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Automated detection of sensor detachments for physiological sensing in the wild 野外生理传感传感器分离的自动检测
K. Plarre, A. Raij, Santanu Guha, M. al’Absi, Emre Ertin, Santosh Kumar
Body area sensor networks measure biomedical signals from subjects continuously, as they go about their daily lives. Signals measured in these conditions are affected by anomalies, such as artifacts and noise. Some anomalies can be corrected, if detected in real-time, for example, ECG electrode detachment. We present energy and computationally efficient algorithms for the detection of sensor detachment, developed for the AutoSense system.
身体区域传感器网络在受试者的日常生活中不断测量生物医学信号。在这些条件下测量的信号会受到异常现象的影响,例如伪影和噪声。如果实时检测到某些异常,例如ECG电极脱离,则可以纠正这些异常。我们提出了能量和计算效率高的算法,用于检测传感器脱离,为AutoSense系统开发。
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引用次数: 6
A patient-centered health monitoring system 以患者为中心的健康监测系统
Y. Chuang, P. Melliar-Smith, L. Moser
We demonstrate a patient-centered health monitoring system that promotes patient-centered healthcare and self-management. Many patients with chronic diseases require daily measurements and medical attention over a long period of time. Patients often forget to take readings and medications on time. We have developed a patient-centered health monitoring system that interfaces two health monitoring devices, a blood pressure monitor and a digital weight scale, to a laptop computer via Bluetooth wireless communication. Each device takes the patient's measurements, and automatically transmits the measured data to the laptop. The patient and/or a healthcare professional can see the historical data from the blood pressure monitor and the digital weight scale as a list or a graph on the laptop. The system not only performs a statistical analysis of the measured data but also displays the results, to enable the patient to understand his/her health conditions and to become engaged in monitoring his/her health.
我们展示了一个以患者为中心的健康监测系统,促进以患者为中心的医疗保健和自我管理。许多患有慢性疾病的患者需要长期进行日常测量和医疗护理。病人经常忘记按时阅读和服药。我们已经开发了一个以病人为中心的健康监测系统,它通过蓝牙无线通信将两个健康监测设备,一个血压监测仪和一个数字体重秤,连接到笔记本电脑上。每个设备都可以测量患者的数据,并自动将测量数据传输到笔记本电脑上。患者和/或医疗保健专业人员可以在笔记本电脑上以列表或图形的形式查看来自血压监测器和数字体重秤的历史数据。该系统不仅对测量数据进行统计分析,而且还显示结果,使患者了解自己的健康状况,并参与监测自己的健康状况。
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引用次数: 1
Evaluation of body sensor network platforms: a design space and benchmarking analysis 人体传感器网络平台评估:设计空间与标杆分析
S. Nabar, Ayan Banerjee, S. Gupta, R. Poovendran
Body Sensor Networks (BSNs) consist of sensor nodes deployed on the human body for health monitoring. Each sensor node is implemented by interfacing a physiological sensor with a sensor platform consisting of components such as microcontroller, radio and memory. Diverse needs of BSN applications require customized platform development for optimizing performance. In this paper, we propose a two-phase framework to evaluate the performance of sensor platforms to match a BSN's computation, communication and sensing requirements: 1) Design Space Determination, wherein we investigate salient features of BSN platforms and quantify them as design coordinates through evaluation metrics such as SPSW (Samples Processed per Second per Watt) and EPC (Expected Power Consumption). To measure these metrics for a platform under typical BSN application workloads, we propose BSN-Bench, a benchmarking suite composed of basic tasks that occur in diverse BSN applications. BSNBench enables an accurate profiling of platforms based on the design coordinates; 2) Design Space Exploration, wherein we explore the design space to find the most suitable platform for a given application. We demonstrate the usage of our framework through a case study, where we consider two practical BSN applications and choose suitable platforms for them.
