physicians,nurses and support staff were 8.6±3.4,9±3.6 and 9±3.3 respectively.The majority of healthcare professionals scored <50%(237,51.4%);204(44.3%) scored 51%-80%and 20(4.34%)scored >80%.Mean scores decreased with age,male sex and across occupation.Nurses who underwent BLS/ACLS training previously had significantly higher mean scores(10.2±3.4) than untrained(8.2±3.6,P=0.001).Physicians with <5 years experience(P=0.002) and nurses in the private sector(P=0.003)had significantly higher scores.One hundred and sixty three(35.3%) healthcare professionals knew the correct airway opening manoeuvres like head tilt,chin lift and jaw thrust.Only 54(11.7%) respondents were aware that atropine is not used in ACLS for cardiac arrest resuscitation and 79(17.1%) correctly opted ventricular fibrillation and pulseless ventricular tachycardia as shockable rhythms.The majority of healthcare professionals(356,77.2%) suggested that BLS/ACLS be included in academic curriculum.CONCLUSION:Inadequate knowledge of BLS/ACLS principles amongst healthcare professionals,especially physicians,illuminate lacunae in existing training systems and merit urgent redressal.展开更多
Wise healthcare is a typical application of wireless sensor network(WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The locat...Wise healthcare is a typical application of wireless sensor network(WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The location of targets need to be determined and reported to the control center,and this leads to the localization problem. While localization in healthcare field demands high accuracy and regional adaptability, the information processing mechanism of human thinking has been introduced,which includes knowledge accumulation, knowledge fusion and knowledge expansion. Furthermore, a fuzzy decision based localization approach is proposed. Received signal strength(RSS) at references points are obtained and processed as position relationship indicators, using fuzzy set theory in the knowledge accumulation stage; after that, optimize degree of membership corresponding to each anchor nodes in different environments during knowledge fusion; the matching degree of reference points is further calculated and sorted in decision-making, and the coordinates of several points with the highest matching degree are utilized to estimate the location of unknown nodes while knowledge expansion. Simulation results show that the proposed algorithm get better accuracy performance compared to several traditional algorithms under different typical occasions.展开更多
With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized serv...With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized service is increasing because of ubiquitous deployment of various kinds of medical sensors, which cause response time increase and resource waste. Therefore, leveraging the advantage of complex event processing(CEP) in data stream processing, we propose a hierarchical fog-cloud computing CEP architecture for personalized service to accelerate response time and reduce resource waste. Firstly, we introduce the proposed architecture, which includes sensor layer, fog layer and cloud layer. Secondly, we propose a series of optimizations for the architecture, there are a partitioning and clustering approach and a communication and parallel processing policy to optimize the fog and cloud computing. Finally, we implement a prototype system based on the architecture named Fog Cep Care. Experimental result shows that Fog Cep Care is superior to the traditional IoT-based healthcare application.展开更多
The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. Thi...The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-health-care architecture. We discuss the four layers of the architecture-data collection, data transport, data storage, and data analysis-as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.展开更多
Wireless body area networks （WBANs） use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, a...Wireless body area networks （WBANs） use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, and body position to be continuously monitored.展开更多
An effective approach to improve healthcare quality is to provide Point-of-care (POC) service through a telecare system. A Body Area Network (BAN) consists of a variety of body sensors. In a telecare system the BAN ca...An effective approach to improve healthcare quality is to provide Point-of-care (POC) service through a telecare system. A Body Area Network (BAN) consists of a variety of body sensors. In a telecare system the BAN can provide a network environment that allows timely collection of information and POC service, which gives significant new meaning to a home network. Through the BAN, a home network is able to promptly and effectively collect monitoring information for a telecare system, and to preview monitored medical data in advance. Should a problem arise, it can immediately notify other family members to provide relieve to the sick.展开更多
