Ontology driven interactive healthcare with wearable sensors

Ubiquitous healthcare is the service that offers health-related information and contents to users without any limitations of time and space. Especially, to offer customized services to users, the technology of acquiring context information of users in real time is the most important consideration. In this paper, we researched wearable sensors. We proposed the ontology driven interactive healthcare with wearable sensors (OdIH_WS) to achieve customized healthcare service. For this purpose, wearable-sensor-based smart-wear and methods of data acquisition and processing are being developed. The proposed system has potential value in healthcare. A smart wear using wearable sensors is fabricated as a way of non-tight and comfortable style fitting for the curves of the human body based on clothes to wear in daily life. The design sample of the smart wear uses basic stretch materials and is designed to sustain its wearable property. To offer related information, it establishes an environment-information-based healthcare ontology model needed for inference, and it is composed of inside-outside context information models depending on the users’ context. The modeling of the proposed system involved combinations of information streams, focusing on service context information. With the proposed service inference rules, customized information and contents could be drawn by the inference engine. In the established OdIH_WS, real-time health information monitoring was achieved. The results of system performance and users’ satisfaction evaluations confirmed that the proposed system is superior to other existing systems.     

Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device

In a typical ambulatory health monitoring systems, wearable medical sensors are deployed on the human body to continuously collect and transmit physiological signals to a nearby gateway that forward the measured data to the cloud-based healthcare platform. However, this model often fails to respect the strict requirements of healthcare systems. Wearable medical sensors are very limited in terms of battery lifetime, in addition, the system reliance on a cloud makes it vulnerable to connectivity and latency issues. Compressive sensing (CS) theory has been widely deployed in electrocardiogramme ECG monitoring application to optimize the wearable sensors power consumption. The proposed solution in this paper aims to tackle these limitations by empowering a gateway-centric connected health solution, where the most power consuming tasks are performed locally on a multicore processor. This paper explores the efficiency of real-time CS-based recovery of ECG signals on an IoT-gateway embedded with ARM’s big.little™ multicore for different signal dimension and allocated computational resources. Experimental results show that the gateway is able to reconstruct ECG signals in real-time. Moreover, it demonstrates that using a high number of cores speeds up the execution time and it further optimizes energy consumption. The paper identifies the best configurations of resource allocation that provides the optimal performance. The paper concludes that multicore processors have the computational capacity and energy efficiency to promote gateway-centric solution rather than cloud-centric platforms.     

Bicycle messengers: energy expenditure and exposure to air pollution

The purpose of the study was to determine the level of energy expenditure and exposure to air pollution for bicycle messengers. Relationships between heart rate (HR) and oxygen uptake, and between HR and pulmonary ventilation (VE) for each participant were established in laboratory tests. Air pollution and HR were measured during one working day. The total oxygen uptake was then described as the total energy expenditure in Joule (J) and in multiples of the energy expenditure at rest (MET). The mean energy expenditure during a working day (8 h) was 12 MJ, (4.8 MET). The level of air pollution exposure when cycling seemed to be comparable with the levels of exposure when sitting inside a vehicle. The VE during cycling was four times higher than resting value. Increased VE led to increased exposure to air pollution.     

Bicycle messengers: energy expenditure and exposure to air pollution

The purpose of the study was to determine the level of energy expenditure and exposure to air pollution for bicycle messengers. Relationships between heart rate (HR) and oxygen uptake, and between HR and pulmonary ventilation (VE) for each participant were established in laboratory tests. Air pollution and HR were measured during one working day. The total oxygen uptake was then described as the total energy expenditure in Joule (J) and in multiples of the energy expenditure at rest (MET). The mean energy expenditure during a working day (8 h) was 12 MJ, (4.8 MET). The level of air pollution exposure when cycling seemed to be comparable with the levels of exposure when sitting inside a vehicle. The VE during cycling was four times higher than resting value. Increased VE led to increased exposure to air pollution.     

可穿戴式生命体征监护设备的研制

专门针对高压氧舱内生命体征多参数监测及健康监护的技术实现问题,提出了一种基于IEEE802.15.4协议的无线传感检测技术系统解决方案,阐述了无线传感检测系统的体系结构以及主控制节点与生命体征参数采集传感器节点的硬件设计方法,给出了软件系统架构、软件设计流程及监护软件工作界面,对MAC层帧结构、物理层帧结构及系统时间同步策略进行了详细分析和设计.该系统样机已进入临床实验阶段,文中还给出了患者的临床检测数据,并与实用的进口监护设备的检测数据进行了对比,验证了临床应用的可行性.     

