An IR Sensor Based Smart System to Approximate Core Body Temperature

Herein demonstrated experiment studies two methods, namely convection and body resistance, to approximate human core body temperature. The proposed system is highly energy efficient that consumes only 165 mW power and runs on 5 VDC source. The implemented solution employs an IR thermographic sensor of industry grade along with AT Mega 328 breakout board. Ordinarily, the IR sensor is placed 1.5–30 cm away from human forehead (i.e., non-invasive) and measured the raw data in terms of skin and ambient temperature which is then converted using appropriate approximation formula to find out core body temperature. The raw data is plotted, visualized, and stored instantaneously in a local machine by means of two tools such as Makerplot, and JAVA-JAR. The test is performed when human object is in complete rest and after 10 min of walk. Achieved results are compared with the CoreTemp CM-210 sensor (by Terumo, Japan) which is calculated to be 0.7 °F different from the average value of BCT, obtained by the proposed IR sensor system. Upon a slight modification, the presented model can be connected with a remotely placed Internet of Things cloud service, which may be useful to inform and predict the user’s core body temperature through a probabilistic view. It is also comprehended that such system can be useful as wearable device to be worn on at the hat attachable way.     

中医诊断技术发展及四诊信息融合研究

近20余年来,中医诊断技术取得了突破性进展,在舌诊、面诊、声诊、嗅诊、问诊、脉诊客观化信息采集设备和数据处理软件方面成果丰硕,并开展了相应的临床研究,但在诊断技术及指标的规范化方面尚处于起步阶段,目前仅出台舌诊仪相关国际标准1项、中医脉诊压力传感器国际标准1项。随着计算机信息处理技术在中医领域的深入渗透,模式识别方法在四诊信息融合中得到应用和发展,四诊合参辅助诊疗及中医四诊检测系统的研发,建立了更适合中医思维的辨证模型和人工智能识别平台;未来,借助物联网技术、云平台技术、可穿戴技术,四诊信息采集设备必将向小型化、可穿戴式发展,并可研发中医诊疗机器人,以期在中医临床及各级医疗平台、慢性病管理、健康风险评估等方面发挥更大的作用。     

A Systematic Review of Wearable Systems for Cancer Detection: Current State and Challenges

Rapid growth of sensor and computing platforms have introduced the wearable systems. In recent years, wearable systems have led to new applications across all medical fields. The aim of this review is to present current state-of-the-art approach in the field of wearable system based cancer detection and identify key challenges that resist it from clinical adoption. A total of 472 records were screened and 11 were finally included in this study. Two types of records were studied in this context that includes 45% research articles and 55% manufactured products. The review was performed per PRISMA guidelines where considerations was given to records that were published or reported between 2009 and 2017. The identified records included 4 cancer detecting wearable systems such as breast cancer (36.3%), skin cancer (36.3%), prostate cancer (18.1%), and multi-type cancer (9%). Most works involved sensor based smart systems comprising of microcontroller, Bluetooth module, and smart phone. Few demonstrated Ultra-Wide Band (i.e. UWB) antenna based wearable systems. Skin cancer detecting wearable systems were most comprehensible ones. The current works are gradually progressing with seamless integration of sensory units along with smart networking. However, they lack in cloud computing and long-range communication paradigms. Artificial intelligence and machine learning are key ports that need to be attached with current wearable systems. Further, clinical inertia, lack of awareness, and high cost are altogether pulling back the actual growth of such system. It is well comprehended that upon sincere orientation of all identified challenges, wearable systems would emerge as vital alternative to futuristic cancer detection.     

Using Non-Traditional Interfaces to Support Physical Therapy for Knee Strengthening

Physical therapy consists mainly in the execution of rehabilitation processes that aim to help overcome injuries, as well as develop, maintain, or restore maximum body movement. Knee rehabilitation is one kind of physical therapy that requires daily exercises which could be considered monotonous and boring by the patients, discouraging their improvement. This is coupled with the fact that most physical therapists assess exercise performance through verbal and visual means with mostly manual measurements, making it difficult to constantly verify and validate if patients perform the exercises correctly. This article describes a physical therapy monitoring system that uses wearable technology to assess exercise performance and patient progress. This wearable device is able to measure and transfer the movement’s data from the patient’s limb to a mobile device. Moreover, the user interface is a game, which provides an entertaining approach to therapy exercising. In this article, it is shown that the developed system significantly increases daily user engagement in rehabilitation exercises, through a gameplay that matches physical therapy requirements for knee rehabilitation, as well as offering useful quantitative information to therapists.     