TRSA,the global association for the linen,uniform and facility services industry,launched the Hygienically Clean Healthcare China certification in Shanghai earlier this month.The Chinese version of Hygienically Clean ...TRSA,the global association for the linen,uniform and facility services industry,launched the Hygienically Clean Healthcare China certification in Shanghai earlier this month.The Chinese version of Hygienically Clean is based on emerging Chinese healthcare laundry guidelines and TRSA’s Hygienically Clean Healthcare certifi-展开更多
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
Heart failure(HF)has been defined as global disease of pandemic proportions,since it affects around 26 million people worldwide.[1]According to a recent study,age is the most important factor influencing the prevalenc...Heart failure(HF)has been defined as global disease of pandemic proportions,since it affects around 26 million people worldwide.[1]According to a recent study,age is the most important factor influencing the prevalence of HF,as it is for most other chronic conditions.[2]This means that,with the predicted aging of the population(the proportion of the world’s population aged 60 years and over will nearly double from 2015 to 2050),[3]there will be a growth in the total burden of HF,and a rise in the number of comorbidities in HF patients.According to a recent study,almost 86%of adults with HF have two or more comorbid conditions.[4]Comorbidity,defined as the co-existence of one or more additional conditions in individuals with a specified index medical condition,[5]adds to the complexity of treating elderly patients with HF.展开更多
Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA colla...Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA collaboration with Federally Qualifed Healthcare Centers(FQHC)to serve rural residing veterans and nearly all existing collaborations involve arrangement of payment for community-based care by VHA to FQHCs.Unfortunately,there is a paucity of descriptive clinical data on existing cross-system collaborations which may help characterize these veterans and aid understanding of conditions for which they may receive treatment across systems.Such data has implications for workforce training,development,and resource allocation[2].Te objective of this report is to describe diferent clinical profles between two mutually exclusive samples:veterans engaged in FQHC only use,and VHA-enrolled veterans engaged in dual VHA and FQHC use.展开更多
In the past decade,the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare,Internet of Things,human–machine interf...In the past decade,the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare,Internet of Things,human–machine interfaces,artificial intelligence and soft robotics.Among them,flexible humidity sensors play a vital role in noncontact measurements relying on the unique property of rapid response to humidity change.This work presents an overview of recent advances in flexible humidity sensors using various active functional materials for contactless monitoring.Four categories of humidity sensors are highlighted based on resistive,capacitive,impedance-type and voltage-type working mechanisms.Furthermore,typical strategies including chemical doping,structural design and Joule heating are introduced to enhance the performance of humidity sensors.Drawing on the noncontact perception capability,human/plant healthcare management,human-machine interactions as well as integrated humidity sensor-based feedback systems are presented.The burgeoning innovations in this research field will benefit human society,especially during the COVID-19 epidemic,where cross-infection should be averted and contactless sensation is highly desired.展开更多
With the development of artificial intelligence,stiffness sensors are extensively utilized in various fields,and their integration with robots for automated palpation has gained significant attention.This study presen...With the development of artificial intelligence,stiffness sensors are extensively utilized in various fields,and their integration with robots for automated palpation has gained significant attention.This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator(Stiff-TENG)for variable inclusions in soft objects detection.The Stiff-TENG employs a stacked structure comprising an indium tin oxide film,an elastic sponge,a fluorinated ethylene propylene film with a conductive ink electrode,and two acrylic pieces with a shielding layer.Through the decoupling method,the Stiff-TENG achieves stiffness detection of objects within 1.0 s.The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed.The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures,enabling effective recognition of variable inclusions in soft object,reaching a recognition accuracy of 99.7%.Furthermore,its adaptability makes it well-suited for the detection of pathological conditions within the human body,as pathological tissues often exhibit changes in the stiffness of internal organs.This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.展开更多
文摘The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.
