Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)最新文献_第2页

A glanceable mobile avatar for behavior change 一个可浏览的移动化身，用于改变行为

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534093

Tylar Murray, L. Jaimes, E. Hekler, D. Spruijt-Metz, A. Raij

引用次数: 9

Remote patient monitoring: what impact can data analytics have on cost? 远程病人监护:数据分析对成本有什么影响?

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534108

S. Lee, Hassan Ghasemzadeh, B. Mortazavi, M. Lan, N. Alshurafa, Michael K. Ong, M. Sarrafzadeh

{"title":"Remote patient monitoring: what impact can data analytics have on cost?","authors":"S. Lee, Hassan Ghasemzadeh, B. Mortazavi, M. Lan, N. Alshurafa, Michael K. Ong, M. Sarrafzadeh","doi":"10.1145/2534088.2534108","DOIUrl":"https://doi.org/10.1145/2534088.2534108","url":null,"abstract":"While significant effort has been made on designing Remote Monitoring Systems (RMS), limited research has been conducted on the potential cost savings that these systems offer in terms of reduction in readmission costs, as well as the costs associated with human resources involved in the intervention process. This paper is particularly interested in exploring potential cost savings that an analytics engine can provide in presence of intelligent back-end data processing and machine learning algorithms against conventional RMS that operate based on simple thresholding approaches. Using physiological data collected from 486 heart failure patients through a clinical study in collaboration with the UCLA School of Medicine, we conduct a retrospective data analysis to estimate prediction accuracy as well as associated costs of the two remote monitoring approaches. Our results show that analytics-based RMS can reduce false negative rates by 61.4% while maintaining a false positive performance close to that of conventional RMS. Furthermore, the proposed analytics engine achieves 61.5% reduction in the overall readmission costs.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"87 1","pages":"4:1-4:8"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73206602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

Mobile electronic triaging for emergency response improvement through crowdsourced and sensor-detected information 移动电子分诊，通过众包和传感器检测信息改善应急反应

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534089

Liliya I. Besaleva, Alfred C. Weaver

{"title":"Mobile electronic triaging for emergency response improvement through crowdsourced and sensor-detected information","authors":"Liliya I. Besaleva, Alfred C. Weaver","doi":"10.1145/2534088.2534089","DOIUrl":"https://doi.org/10.1145/2534088.2534089","url":null,"abstract":"Emergency resources are often insufficient to satisfy fully the demands for professional help and supplies after a public disaster. Furthermore, in a mass casualty situation, the emphasis shifts from ensuring the best possible outcome for each individual patient to ensuring the best possible outcome for the greatest number of patients. Historically, various manual and electronic medical triage systems have been used both under civil and military conditions to determine the order and priority of emergency treatment, transport, and best possible destination for the patients [3][4][5]. Unfortunately, none of those solutions has proven flexible, accurate, scalable or unobtrusive enough to meet the public's expectations [1]. We demonstrate a system for real-time patient assessment which uses mobile electronic triaging accomplished via crowdsourced and sensor-detected information. With the use of our system, emergency management professionals receive most of the information they need for preparing themselves to perform a timely and accurate treatment of their patients even before dispatching a response team to the event. During our demonstration, we will show how our system behaves with different combinations of information inputs and compare its resulting outputs with evaluations done by medical experts. The public will be given the chance to participate in real-time demos by posing as victims and providing self-reported information about their health.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"93 1","pages":"10:1-10:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91429987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interactions in an intensive care unit: experiences pre-processing sensor network data 在重症监护病房的互动:经验预处理传感器网络数据

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534105

M. Monsalve, S. Pemmaraju, P. Polgreen

{"title":"Interactions in an intensive care unit: experiences pre-processing sensor network data","authors":"M. Monsalve, S. Pemmaraju, P. Polgreen","doi":"10.1145/2534088.2534105","DOIUrl":"https://doi.org/10.1145/2534088.2534105","url":null,"abstract":"Healthcare-associated infections (HAIs) represent a significant burden to healthcare provision; in the United States alone, it is estimated that approximately 2 million patients acquire HAIs each year. As part of a larger effort to understand how HAIs spread, we deployed a wireless sensor network in the Medical Intensive Care Unit of the University of Iowa Hospitals and Clinics. We used data reported by the network to estimate healthcare worker movement, interactions between healthcare workers, and adherence to hand sanitization policies.\u0000 Our experiment joins the growing yet still small collection of sensor network deployments in healthcare settings. This work contributes to this body of research by presenting a comprehensive approach to pre-processing the collected sensor data, thereby reducing errors and increasing robustness. We provide two main contributions: (i) a simple and theoretically sound calibration method for sensor signals that eliminates biases in pairwise sensor communication and (ii) filters that increase the reliability of signal strength from stationary sensors. We validate our methods by comparing visits of healthcare workers to rooms, as discovered from the sensor data, to ground truth room occupancy data collected in notes.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"25 1","pages":"5:1-5:8"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78197206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Accurate energy expenditure estimation using smartphone sensors 使用智能手机传感器进行准确的能量消耗估算

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534099

A. Pande, Yunze Zeng, Aveek K. Das, P. Mohapatra, S. Miyamoto, E. Seto, E. Henricson, Jay J. Han

引用次数: 4

Wireless multi sensor bracelet with discreet feedback 无线多传感器手镯与谨慎的反馈

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534104

M. Ouwerkerk, Pierre Dandine, D. Bolio, Rafal Kocielnik, J. Mercurio, H. Huijgen, J. Westerink

引用次数: 20

A low power and convenient bio-impedance monitor, and its application to respiration monitoring 一种低功耗、方便的生物阻抗监测仪，及其在呼吸监测中的应用

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534091

Seulki Lee, C. Agell, Salvatore Polito, R. Vullers, J. Penders

引用次数: 2

A 16-channel bluetooth enabled wearable EEG platform with dry-contact electrodes for brain computer interface 一种16通道蓝牙可穿戴EEG平台，干接触电极用于脑机接口

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534098

Viswam Nathan, Jian Wu, Chengzhi Zong, Yuan Zou, O. Dehzangi, Mary Reagor, R. Jafari

引用次数: 12

AsthmaGuru: a framework to improve adherence to asthma medication AsthmaGuru:一个改善哮喘药物依从性的框架

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534102

R. Biswas, Peter Chang, H. Dharmasiri, G. Patel, A. Sabharwal

引用次数: 2

Feasibility of personalized nonparametric analytics for predictive monitoring of heart failure patients using continuous mobile telemetry 使用连续移动遥测技术对心力衰竭患者进行预测性监测的个性化非参数分析的可行性

Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference) Pub Date : 2013-11-01 DOI: 10.1145/2534088.2534107

R. M. Pipke, S. Wegerich, A. Saidi, J. Stehlik

引用次数: 6