Evaluation of a Novel Integrated Sensor System for Synchronous Measurement of Cardiac Vibrations and Cardiac Potentials

The measurement of human body vibrations as a result of heart beating, simultaneously with cardiac potentials have been demonstrated in past studies to bring additional value to diagnostic cardiology through the detection of irregularities in the mechanical movement of the heart. The equipment currently available to the medical community is either large and bulky or difficult to synchronize. To address this problem, a novel integrated sensor system has been developed to record cardiac vibration and cardiac potential simultaneously and synchronously from a single compact site on the chest. The developed sensor system is lightweight, small in size, and suitable for mounting on active moving patients. The sensor is evaluated for its adequacy in measuring cardiac vibrations and potentials. In this evaluation, 45 independent signal recording are studied from 15 volunteers, and the morphology of the recorded signals are analyzed qualitatively (by visual inspection) and quantitatively (by computational methods) against larger devices used in established cardiac vibration studies (reference devices). It is found that the cardiac vibration signals acquired by the integrated sensor has 92.37% and 81.76% identically identifiable systolic and diastolic cardiac complexes, respectively, when compared to the cardiac vibration signals recorded simultaneously from the reference device. Further, the cardiac potential signals acquired by the integrated sensor show a high correlation coefficient of 0.8912 and a high estimated signal-to-noise-ratio of 22.00 dB when compared to the reference electrocardiograph (non-standard leads) acquired through a common clinical machine. The results suggest that the tiny, wearable, integrated sensor system that synchronously measures cardiac vibrations and cardiac potentials may be practical for use as an alternative or assistive cardiac diagnostic tool.     

Whole Body Scanning in Medicine: I. Physical Aspects

Fundamental physical aspects of scanning the human body to determine in vivo radioisotope distribution are discussed, with special reference to the importance of collimator design and data display system. The pertinent details of a sensitive whole body scanner which has proved useful in clinical practice are described. The main features contributing to its versatility are: (1) fully automatic operation, (2) use of a 5″ diameter detector crystal with a focusing collimator, (3) provision to scale the scintiscan presentation to a convenient size, and (4) photographic recording of the scan.     

基于CUDA和深度置信网络的手写字符识别

为了应对海量的字符(手写)识别,提出了一种将统一计算设备架构 (Compute Unified Device Architecture, CUDA)和深度置信网络相结合的方法进行手写字符识别。该方法结合受限玻尔兹曼机和反向传播神经网络形成深度置信网络对字符图片数据进行识别,并且使用CUDA在图形处理器（GPU）上进行并行计算来完成识别过程。实验结果表明,使用该方法后,在不降低识别精度的情况下手写字符识别的速度大幅提升。     

移动背景下医疗健康可穿戴设备的数据生命周期

简要介绍国内外医疗健康可穿戴设备数据生命周期研究现状并对代表案例加以分析。从数据采集、数据传输、互操作、数据集成和数据管理5个方面对医疗健康可穿戴设备数据的流通路径进行分析,结合代表案例G5移动健康血糖监测系统加以验证,发现医疗健康可穿戴设备数据流通过程中存在数据复用与交换、数据隐私安全与保护等问题。     

The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility

Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center (http://mobilize.stanford.edu) to harness these data to improve human mobility and help lay the foundation for using data science methods in biomedicine. The Center is organized around 4 data science research cores: biomechanical modeling, statistical learning, behavioral and social modeling, and integrative modeling. Important biomedical applications, such as osteoarthritis and weight management, will focus the development of new data science methods. By developing these new approaches, sharing data and validated software tools, and training thousands of researchers, the Mobilize Center will transform human movement research.     