基金supported by the Emergency Science and Technology Project in Hubei Province(2020FCA008)
文摘Background:Many healthcare workers were infected by coronavirus disease 2019(COVID-19)early in the epidemic posing a big challenge for epidemic control.Hence,this study aims to explore perceived infection routes,influencing factors,psychosocial changes,and management procedures for COVID-19 infected healthcare workers.Methods:This is a cross-sectional,single hospital-based study.We recruited all 105 confirmed COVID-19 healthcare workers in the Zhongnan Hospital of Wuhan University from February 15 to 29,2020.All participants completed a validated questionnaire.Electronic consent was obtained from all participants.Perceived causes of infection,infection prevention,control knowledge and behaviour,psychological changes,symptoms and treatment were measured.Results:Finally,103 professional staff with COVID-19 finished the questionnaire and was included(response rate:98.1%).Of them,87 cases(84.5%)thought they were infected in working environment in hospital,one(1.0%)thought their infection was due to the laboratory environment,and 5(4.9%)thought they were infected in daily life or community environment.Swab of throat collection and physical examination were the procedures perceived as most likely causing their infection by nurses and doctors respectively.Forty-three(41.8%)thought their infection was related to protective equipment,utilization of common equipment(masks and gloves).The top three first symptoms displayed before diagnosis were fever(41.8%),lethargy(33.0%)and muscle aches(30.1%).After diagnosis,88.3%staff experienced psychological stress or emotional changes during their isolation period,only 11.7%had almost no emotional changes.Arbidol(Umifenovir;an anti-influza drug;69.2%)was the drug most commonly used to target infection in mild and moderate symptoms.Conclusion:The main perceived mode of transmission was not maintaining protection when working at a close distance and having intimate contact with infected cases.Positive psychological intervention is necessary.
基金supported in part by National Natural Science Foundation of China Grant 61202360, 61033001, 61361136003the National Basic Research Program of China Grant 2011CBA00300, 2011CBA00302
文摘Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.
基金The Department of Defense Military Operational Medicine Research Program(MOMRP)supported this study。
文摘Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members.The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S.military healthcare records from fiscal year 2018(FY2018).Methods:Medical diagnosis information and deployability profiles(e-Profiles)were queried for all active-duty U.S.Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018.Nondeployability was predicted from medical reasons for having an e-Profile(categorized as sleep,behavioral health,musculoskeletal,cardiometabolic,injury,or accident)using binomial logistic regression.Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability.Results:Out of 582,031 soldiers,48.4%(n=281,738)had a sleep-related diagnosis in their healthcare records,9.7%(n=56,247)of soldiers had e-Profiles,and 1.9%(n=10,885)had a sleep e-Profile.Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident(p OR(prevalence odds ratio)=4.7,95%CI 2.63–8.39,P≤0.001)or work/duty-related injury(p OR=1.6,95%CI 1.32–1.94,P≤0.001).The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile(p OR=4.25,95%CI 3.75–4.81,P≤0.001)or work/dutyrelated injury(p OR=2.62,95%CI 1.63–4.21,P≤0.001).Conclusion:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018,but their sleep problems are largely not profiled as limitations to medical readiness.Musculoskeletal issues and physical injury predict nondeployability,and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues.Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.
基金funded by the Directorate General of Civil Defense,Jordan。
文摘BACKGROUND: Stroke is a time-sensitive neurological disease and a life-threatening medical condition. Providing timely management for stroke patients is a crucial issue in healthcare settings. The primary objective of this study is to evaluate the effectiveness of an evidence-based educational program on healthcare providers'(HCPs) overall knowledge of stroke.METHODS: A randomized block design with post-test only was used. A total of 189 HCPs(physicians, registered nurses, and paramedics) involved with treating stroke patients in the emergency were recruited. Participants were randomly assigned to either the intervention or waiting list control group. A one-session, stroke educational program was offered to the HCPs followed by a post-test designed to assess knowledge about stroke. RESULTS: A significant main effect on the profession type was found, with physicians having higher mean scores of stroke knowledge compared with nurses and paramedics(F [2, 183]=48.55, P<0.001). The implemented educational program had a positive effect on increasing the level of stroke knowledge among HCPs(F [1, 183]=43.31, P<0.001). The utilization of any evidence-based assessment tools for patients with suspected stroke was denied by 36% of the total sample.CONCLUSIONS: The implemented intervention can increase HCP's knowledge regarding stroke. Stroke education should be considered as one of the essential requirements for professional development for all HCPs in the emergency.
