Context-aware middleware for pervasive elderly homecare

The growing aging population faces a number of challenges, including rising medical cost, inadequate number of medical doctors and healthcare professionals, as well as higher incidence of misdiagnosis. There is an increasing demand for a better healthcare support for the elderly and one promising solution is the development of a context-aware middleware infrastructure for pervasive health/wellness-care. This allows the accurate and timely delivery of health/medical information among the patients, doctors and healthcare workers through a widespread deployment of wireless sensor networks and mobile devices. In this paper, we present our design and implementation of such a Context-Aware Middleware for Pervasive Homecare (CAMPH). The middleware offers several key-enabling system services that consist of P2P-based context query processing, context reasoning for activity recognition and context-aware service management. It can be used to support the development and deployment of various homecare services for the elderly such as patient monitoring, location-based emergency response, anomalous daily activity detection, pervasive access to medical data and social networking. We have developed a prototype of the middleware and demonstrated the concept of providing a continuing-care to an elderly with the collaborative interactions spanning multiple physical spaces: person, home, office and clinic. The results of the prototype show that our middleware approach achieves good efficiency of context query processing and good accuracy of activity recognition.     

A system for automatic measurement of circadian activity deviations in telemedicine

A system for the automatic measurement of the circadian activity deviations in telemedicine has been developed within the framework of a "Health Integrated Smart Home Information System" (HIS2). HIS2 is an experimental platform for the evaluation and the development of technologies in order to ensure the security and quality of life for patients who need home based medical monitoring. Location sensors are placed in each room of the HIS2, allowing the monitoring of patient's successive activity phases within the patient's home environment. We proceeded with a sampling in an hourly schedule to detect weak rhythmic variations. Based on numerous measurements, we established a mean value with confidence limits. These also allowed us to define a zone within which the patient's activity is qualified to be "predictable." Alerts are set off if the patient's activity deviates from this zone.     

Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly

A new method of physical activity monitoring is presented, which is able to detect body postures (sitting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking," 8.2% for sitting, and 1.3% for lying.     

互联网 老人居家监控系统的设计与实现

针对传统老人居家服务系统终端复杂、布线多的问题,提出基于“互联网+”模式的具有远距离监控、灯光控制、防火、防盗、防漏、报警功能的老人居家监控系统.该系统以嵌入式处理器ARM Cortex-A8作为智能终端,采用ZigBee、RF-433、蓝牙、无线网络相结合的通信技术,满足老人居家远程监控和家电控制的个性化需求.使用ZigBee建立家庭内部无线网络,WiFi通信实现外部对该系统的连接和控制,达到不破坏原有装修结构的目的.实验结果表明,该系统能有效提高老人居家监控的便利性和智能家居设备扩展方面的兼容性.     

Comparison Analysis of Radio_Based and Sensor_Based Wearable Human Activity Recognition Systems

Human activity recognition (HAR) systems aim to provide low-cost, low-power, unobtrusive and non-invasive solutions to monitor and collect data accurately for human-centric applications, such as health monitoring, assisted living and rehabilitation. Although wearable sensor_based HAR systems have been demonstrated to be effective in the literature, they raise various concerns such as energy consumption and hardware cost. In this work, we examine the pattern of radio signal strength variations in different activity classes in absence of sensor hardware. We present a performance comparison analysis by setting up two testbeds to compare a sensor_based with a radio_based HAR system over a range of variable metrics such as the number of sensor nodes, and the nodes and the sink node placement with respect to the accuracy and the energy efficiency. Wearable HAR datasets are constructed based on our reported testbeds. The main contributions of this work are in two folds: (1) when eliminating the use of accelerometers in the radio_based system, beside the reduced hardware cost, prolonged lifetime of the HAR system by nearly 30% can be achieved while maintaining the accuracy. The impact of the selected overlapping window size (WS) is also investigated with respect to the accuracy level in both systems over a range of activity classes. (2) The impact of the node placement on the accuracy indicates a higher dependency to the number of nodes, the nodes and the sink node placements in the radio_based system due to the dependency of the results to the distance.     

基于Arduino的老人环境监测系统研究

本文以居家老年人为研究对象;研究基于Arduino控制板和多传感器融合,集生活环境检测模块(温度、污染气体和湿度等)、生理参数信息采集模块(心率、体温和脉搏)与报警模块于一体的健康智能监控系统.该系统对老年人进行生活监测,对于出现异常的情况进行报警,并提醒家人或者相关人员及时救助从而保障老人的健康和安全.该系统具有价格...     

基于CLIPS的居家老年人监护系统设计与实现

随着人口老龄化加剧,独居老人现象逐渐严峻,对老年人生活的实时监护变得日益迫切.基于物联网通信和计算机技术,设计并实现了智能化家居式监护系统.该系统由传感器网络、ZigBee无线网络和PC用户端组成.结合先进的智能传感器、ZigBee和CLIPS专家系统技术,设计开发了PC用户端.设计并搭建了CLIPS专家平台,可以对获...     