An acceleration vector variance based method for energy expenditure estimation in real-life environment with a smartphone/smartwatch integration

By combining embedded passive sensing technologies from both smartphone and smartwatch, it is possible to obtain a high quality detection of sedentary activities (sitting, reclining posture…), movements (walking…) and periods of more intense body movements (running…). Our research encompasses the definition of an energy-saving function for the total energy expenditure (TEE) estimation using accelerometry data. This topic is clearly at the crossroad of both computer science and medical research. The present contribution proposes an intelligent wearable system, which combines the use of two complementary devices: smartphone and smartwatch to collect accelerometry data. Together they can precisely discriminate real-world human sedentary and active behaviors and their duration and estimate energy expenditure in real time and in free-living conditions. The results of the study are expected to help subjects to handle their daily-living physical activity notably for being compliant with the physical activity international guidelines (150 min of moderate intensity activity/week). It is also expected that the physical activity feedbacks using these popular devices can prove the effectiveness of such wearable objects to promote individually-adapted healthy behavioral changes. The performance of the proposed function was evaluated by comparing the energy expenditure given by the smartphone and smartwatch with that produced by Armband^®. The mean error of TEE between the proposed function and Armband^® was less than 4% for an average 6 h period of daily-living activities. The main theoretical contribution is the definition of a new predictive mathematical function of energy expenditure, which competes with the non-public function used in dedicated costly devices such as Armband^®. In addition, this work demonstrates the potential of wearable technologies.     

Wearable IoT data stream traceability in a distributed health information system

With the soaring interest in the Internet of Things (IoT), some healthcare providers are facilitating remote care delivery through the use of wearable devices. These devices are employed for continuous streaming of personal medical data (e.g., vitals, medications, allergies, etc.) into healthcare information systems for the purposes of health monitoring and efficient diagnosis. However, a challenge from the perspective of the physicians is the inability to reliably determine which data belongs to who in real-time. This challenge emanates from the fact that healthcare facilities have numerous users who own multiple devices; thereby creating an N x M data source heterogeneity and complexities for the streaming process. As part of this research, we seek to streamline the process by proposing a wearable IoT data streaming architecture that offers traceability of data routes from the originating source to the health information system. To overcome the complexities of mapping and matching device data to users, we put forward an enhanced Petri Nets service model that aids with a transparent data trace route generation, tracking and the possible detection of medical data compromises. The results from several empirical evaluations conducted in a real-world wearable IoT ecosystem prove that: 1) the proposed system’s choice of Petri Net is best suited for linkability, unlinkability, and transparency of the medical IoT data traceability, 2) under peak load conditions, the IoT architecture exhibits high scalability, and 3) distributed health information system threats such as denial of service, man-in-the-middle, spoofing, and masking can be effectively detected.     

基于HJ-1B卫星遥感数据的水稻识别技术研究

为快速、准确地在遥感图像上识别水稻作物的信息,满足县级尺度水稻遥感监测的需要,以野外实地调查资料、1∶5万地形图数据为辅助,通过光谱分析法,分析研究HJ-1B星CCD数据的水稻作物的光谱反射特性,建立水稻作物遥感信息识别模型。采用决策树分类方法提取水稻作物信息,并将该技术方法应用于广西宾阳县水稻作物信息提取研究。采用实测样地数据,利用混淆矩阵进行精度评价验证,总精度为94.9%,Kappa系数为0.8533。研究表明,该水稻作物的识别技术,可以为了解我国水稻种植情况,进行水稻长势监测和产量估测提供技术参考。     

A novel fog-computing-assisted architecture of E-healthcare system for pregnant women

Recently, there is a tremendous rise and adoption of smart wearable devices in smart healthcare applications. Moreover, the advancement in sensors and communication technology empowers to detect and analyse physiological data of an individual from the wearable device. At present, the smart wearable device based on internet of things is assisting the pregnancy woman to continuously monitor their health status for avoiding the severity. The physiological data analysis of wearable device is processed with the assistance of fog computing due to limited computational and energy capability in the wearable device. Additionally, fog computing overcomes the excess latency that is created by cloud computing during physiological data analysis. In this article, a smart health monitoring IoT and fog-assisted framework are proposed for obtaining and processing the temperature, blood pressure, ECG, and pulse oximeter parameters of the pregnant woman. Based on real time series data, the rule-based algorithm logged in the wearable device with fog computing to analyse the critical health conditions of pregnant women. The proposed wearable device is validated and tested on 80 pregnant women in real time, and wearable device is delivering the 98.75% accuracy in providing health recommendations.