文摘BACKGROUND: Ensuring about the patient's safety is the f irst vital step in improving the quality of care and the emergency ward is known as a high-risk area in treatment health care. The present study was conducted to evaluate the selected risk processes of emergency surgery department of a treatment-educational Qaem center in Mashhad by using analysis method of the conditions and failure effects in health care.METHODS: In this study, in combination(qualitative action research and quantitative crosssectional), failure modes and effects of 5 high-risk procedures of the emergency surgery department were identified and analyzed according to Healthcare Failure Mode and Effects Analysis(HFMEA). To classify the failure modes from the "nursing errors in clinical management model(NECM)", the classification of the effective causes of error from "Eindhoven model" and determination of the strategies to improve from the "theory of solving problem by an inventive method" were used. To analyze the quantitative data of descriptive statistics(total points) and to analyze the qualitative data, content analysis and agreement of comments of the members were used.RESULTS: In 5 selected processes by "voting method using rating", 23 steps, 61 sub-processes and 217 potential failure modes were identifi ed by HFMEA. 25(11.5%) failure modes as the high risk errors were detected and transferred to the decision tree. The most and the least failure modes were placed in the categories of care errors(54.7%) and knowledge and skill(9.5%), respectively. Also, 29.4% of preventive measures were in the category of human resource management strategy.CONCLUSION: "Revision and re-engineering of processes", "continuous monitoring of the works", "preparation and revision of operating procedures and policies", "developing the criteria for evaluating the performance of the personnel", "designing a suitable educational content for needs of employee", "training patients", "reducing the workload and power shortage", "improving team communication" and "preventive management of equipment's" were on the agenda as the guidelines.
基金supported by the National Natural Science Foundation of China (62174115, U21A20147)the Natural Science Foundation of Jiangsu Province (BK20220284)+6 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (22KJB510013)the Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (SYG201924)the University Research Development Fund (RDF-17-01-13)the Key Program Special Fund in XJTLU (KSF-T-03, KSF-A-07)partially supported by the XJTLU AI University Research Centre and Jiangsu (Provincial) Data Science and Cognitive Computational Engineering Research Centre at XJTLUthe Collaborative Innovation Center of Suzhou Nano Science & Technologythe 111 Project and Joint International Research。
文摘Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges.Here, a flexible triboelectric patch(FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porousreinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa^(-1) under a pressure range of 0–5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa^(-1). The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare(Io H) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring.
文摘This paper reviews recent advances in radar sensor design for low-power healthcare,indoor real-time positioning and other applications of IoT.Various radar front-end architectures and digital processing methods are proposed to improve the detection performance including detection accuracy,detection range and power consumption.While many of the reported designs were prototypes for concept verification,several integrated radar systems have been demonstrated with reliable measured results with demo systems.A performance comparison of latest radar chip designs has been provided to show their features of different architectures.With great development of IoT,short-range low-power radar sensors for healthcare and indoor positioning applications will attract more and more research interests in the near future.
文摘BACKGROUND:Healthcare professionals are expected to have knowledge of current basic and advanced cardiac life support(BLS/ACLS) guidelines to revive unresponsive patients.METHODS:Across-sectional study was conducted to evaluate the current practices and knowledge of BLS/ACLS principles among healthcare professionals of North-Kerala using pretested self-administered structured questionnaire.Answers were validated in accordance with American Heart Association's BLS/ACLS teaching manual and the results were analysed.RESULTS:Among 461 healthcare professionals,141(30.6%) were practicing physicians,268(58.1%) were nurses and 52(11.3%) supporting staff.The maximum achievable score was 20(BLS15/ACLS 5).The mean score amongst all healthcare professionals was 8.9±4.7.The mean score among physicians,nurses and support staff were 8.6±3.4,9±3.6 and 9±3.3 respectively.The majority of healthcare professionals scored <50%(237,51.4%);204(44.3%) scored 51%-80%and 20(4.34%)scored >80%.Mean scores decreased with age,male sex and across occupation.Nurses who underwent BLS/ACLS training previously had significantly higher mean scores(10.2±3.4) than untrained(8.2±3.6,P=0.001).Physicians with <5 years experience(P=0.002) and nurses in the private sector(P=0.003)had significantly higher scores.One hundred and sixty three(35.3%) healthcare professionals knew the correct airway opening manoeuvres like head tilt,chin lift and jaw thrust.Only 54(11.7%) respondents were aware that atropine is not used in ACLS for cardiac arrest resuscitation and 79(17.1%) correctly opted ventricular fibrillation and pulseless ventricular tachycardia as shockable rhythms.The majority of healthcare professionals(356,77.2%) suggested that BLS/ACLS be included in academic curriculum.CONCLUSION:Inadequate knowledge of BLS/ACLS principles amongst healthcare professionals,especially physicians,illuminate lacunae in existing training systems and merit urgent redressal.