身体传感器网络(BSNs)由部署在人体上的传感器节点组成,用于健康监测。每个传感器节点通过将生理传感器与由微控制器、无线电和存储器等组件组成的传感器平台相连接来实现。BSN应用的多样化需求需要定制化的平台开发来优化性能。在本文中,我们提出了一个两阶段框架来评估传感器平台的性能,以满足BSN的计算、通信和传感要求:1)设计空间确定,其中我们研究了BSN平台的显著特征,并通过评估指标(如SPSW(每秒每瓦处理的样本)和EPC(预期功耗))将其量化为设计坐标。为了测量典型BSN应用程序工作负载下的平台的这些指标,我们提出了BSN- bench,这是一个基准测试套件,由各种BSN应用程序中的基本任务组成。BSNBench能够基于设计坐标对平台进行精确剖析;2)设计空间探索,我们探索设计空间,为给定的应用程序找到最合适的平台。我们通过一个案例研究来演示我们的框架的使用,其中我们考虑了两个实际的BSN应用程序,并为它们选择了合适的平台。
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引用次数: 22
Wireless non-contact cardiac and neural monitoring 无线非接触式心脏和神经监测
Y. Chi, P. Ng, E. Kang, Joseph Kang, Jennifer Fang, G. Cauwenberghs
Ubiquitous physiological monitoring will be a key driving force in the upcoming wireless health revolution. Cardiac and brain signals in the form of ECG and EEG are two critical health indicators that directly benefit from long-term monitoring. Despite advancements in wireless technology and electronics miniaturization, however, the use of wireless home ECG/EEG monitoring is still limited by the inconvenience and discomfort of wet adhesive electrodes. We have developed a wireless biopotential instrumentation system using non-contact capacitive electrodes that operate without skin contact. The sensors can be embedded within comfortable layers of fabric for unobtrusive use. All of the issues relating to the design of low noise, high performance capacitive sensors are discussed along with full technical details, circuit schematics and construction techniques. The non-contact electrode has been integrated into both a wearable ECG chest harness as well a EEG headband. We have also designed a compact, battery-powered, wireless data acquisition system to interface with multiple electrodes and monitor patient cardiac and neural signals in real time. Experimental data shows that the non-contact capacitive electrode perform comparable to Ag/AgCl electrodes using our special chest harness and head bands to ensure tight, movement-free electrode positioning.
无处不在的生理监测将是即将到来的无线健康革命的关键驱动力。心电和脑电图形式的心脏和大脑信号是两个重要的健康指标,直接受益于长期监测。然而,尽管无线技术和电子微型化取得了进步,但由于湿式粘接电极的不方便和不舒服,家庭无线心电/脑电图监测的使用仍然受到限制。我们开发了一种无线生物电位仪器系统,使用非接触式电容电极,无需皮肤接触即可操作。传感器可以嵌入舒适的织物层中,不显眼地使用。所有与低噪声,高性能电容传感器的设计有关的问题,以及完整的技术细节,电路原理图和施工技术进行了讨论。这种非接触式电极已被集成到可穿戴ECG胸带和EEG头带中。我们还设计了一个紧凑的,电池供电的无线数据采集系统,与多个电极接口,实时监测患者的心脏和神经信号。实验数据表明,使用我们特殊的胸带和头带,非接触式电容电极的性能可与Ag/AgCl电极相比较,以确保紧密,无运动的电极定位。
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引用次数: 89
An Android-based body area network gateway for mobile health applications 一个基于android的身体区域网络网关,用于移动健康应用程序
M. Altini, J. Penders, Herman W. Roebbers
This paper presents a Body Area Network (BAN) gateway to Android mobile phones for mobile health applications. The proposed approach is based on a Secure Digital Input Output (SDIO) interface, which allows for long-term monitoring since the mobile phone hardware can be extended in order to operate with ultra low-power radios. The software architecture implemented on the mobile phone enables different features; data can be displayed, further processed or sent to a remote server exploiting the WLAN or 3G networks. Moreover, the system allows to configure thresholds on the measured parameters and to automatically send alerts such as SMS messages and emails based on these values. The system is illustrated for the case of ambulatory ECG monitoring.