     

基于网页信息检索的地理信息变化检测方法

针对地理信息变化频繁,难以及时发现的问题,提出了一种基于网页信息检索的地理信息变化检测方法,通过设计搜索条件在互联网上收集符合条件的网页,设计评价方法评价搜索结果的可信度,并对最终搜索结果进行统计和空间分析,实现基于网页信息检索技术的地理信息变化检测。以杭州地区为例,开发了基于Web的杭州地区地物变化检测系统,验证了该方法的可行性及有效性,为区域的地物变化检测提供了新方法。     

Woodlands equipment maintenance: An analysis of mechanical labor energy expenditure

The accident frequency rate of forestry field mechanics is eight times the industry average. A contributing factor is physical fatigue caused by energy-demanding tasks, difficult working positions, and hostile environmental conditions.

the heart rate/oxygen uptake method was used to determine human energy expenditure levels for eight specific maintenance activities. Eight work positions were also identified, and their energy expenditure levels were recorded.

Mean daily shift-level energy expenditures were found to be in excess of 10,000 kJ, equivalent to a continuous average output above 20.0 kJ per minute. The most energy-demanding task was removing engine protection plates. The most energy-demanding position was standing, with the body bent forward at the waist, over some obstacle.

It is concluded that maintenance mechanics routinely work at 33% of their predicted maximal oxygen uptake level, a value that is considered to be at the top of the acceptable range for physically active male workers. At this level, the potential for an accident situation is high.     

一种基于压缩域的穿戴式心电信号直接特征提取与分类方法

压缩感知是实现可穿戴式健康监测系统低能耗工作方式的一种有效途径,而现有基于压缩感知的心电信号分类方法大多需要在进行分类之前,先使用重构算法恢复出原始心电信号,这可能会导致较高的计算复杂度高,不适合于具有实时性需求的可穿戴式系统。提出一种基于压缩域的穿戴式心电信号的特征提取与自动分类方法。跳过信号重构步骤,使用改进的主成分分析法在压缩域上直接对压缩后的心电信号进行特征提取,并基于最小二乘支持向量机半监督学习方法实现心电信号的自动分类。实验结果表明,相较于在非压缩域上的分类方法,该方法在保证分类性能下降非常少的前提下,心电数据量大大地减少,有效提高了心电信号自动分类的效率。     

基于FSR的可穿戴式足底压力检测系统

足底压力信息的研究对人体健康状况评估和某些疾病预测具有重要意义。提出了一种可穿戴式足底压力检测系统的实现方法。该方法充分考虑了穿戴式检测工具的方便灵活和低功耗要求,采用薄片式力敏电阻器(FSR)和低功耗微小器件,将整套系统集成于鞋垫中,体积小巧,功能实用。实验结果表明:介绍的系统可以实时采集足底压力信号并在计算机上显示压力波形,系统有较好的灵敏度、准确性和可穿戴性,低功耗运行,满足足底压力检测和人体步态分析等实际需要。     

A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications

Human context recognition (HCR) from on-body sensor networks is an important and challenging task for many healthcare applications because it offers continuous monitoring capability of both personal and environmental parameters. However, these systems still face a major energy issue that prevent their wide adoption. Indeed, in healthcare applications, sensors are used to capture data during daily life or extended stays in hospital. Thus, continuous sampling and communication tasks quickly deplete sensors’ battery reserves, and frequent battery replacement is not convenient. Therefore, there is a need to develop energy-efficient solutions for long-term monitoring applications in order to foster the acceptance of these technologies by the patients. In this paper, we survey existing energy-efficient approaches designed for HCR based on wearable sensor networks. We propose a new classification of the energy-efficient mechanisms for health-related human context recognition applications and we review the related works in detail. Moreover, we provide a qualitative comparison of these solutions in terms of energy-consumption, recognition accuracy and latency. Finally, we discuss open research issue and give directions for future works.     