基于ZigBee的养老院医疗监护系统的设计

本文针对养老院老人的监护需求,设计了一种基于ZigBee的养老院医疗监护系统,同时介绍了该系统的软件设计和硬件设计。该系统以ZigBee网络为载体建立无线医疗监护网络,采集老人的生命体征数据,并通过上位机软件进行监测与分析。在发生危机情况时,系统可以通过GSM网络向医院发出急救信号。实验表明,该系统可以有效提高养老院的监护水平。     

基于家庭主站的远程医疗监护系统

针对我国远程医疗现状,提出基于家庭主站的远程医疗监护系统。该系统采用以TMS320F2812型DSP为核心的嵌入式家庭主站,能够实现多功能护理床的控制和生理信息的采集等功能。该系统可应用于家庭环境,节省病人监护成本,并保证监护质量。     

Wireless Aspects of Telehealth

Telehealth is the use of electronic information and communication technology to deliver health and medical information and services over large and small distances. Broadband wireless services available today, along with more powerful and convenient handheld devices, will enable a transformational change in health management and healthcare with the introduction of real-time monitoring and timely responses to a wide array of patient needs. Further, a network of low-cost sensors and wireless systems help in creating constantly vigilant and pervasive monitoring capability at home and at work. This paper addresses recent efforts in this growing field, including standards, system architectures, propagation models, and lower layer protocols for body area networks. The paper also suggests the use of cooperative transmission-based strategies for such wireless topologies.     

Determination of Sit-to-Stand Transfer Duration Using Bed and Floor Pressure Sequences

The duration of a sit-to-stand (SiSt) transfer is a representative measure of a person's status of physical mobility. This paper measured the duration unobtrusively and automatically using a pressure sensor array under a bed mattress and a floor plate beside the bed. Pressure sequences were extracted from frames of sensor data measuring bed and floor pressure over time. The start time was determined by an algorithm based on the motion of the center of pressure (COP) on the mattress toward the front edge of the bed. The end time was determined by modeling the foot pressure exerted on the floor in the wavelet domain as the step response of a third-order transfer function. As expected, young and old healthy adults generated shorter SiSt durations of around 2.31 and 2.88 s, respectively, whereas post-hip fracture and post-stroke adults produced longer SiSt durations of around 3.32 and 5.00 s. The unobtrusive nature of pressure sensing techniques used in this paper provides valuable information that can be used for the ongoing monitoring of patients within extended-care facilities or within the smart home environment.     

Development of a continuous respiration olfactometer for odorant delivery synchronous with natural respiration during recordings of brain electrical activity

The continuous respiration olfactometer (CRO) was designed as a respiration-synchronous method for delivering odorants during recordings of brain electrical activity, providing control and monitoring of the timing of the delivery as well as the quantities of odorant involved. The CRO incorporates a purpose-built electronic system designed with very specific temporal and quantitative characteristics, and is composed of four main parts: the respiratory monitoring apparatus, the odorant/air delivery system, the serial interface device and the respiratory monitoring software. Tests were undertaken to determine the performance of the system with reference to the accuracy and precision of timing and control of odorant delivery. Tests were also undertaken to determine the effects of variations in natural respiration between subjects on the capability of the respiratory monitoring system, using a group of 50 subjects, to test the success of a variable gain control to optimize the range of the digitized respiratory output. The delivery system was able to provide information concerning quantities of air or odorant delivered, and the stimulus timing information required for integration with neurophysiological recording techniques.     

计算光学成像:何来，何处，何去，何从？

计算光学成像是一种通过联合优化光学系统和信号处理以实现特定成像功能与特性的新兴研究领域。它并不是光学成像和数字图像处理的简单补充,而是前端（物理域）的光学调控与后端（数字域）信息处理的有机结合,通过对照明、成像系统进行光学编码与数学建模,以计算重构的方式获取图像与信息。这种新型的成像方式将有望突破传统光学成像技术对光学系统以及探测器制造工艺、工作条件、功耗成本等因素的限制,使其在功能（相位、光谱、偏振、光场、相干度、折射率、三维形貌、景深延拓,模糊复原,数字重聚焦,改变观测视角）、性能（空间分辨、时间分辨、光谱分辨、信息维度与探测灵敏度）、可靠性、可维护性等方面获得显著提高。现阶段,计算光学成像已发展为一门集几何光学、信息光学、计算光学、现代信号处理等理论于一体的新兴交叉技术研究领域,成为光学成像领域的国际研究重点和热点,代表了先进光学成像技术的未来发展方向。国内外众多高校与科研院所投身其中,使该领域全面进入了“百花齐放,百家争鸣”的繁荣发展局面。作为本期《红外与激光工程》——南京理工大学专刊“计算光学成像技术”专栏的首篇论文,本文概括性地综述了计算光学成像领域的历史沿革、发展现状、并展望其未来发展方向与所依赖的核心赋能技术,以求抛砖引玉。     

基于TVOS打造广电智慧医养平台的实践

本文阐述了基于TVOS终端开发家庭医疗、健康和养老等应用服务的创新实践。通过与医疗机构和管理机构进行信息化共享,以云计算和大数据为基础,构建了广电的“智慧医养”业务平台,创新了广电网络在医疗卫生健康领域的信息化服务。     