可穿戴设备研究热点及趋势分析

目的：探讨可穿戴设备研究热点及其发展态势。方法：用文献调研法与文献计量法分析可穿戴设备的研究热点及趋势。结果：未来的一段时间内,可穿戴设备的发展前景依然广阔,美国、韩国等电子科技强国是可穿戴设备新技术的主要发源地,我国可穿戴设备产品的核心竞争力、配套产业链、生产主体及应用程度等方面与美国、韩国有较大差距。共词分析和聚类分析发现,当前可穿戴设备的研究热点是产品的设计理念、传感器新技术以及能源储存装置技术。文献调研发现,今后可穿戴设备的发展趋势主要分布在系统的独立性、低耗续航高集成技术、人机交互技术以及大数据以及云计算技术等方面。结论：可穿戴设备目前尚处于初步发展的阶段,其设计、功能和管理等方面的许多问题需待解决,但其发展前景广阔。     

基于核谱回归与随机森林的脑电情感识别

利用DEAP情感数据库研究脑电的情感识别问题。首先,使用聚类算法确定情感状态的目标类别;然后,比较了两种不同的特征提取方法：一种是小波变换,另一种是非线性动力学,并研究了基线特征对情感分类效果的影响;最后,研究了5种特征降维方法对分类性能的影响,同时比较了4种不同分类器的性能,包括K-最近邻（KNN）、朴素贝叶斯（NB）、支持向量机（SVM）和随机森林（RF）。研究结果表明,核谱回归（KSR）降维方法和随机森林分类器的组合对情感状态的分类效果最好。通过对脑区与情感关系的研究发现,只使用部分脑区的少量电极也可以达到90%的分类准确度,这些电极主要分布在额叶皮层。     

Variability Analysis of Therapeutic Movements using Wearable Inertial Sensors

A variability analysis of upper limb therapeutic movements using wearable inertial sensors is presented. Five healthy young adults were asked to perform a set of movements using two sensors placed on the upper arm and forearm. Reference data were obtained from three therapists. The goal of the study is to determine an intra and inter-group difference between a number of given movements performed by young people with respect to the movements of therapists. This effort is directed toward studying other groups characterized by motion impairments, and it is relevant to obtain a quantified measure of the quality of movement of a patient to follow his/her recovery. The sensor signals were processed by applying two approaches, time-domain features and similarity distance between each pair of signals. The data analysis was divided into classification and variability using features and distances calculated previously. The classification analysis was made to determine if the movements performed by the test subjects of both groups are distinguishable among them. The variability analysis was conducted to measure the similarity of the movements. According to the results, the flexion/extension movement had a high intra-group variability. In addition, meaningful information were provided in terms of change of velocity and rotational motions for each individual.     

Differential Privacy Preserving in Big Data Analytics for Connected Health

In Body Area Networks (BANs), big data collected by wearable sensors usually contain sensitive information, which is compulsory to be appropriately protected. Previous methods neglected privacy protection issue, leading to privacy exposure. In this paper, a differential privacy protection scheme for big data in body sensor network is developed. Compared with previous methods, this scheme will provide privacy protection with higher availability and reliability. We introduce the concept of dynamic noise thresholds, which makes our scheme more suitable to process big data. Experimental results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy.     

Center of excellence for mobile sensor data-to-knowledge (MD2K)

Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. The research conducted by MD2K is targeted at improving health through early detection of adverse health events and by facilitating prevention. MD2K will make its tools, software, and training materials widely available and will also organize workshops and seminars to encourage their use by researchers and clinicians.     

Ubiquitous Healthcare Computing with Sensor Grid Enhancement with Data Management System (SEGEDMA)

Wireless Sensor Network (WSN) can be deployed to monitor the health of patients suffering from critical diseases. Also a wireless network consisting of biomedical sensors can be implanted into the patient’s body and can monitor the patients’ conditions. These sensor devices, apart from having an enormous capability of collecting data from their physical surroundings, are also resource constraint in nature with a limited processing and communication ability. Therefore we have to integrate them with the Grid technology in order to process and store the collected data by the sensor nodes. In this paper, we proposed the SEnsor Grid Enhancement Data Management system, called SEGEDMA ensuring the integration of different network technologies and the continuous data access to system users. The main contribution of this work is to achieve the interoperability of both technologies through a novel network architecture ensuring also the interoperability of Open Geospatial Consortium (OGC) and HL7 standards. According to the results, SEGEDMA can be applied successfully in a decentralized healthcare environment.     