可穿戴设备的数值型流数据差分隐私均值发布

可穿戴设备实时产生的用户健康数据（如心率、血糖等）对健康监测及疾病诊断具有重大意义，然而健康数据属于用户的隐私信息。针对可穿戴设备的数值型流数据均值发布，为防止用户的隐私信息泄漏，提出一种基于自适应采样的可穿戴设备差分隐私均值发布方法。首先，引入适应可穿戴设备流数据均值波动小这一特点的全局敏感度；然后，采用基于卡尔曼滤波调整误差的自适应采样的方式分配隐私预算，提高发布数据的可用性。在发布两种健康数据的实验中，所提方法在隐私预算为0.1时，即高隐私保护强度下，在心率和血糖数据集上的平均相对误差(MRE)分别为0.01和0.08，相较于差分隐私时序监测的滤波和自适应采样（FAST）算法分别降低了36%和33%。所提的均值发布方法能够提高可穿戴设备均值流数据发布的可用性。     

RFID-based indoor location tracking to ensure the safety of the elderly in smart home environments

In recent years, the number of elderly people living alone has grown rapidly. This increases the need for indoor healthcare services that help elderly residents live a safe and independent life. There has been increasing interest in indoor ubiquitous healthcare (U-Healthcare) applications that monitor the elderly unobtrusively via sensors and that warn them or healthcare providers of abnormal conditions. In U-Healthcare applications, automatically locating and tracking users who move around a building is a fundamental feature. Outdoor location sensing technologies such as the global positioning systems are not suitable for use in indoor environments, such as “smart home” applications, because their indoor-based location sensing lacks accuracy. This paper proposes an indoor U-Healthcare system that uses radio-frequency identification technology to accurately locate and track the elderly. The proposed system provides real-time monitoring of elderly people’s whereabouts. In addition, it analyzes their locations in association with time slots and the length of time they stay in the same place, thus inferring information such as movement patterns, ranges, and frequencies. This information is used to determine elderly people’s well-being and to warn family or healthcare workers of any potential problems. The proposed indoor U-Healthcare system improves the quality and convenience of care delivered to elderly people.     

小波局部高频替代融合方法

IHS变换是图象融合的经典算法之一。采取IHS变换与小波变换结合的融合算法是近年发展起来的遥感数据融合方法,发挥了IHS和小波两种变换算法的优点。效果比用单一一种方法更显著,提出了小波局部高频替代的融合方法。ETM遥感数据5、4、3波段由IHS盂塞尔彩色空间正变换后的Ⅰ亮度分量,经与全色波段中高能量替代的小波变换,形成一个新的集中了Ⅰ亮度分量和全色波段高频能量的新分量,再通过IHS进行反变换,经过实验,表明此方法可显著提高融合后图象的分辨率,同时又保持了原来多光谱图象的光谱信息。将此方法处理结果用在四川岷江“退耕还林”遥感调查项目中,可以提高土地利用计算精度。     

A Low-Power, Battery-Free Tag for Body Sensor Networks

The authors describe a low-power, battery-free tag for use in pervasive sensing applications such as wearable patient-monitoring systems and body sensor networks. The tag consists of a custom integrated circuit, an antenna for RF energy harvesting, and several sensors for monitoring important physiological parameters and generating alarms when necessary. They also describe experimental results with phonocardiogram and photoplethysmogram signals and demonstrate tag localization within 0.6 m by using an audio localization scheme.     

Remote sensing of land use and vegetation for mesoscale hydrological studies

In this paper, methods for mapping land use changes and vegetation parameters using remote sensing data are presentedin the context of hydrological studies. In the first part, a land use and land cover classification system (RUB-LUCS: Ruhr University Bochum - Land Use and Land Cover Classification System) is developed for providing distributed information for hydrological modelling and for detection of distributed land use changes. Applying this system to Landsat data, land use time series is created for hydrological modelling of effects of man-made changes in the Sauer River Basin. In the second part, equations are established for estimating leaf area indices using vegetation indices calculated from remote sensing data and a two stream approximation model for estimating leaf area indices is applied to the Sauer River Basin. Combining the two approaches, a method has been found for calculating leaf area indices for mesoscale river basins using remote sensing data.     

柔性压力传感技术及发展趋势

为了实现“治已病”向“治未病”的转变,用于人体健康监测的智能传感技术亟待研究。人体脉搏包含丰富的心血管系统参数,历来是临床健康评估的重要信息来源。因此,准确、有效地监测人体脉搏并分析其包含的生理健康参数,是预防和治疗心血管疾病的有效途径。随着传感技术、电子设备和人工智能的发展,可穿戴式的智能健康监测设备备受关注。为了实现随时随地的自主健康监测,越来越多的研究学者致力于柔性压力传感器在智能医疗领域的研究,旨在通过柔性传感器的设计,实现体表脉搏监测以评估人体心血管系统健康状况。根据传感器工作原理和结构的不同,介绍了用于人体体表脉搏监测的柔性压力传感器的最新研究进展,并对未来的柔性压力传感技术提出了展望。