基金supported by the National Natural Science Foundation of China (Grant No. 51677065)
文摘Wise healthcare is a typical application of wireless sensor network(WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The location of targets need to be determined and reported to the control center,and this leads to the localization problem. While localization in healthcare field demands high accuracy and regional adaptability, the information processing mechanism of human thinking has been introduced,which includes knowledge accumulation, knowledge fusion and knowledge expansion. Furthermore, a fuzzy decision based localization approach is proposed. Received signal strength(RSS) at references points are obtained and processed as position relationship indicators, using fuzzy set theory in the knowledge accumulation stage; after that, optimize degree of membership corresponding to each anchor nodes in different environments during knowledge fusion; the matching degree of reference points is further calculated and sorted in decision-making, and the coordinates of several points with the highest matching degree are utilized to estimate the location of unknown nodes while knowledge expansion. Simulation results show that the proposed algorithm get better accuracy performance compared to several traditional algorithms under different typical occasions.
基金supported in part by the National High-tech R&D Program of China(863 Program) under Grant No. 2013AA102301Shandong Provincial Natural Science Foundation(No. ZR2017MF050)
文摘With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized service is increasing because of ubiquitous deployment of various kinds of medical sensors, which cause response time increase and resource waste. Therefore, leveraging the advantage of complex event processing(CEP) in data stream processing, we propose a hierarchical fog-cloud computing CEP architecture for personalized service to accelerate response time and reduce resource waste. Firstly, we introduce the proposed architecture, which includes sensor layer, fog layer and cloud layer. Secondly, we propose a series of optimizations for the architecture, there are a partitioning and clustering approach and a communication and parallel processing policy to optimize the fog and cloud computing. Finally, we implement a prototype system based on the architecture named Fog Cep Care. Experimental result shows that Fog Cep Care is superior to the traditional IoT-based healthcare application.
基金the Natural Science Foundation of Guangdong Province, China (No.9151009001000021)the Ministry of Education of Guangdong Province Special Fund Funded Projects through the Cooperative of China (No.2009B090300341)+2 种基金the National Natural Science Foundation of China (No.61262013)the Open Fund of Guangdong Province Key Laboratory of Precision Equipment and Manufacturing Technology (No.PEMT1303)the Higher Vocational Education Teaching Reform Project of Guangdong Province (No.20130301011) for their support in this research
文摘The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-health-care architecture. We discuss the four layers of the architecture-data collection, data transport, data storage, and data analysis-as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.
文摘Wireless body area networks （WBANs） use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, and body position to be continuously monitored.
文摘An effective approach to improve healthcare quality is to provide Point-of-care (POC) service through a telecare system. A Body Area Network (BAN) consists of a variety of body sensors. In a telecare system the BAN can provide a network environment that allows timely collection of information and POC service, which gives significant new meaning to a home network. Through the BAN, a home network is able to promptly and effectively collect monitoring information for a telecare system, and to preview monitored medical data in advance. Should a problem arise, it can immediately notify other family members to provide relieve to the sick.