本文提出了一种基于Android手机的身体区域网络(BAN)网关,用于移动健康应用。所提出的方法基于安全数字输入输出(SDIO)接口,该接口允许长期监控,因为移动电话硬件可以扩展,以便与超低功率无线电一起操作。在手机上实现的软件架构实现了不同的功能;数据可以显示、进一步处理或发送到利用WLAN或3G网络的远程服务器。此外,系统允许在测量参数上配置阈值,并根据这些值自动发送SMS消息和电子邮件等警报。该系统以动态心电监护为例进行了说明。
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引用次数: 30
Longitudinal high-fidelity gait analysis with wireless inertial body sensors 基于无线惯性体传感器的纵向高保真步态分析
Adam T. Barth, Benjamin Boudaoud, Jeff S. Brantley, Shanshan Chen, Christopher L. Cunningham, Taeyoung Kim, H. Powell, Samuel A. Ridenour, J. Lach, B. Bennett
Gait analysis has long been used for various medical and healthcare assessments [1]. In orthopedics and prosthetics, gait analysis is essential for identifying the pathology and assessing the efficacy of the orthopedic assistants or prosthetics prescribed. For example, the efficacy of ankle-foot orthoses (AFOs), usually prescribed to patients with muscle disorders, (e.g., cerebral palsy, spinal cord injury, muscular dystrophy, etc.) to prevent contractures [2], remains unclear. Studies on recovery and rehabilitation from knee surgery have shown that gait analysis focusing on knee joint angles is the key to evaluating the efficacy of treatment. In elderly healthcare, gait analysis has also played an important role in studies of fall risks and fall prevention [3]. Even in cognitive and neuropsychology studies, gait analysis becomes an important parameter because of the close relationship between human cognitive skills and motor function. For example, [4] and [5] have shown the research value of gait analysis in Parkinson's disease and early childhood autism diagnosis, respectively.
步态分析早已被用于各种医疗保健评估[1]。在骨科和义肢中,步态分析对于识别病理和评估骨科助手或义肢的疗效至关重要。例如,通常用于肌肉疾病(如脑瘫、脊髓损伤、肌肉萎缩症等)患者的踝足矫形器(AFOs)预防挛缩的效果尚不清楚[2]。膝关节手术后的恢复和康复研究表明,以膝关节角度为重点的步态分析是评估治疗效果的关键。在老年人医疗保健中,步态分析在跌倒风险和预防的研究中也发挥了重要作用[3]。即使在认知和神经心理学研究中,步态分析也成为一个重要的参数,因为人类的认知技能和运动功能之间有着密切的关系。例如[4]和[5]分别显示了步态分析在帕金森病和儿童早期自闭症诊断中的研究价值。
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引用次数: 15
Wireless monitoring of a distributed environmental health intervention in Haiti 海地分布式环境卫生干预的无线监测
D. Holstius, Jofish Kaye, E. Seto
Each year, countless children die in underdeveloped countries as a result of water-borne illness. We present a prototype system, currently in pilot testing by a Haiti-based NGO, that supports increased transparency and scalability for data assimilation efforts in the context of a distributed water sanitation project. Wireless technologies such as Short Messaging System (SMS) and Near Field Communication (NFC) are integral to the system. Due to the use of low-cost, off-the-shelf mobile hardware and open-source software, and the selection of SMS for the network transport layer, the system is affordable yet reliable enough to be deployed where power and connectivity may be extremely intermittent. This includes regions such as rural Haiti, home to over half the country's population, as well as disaster areas, such as Port-au-Prince, the urban epicenter of a recent devastating earthquake---both locations where the system is currently being deployed.