Wireless Health Care Service System for Elderly With Dementia

The purpose of this paper is to integrate the technologies of radio frequency identification, global positioning system, global system for mobile communications, and geographic information system (GIS) to construct a stray prevention system for elderly persons suffering from dementia without interfering with their activities of daily livings. We also aim to improve the passive and manpowered way of searching the missing patient with the help of the information technology. Our system provides four monitoring schemes, including indoor residence monitoring, outdoor activity area monitoring, emergency rescue, and remote monitoring modes, and we have developed a service platform to implement these monitoring schemes. The platform consists of a web service server, a database server, a message controller server, and a health-GIS (H-GIS) server. Family members or volunteer workers can identify the real-time positions of missing elderly using mobile phone, PDA, Notebook PC, and various mobile devices through the service platform. System performance and reliability is analyzed. Experiments performed on four different time slots, from three locations, through three mobile telecommunication companies show that the overall transaction time is 34 s and the average deviation of the geographical location is about 8 m. A questionnaire surveyed by 11 users show that eight users are satisfied with the system stability and 10 users would like to carry the locating device themselves, or recommend it to their family members.     

FBG-based smart bed system for healthcare applications

This paper presents a smart fiber Bragg grating (FBG) sensor system with an unobtrusive and easy-to-use FBG sensor bed, which automatically monitors the behavior of bedridden patients and their vital signs based on indicative spatio-temporal signature for adaptive intervention triggering and activity planning. We present the subtle design, fabrication, calibration, implementation and deployment issues of the FBG pressure sensors to be used in hospitals or nursing homes to prevent bedsore generation, patient falling out of the bed, and life-threatening situations such as patient's heart rate weakening, breathing pattern change, etc. Through trials conducted in the laboratory for respiratory rate monitoring with a sample group of 10 subjects, the system showed maximum error of ± 1 breaths per minute as compared to manual counting.     

A multiparameter wearable physiologic monitoring system for space and terrestrial applications.

A novel, unobtrusive and wearable, multiparameter ambulatory physiologic monitoring system for space and terrestrial applications, termed LifeGuard, is presented. The core element is a wearable monitor, the crew physiologic observation device (CPOD), that provides the capability to continuously record two standard electrocardiogram leads, respiration rate via impedance plethysmography, heart rate, hemoglobin oxygen saturation, ambient or body temperature, three axes of acceleration, and blood pressure. These parameters can be digitally recorded with high fidelity over a 9-h period with precise time stamps and user-defined event markers. Data can be continuously streamed to a base station using a built-in Bluetooth RF link or stored in 32 MB of on-board flash memory and downloaded to a personal computer using a serial port. The device is powered by two AAA batteries. The design, laboratory, and field testing of the wearable monitors are described.     

云计算环境中差异化数据采集方法研究（英文）

In a growing number of information processing applications,data takes the form of continuous data streams rather than traditional stored databases.Monitoring systems that seek to provide monitoring services in cloud environment must be prepared to deal gracefully with huge data collections without compromising system performance.In this paper,we show that by using a concept of urgent data,our system can shorten the response time for most 'urgent' queries while guarantee lower bandwidth consumption.We argue that monitoring data can be treated differently.Some data capture critical system events;the arrival of these data will significantly influence the monitoring reaction speed which is called urgent data.High speed urgent data collections can help system to react in real time when facing fatal errors.A cloud environment in production,MagicCube,is used as a test bed.Extensive experiments over both real world and synthetic traces show that when using urgent data,monitoring system can lower the response latency compared with existing monitoring approaches.     

Cloud support for large scale e-healthcare systems

Rapid development of wearable devices and mobile cloud computing technologies has led to new opportunities for large scale e-healthcare systems. In these systems, individuals’ health information are remotely detected using wearable sensors and forwarded through wireless devices to a dedicated computing system for processing and evaluation where a set of specialists namely, hospitals, healthcare agencies and physicians will take care of such health information. Real-time or semi-real time health information are used for online monitoring of patients at home. This in fact enables the doctors and specialists to provide immediate medical treatments. Large scale e-healthcare systems aim at extending the monitoring coverage from individuals to include a crowd of people who live in communities, cities, or even up to a whole country. In this paper, we propose a large scale e-healthcare monitoring system that targets a crowd of individuals in a wide geographical area. The system is efficiently integrating many emerging technologies such as mobile computing, edge computing, wearable sensors, cloud computing, big data techniques, and decision support systems. It can offer remote monitoring of patients anytime and anywhere in a timely manner. The system also features some unique functions that are of great importance for patients’ health as well as for societies, cities, and countries. These unique features are characterized by taking long-term, proactive, and intelligent decisions for expected risks that might arise by detecting abnormal health patterns shown after analyzing huge amounts of patients’ data. Furthermore, it is using a set of supportive information to enhance the decision support system outcome. A rigorous set of evaluation experiments are conducted and presented to validate the efficiency of the proposed model. The obtained results show that the proposed model is scalable by handling a large number of monitored individuals with minimal overhead. Moreover, exploiting the cloud-based system reduces both the resources consumption and the delay overhead for each individual patient.