文摘TRSA,the global association for the linen,uniform and facility services industry,launched the Hygienically Clean Healthcare China certification in Shanghai earlier this month.The Chinese version of Hygienically Clean is based on emerging Chinese healthcare laundry guidelines and TRSA’s Hygienically Clean Healthcare certifi-
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Heart failure(HF)has been defined as global disease of pandemic proportions,since it affects around 26 million people worldwide.[1]According to a recent study,age is the most important factor influencing the prevalence of HF,as it is for most other chronic conditions.[2]This means that,with the predicted aging of the population(the proportion of the world’s population aged 60 years and over will nearly double from 2015 to 2050),[3]there will be a growth in the total burden of HF,and a rise in the number of comorbidities in HF patients.According to a recent study,almost 86%of adults with HF have two or more comorbid conditions.[4]Comorbidity,defined as the co-existence of one or more additional conditions in individuals with a specified index medical condition,[5]adds to the complexity of treating elderly patients with HF.
基金supported in part by an award from the VHA Office of Rural Health,Veterans Rural Health Resource CenterDIowa City(VRHRC-IC),Iowa City VA Health Care System,Iowa City,IA(Award#7345)。
文摘Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA collaboration with Federally Qualifed Healthcare Centers(FQHC)to serve rural residing veterans and nearly all existing collaborations involve arrangement of payment for community-based care by VHA to FQHCs.Unfortunately,there is a paucity of descriptive clinical data on existing cross-system collaborations which may help characterize these veterans and aid understanding of conditions for which they may receive treatment across systems.Such data has implications for workforce training,development,and resource allocation[2].Te objective of this report is to describe diferent clinical profles between two mutually exclusive samples:veterans engaged in FQHC only use,and VHA-enrolled veterans engaged in dual VHA and FQHC use.
基金supported by the National Science and Technology Innovation 2030 Major Project(Grant No.2022ZD0208601)the National Natural Science Foundation of China(Grant No.52105593 and 51975513)the Natural Science Foundation of Zhejiang Province,China(No.LR20E050003)。
文摘In the past decade,the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare,Internet of Things,human–machine interfaces,artificial intelligence and soft robotics.Among them,flexible humidity sensors play a vital role in noncontact measurements relying on the unique property of rapid response to humidity change.This work presents an overview of recent advances in flexible humidity sensors using various active functional materials for contactless monitoring.Four categories of humidity sensors are highlighted based on resistive,capacitive,impedance-type and voltage-type working mechanisms.Furthermore,typical strategies including chemical doping,structural design and Joule heating are introduced to enhance the performance of humidity sensors.Drawing on the noncontact perception capability,human/plant healthcare management,human-machine interactions as well as integrated humidity sensor-based feedback systems are presented.The burgeoning innovations in this research field will benefit human society,especially during the COVID-19 epidemic,where cross-infection should be averted and contactless sensation is highly desired.
基金This work is supported by the grant from the National Natural Science Foundation of China under Grants 62104125 and 62311530102,Guangdong Innovative and Entrepreneurial Research Team Program(2021ZT09L197)Guangdong Basic and Applied Basic Research Foundation(2020A1515110887)+1 种基金Tsinghua Shenzhen International Graduate School-Shenzhen Pengrui Young Faculty Program of Shenzhen Pengrui Foundation(No.SZPR2023005)Shenzhen Science and Technology Program(JCYJ20220530143013030).
文摘With the development of artificial intelligence,stiffness sensors are extensively utilized in various fields,and their integration with robots for automated palpation has gained significant attention.This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator(Stiff-TENG)for variable inclusions in soft objects detection.The Stiff-TENG employs a stacked structure comprising an indium tin oxide film,an elastic sponge,a fluorinated ethylene propylene film with a conductive ink electrode,and two acrylic pieces with a shielding layer.Through the decoupling method,the Stiff-TENG achieves stiffness detection of objects within 1.0 s.The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed.The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures,enabling effective recognition of variable inclusions in soft object,reaching a recognition accuracy of 99.7%.Furthermore,its adaptability makes it well-suited for the detection of pathological conditions within the human body,as pathological tissues often exhibit changes in the stiffness of internal organs.This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.