在不发达国家,每年有无数儿童死于水传播疾病。我们提出了一个原型系统,目前正在由海地的一个非政府组织进行试点测试,该系统支持在分布式水卫生项目背景下提高数据同化工作的透明度和可扩展性。无线技术,如短消息系统(SMS)和近场通信(NFC)是不可或缺的系统。由于使用了低成本、现成的移动硬件和开源软件,并为网络传输层选择了SMS,该系统价格合理,但足够可靠,可以部署在电力和连接可能非常断断续续的地方。这包括海地农村等地区,海地一半以上的人口居住在农村地区,以及太子港等灾区,太子港是最近一次毁灭性地震的城市震中,这两个地区目前都在部署该系统。
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引用次数: 2
An intelligent and portable ambulatory medical toolkit for automatic detection and assessment of traumatic brain injuries 一种用于自动检测和评估创伤性脑损伤的智能便携式门诊医疗工具包
P. Ganapathy, Shantanu H. Joshi, J. Yadegar, Niranjan Kamat, C. Caluser
We propose to develop a portable, handheld, noninvasive solution for accurate screening and real-time monitoring of traumatic brain injury (BI) in ambulatory/emergency response scenarios. A layered sensing concept that unifies alternate modalities such as a) ultrasound (US), b) near infrared spectroscopy (NIRS), c) tonometry (IOP), to predict BI, their severity and mode of recommendations for emergency medical service (EMS) personnel is offered. Specifically, we aim to determine i) novel 3D morphometric parameters of optic nerve sheath that can predict elevated intracranial pressure from US data, ii) incidence of intracranial hematomas using NIRS, iii) intraocular pressure using a tonometer, iv) cerebral blood flow and blood oxygen content using other auxiliary non-invasive sensing modes and v) finally provide a sensor fused outcome of all i)-iv) combined. This decision-support system (DSS) will improve BI detection by incorporating accurate on-site measurements that accounts for individual baseline variations and monitors temporal manifestation of the injury. The data collected and the preliminary analysis performed by the DSS will be sent to an emergency department (ED) physician stationed at a nearby trauma center via a wireless 3G network. Based on the available bandwidth, either all the data including the preliminary analysis (US video, images, 1D measurements, etc) or only the refined signals (feature vector extracted during screening) along with the DSS diagnosis will be sent to the physician. If the DSS determined output is agreeable to the physician then the screening can be terminated and the physician/ED staff can prepare to perform advanced interventions (intubation, cerebralspinal fluid (CSF) drainage, etc). If not, the on-call physician can inform the medic to repeat the scans/take additional measurements to obtain a more concrete outcome via the DSS. In summary, such a knowledge-driven system will equip a novice or a trained medic with an easy-to-use tool to detect traumatic BI and reduce the diagnosis time involved (i.e., computed tomography (CT) scan, clinical evaluation) in ED before performing advanced interventions and thereby improve the prognosis.
我们建议开发一种便携式、手持式、无创的解决方案,用于在门诊/应急响应场景中准确筛查和实时监测创伤性脑损伤(BI)。提出了一种分层传感概念,统一了A)超声(US)、b)近红外光谱(NIRS)、c)血压计(IOP)等替代模式,以预测BI、BI的严重程度和紧急医疗服务(EMS)人员的建议模式。具体来说,我们的目标是确定i)新的视神经鞘三维形态测量参数,该参数可以从US数据中预测颅内压升高,ii)使用近红外光谱(NIRS)预测颅内血肿的发生率,iii)使用血压计测量眼压,iv)使用其他辅助非侵入性传感模式测量脑血流量和血氧含量,v)最后提供所有i)-iv)组合的传感器融合结果。该决策支持系统(DSS)将通过结合准确的现场测量来改善BI检测,该测量可以解释个体基线变化并监测损伤的时间表现。DSS收集的数据和进行的初步分析将通过无线3G网络发送给驻扎在附近创伤中心的急诊科(ED)医生。根据可用带宽,所有数据(包括初步分析(美国视频,图像,一维测量等)或仅精炼信号(筛选过程中提取的特征向量)将与DSS诊断一起发送给医生。如果DSS确定的输出是医生同意的,那么筛查可以终止,医生/急诊科工作人员可以准备进行高级干预(插管,脑脊液(CSF)引流等)。如果没有,值班医生可以通知医生重复扫描/采取额外的测量,以通过DSS获得更具体的结果。总之,这样一个知识驱动的系统将为新手或训练有素的医生提供一个易于使用的工具来检测创伤性BI,并在进行高级干预之前减少ED的诊断时间(即计算机断层扫描(CT)扫描,临床评估),从而改善预后。
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引用次数: 4
A wireless biomedical instrument for evidence-based tissue wound characterization 一种基于证据的组织伤口特征的无线生物医学仪器
Frank Wang, Yeung Lam, A. Mehrnia, B. Bates-Jensen, M. Sarrafzadeh, W. Kaiser
Patients' skin integrity has long been an issue of concern in nursing homes and hospitals. Overall incidence of pressure ulcers for hospitalized patients is as high as 50%. The estimated cost of treating pressure ulcers ranges from $5,000 to $40,000 for each ulcer, depending on severity. A smart compact capacitive sensing wireless handheld system is presented which measures Sub-Epidermal Moisture (SEM) as a mean to detect and monitor early symptoms of ulcer development. The system was successfully verified in trials with 30 volunteers and is currently deployed for clinical trials in four nursing homes.