Relationship Between Measurement Site and Motion Artifacts in Wearable Reflected Photoplethysmography

Pulse rates obtained from wearable photoplethysmography (PPG) sensors are important for monitoring cardiovascular condition, especially during exercise. However, it is difficult to precisely count pulse rates during exercise because PPG is sensitive to body movement. The artifacts from body movement are caused by a change in the blood volume at the measurement site, in addition to pulsatile changes. Here, we investigated the influence of motion artifact with respect to light source and anatomical sites. In this study, we compared the signal from green-light PPG to that from infrared PPG at different anatomical sites. In these experiments, 11 subjects were asked to either assume a resting position or generate spontaneous motion artifact by jumping and swinging their arm. As a result, pulse rates obtained from green-light PPG showed a higher correlation with the ECG R-R interval as compared to those obtained with infrared. Additionally, the signal from the upper arm showed less artifact than did the peripheral one. Therefore, the green-light PPG may be useful for pulse rate monitoring.     

A WBAN-based Real-time Electroencephalogram Monitoring System: Design and Implementation

In this study, a flexible wireless body area network (WBAN) node platform has been designed and implemented based on the Zigbee technology. In order to provide wide range WBAN for health monitoring, a Zigbee/Internet Gateway (ZiGW) has also been developed rather than using a PDA or a host PC to connect different WBANs by using the Internet as the communication infrastructure. The proposed body sensor node platform promises a cost-effective, flexible platform for developing physical sensor node in real-time health monitoring. The ZiGW can provide an effective method to connect WBAN with the Internet. In this work, we present the implementation of an Electroencephalogram (EEG) monitoring system using the proposed methods. In this proposed system, real-time EEG signals can be remotely monitored by physicians via Internet, and the collected EEG data is stored in the online EEG database which can be shared with physicians or researchers for further analysis.     

寄生虫虫卵图像计算机自动识别技术研究

目的探讨建立寄生虫虫卵显微图像计算机自动识别系统的相关问题。方法从研究国内外虫卵图像识别技术的相关进展和10种人体常见寄生虫虫卵图像的特性入手,以Visual C++6.0为系统开发工具,运用数字图像处理,将虫卵数字图像经过预处理,提取形态、颜色和纹理等特征,通过相应的虫卵分类识别算法的实现,初步实现人体常见寄生虫虫卵图像的计算机自动识别。结果实验系统可在获取图像后一步实现对10种常见人体寄生虫虫卵的自动识别,平均识别率为93.0%,每幅图像处理时间为1~3s。结论对虫卵图像纹理特征的选用,可显著提高系统对样本的分类精度;系统开发所采用的分析方法、特征和计算参数的选取原则,有益于日后的类似研究。     

Network Coded Cooperative Communication in a Real-Time Wireless Hospital Sensor Network

The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated.     

Toward a Fault Tolerant Architecture for Vital Medical-Based Wearable Computing

Advancements in computers and electronic technologies have led to the emergence of a new generation of efficient small intelligent systems. The products of such technologies might include Smartphones and wearable devices, which have attracted the attention of medical applications. These products are used less in critical medical applications because of their resource constraint and failure sensitivity. This is due to the fact that without safety considerations, small-integrated hardware will endanger patients’ lives. Therefore, proposing some principals is required to construct wearable systems in healthcare so that the existing concerns are dealt with. Accordingly, this paper proposes an architecture for constructing wearable systems in critical medical applications. The proposed architecture is a three-tier one, supporting data flow from body sensors to cloud. The tiers of this architecture include wearable computers, mobile computing, and mobile cloud computing. One of the features of this architecture is its high possible fault tolerance due to the nature of its components. Moreover, the required protocols are presented to coordinate the components of this architecture. Finally, the reliability of this architecture is assessed by simulating the architecture and its components, and other aspects of the proposed architecture are discussed.