长期以来,患者的皮肤完整性一直是养老院和医院关注的问题。住院患者压疮的总发生率高达50%。根据严重程度的不同,治疗压疮的费用估计在每个溃疡5000美元到40000美元之间。介绍了一种智能紧凑型电容传感无线手持系统,该系统测量表皮下水分(SEM),作为检测和监测溃疡发展早期症状的手段。该系统在30名志愿者的试验中得到了成功验证,目前正在4家养老院进行临床试验。
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引用次数: 7
Just-in-time sampling and pre-filtering for wearable physiological sensors: going from days to weeks of operation on a single charge 可穿戴式生理传感器的实时采样和预滤波:一次充电可运行数天至数周
Nan Hua, Ashwin Lall, J. Romberg, Jun Xu, M. al’Absi, Emre Ertin, Santosh Kumar, Shikhar Suri
Continuous monitoring of human physiology and behavior in natural environments via unobtrusively wearable wireless sensors is witnessing rapid adoption in both consumer health-care and in scientific studies, since those portable and long-running devices can provide critical information for diagnosis and early prevention of disease, as well as invaluable data for scientific studies. Due to the requirement of continuous monitoring, these sensors, all operating on small wearable batteries, require frequent recharging. Lowering this recharging burden is essential for their widespread adoption. In this paper we explore mechanisms for significantly enhancing the lifetime of these wearable sensors at the cost of a small loss in their sensing accuracy. We propose two ideas that build upon our observation that collecting bursts of samples over short periods of time is sufficient to capture the most interesting and informative part of the signal. In the first part of this paper, we propose a general methodology for reconstructing bandlimited signals accurately from such short bursts of samples. While this reconstruction task is in nature an ill-conditioned problem, we show that the insertion of an analog "modulated pre-filter" hardware module before the ADC can almost surely alleviate this conditioning problem. In the second part of this paper, we describe just-in-time sampling, which by sampling in short bursts at the "right" times, can accurately track R-wave peaks in ECG signals. Using simulations on publicly available traces as well as self-collected data, we show the efficacy of this technique.
通过不显眼的可穿戴无线传感器在自然环境中持续监测人的生理和行为,在消费者保健和科学研究中得到迅速采用,因为这些便携式和长期使用的设备可以为诊断和早期预防疾病提供关键信息,并为科学研究提供宝贵数据。由于需要持续监测,这些传感器都是在小型可穿戴电池上运行,需要经常充电。降低充电负担对它们的广泛采用至关重要。在本文中,我们探索了显著提高这些可穿戴传感器寿命的机制,而代价是传感精度的小损失。基于我们的观察,我们提出了两个想法,即在短时间内收集样本的爆发足以捕获信号中最有趣和最有信息的部分。在本文的第一部分中,我们提出了一种从这种短脉冲样本中精确重建带限信号的一般方法。虽然这个重建任务本质上是一个病态问题,但我们表明,在ADC之前插入一个模拟“调制预滤波器”硬件模块几乎可以肯定地缓解这个条件问题。在本文的第二部分,我们描述了即时采样,它通过在“正确”的时间在短脉冲中采样,可以准确地跟踪心电信号中的r波峰值。通过对公开可用的轨迹以及自己收集的数据进行模拟,我们展示了这种技术的有效性。
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引用次数: 6
期刊
Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)
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