Tylar Murray, L. Jaimes, E. Hekler, D. Spruijt-Metz, A. Raij
We present a mobile avatar system designed to provide a constant user-avatar interface for health behavior change therapy. The presented Android application replaces the user's phone background with an animated avatar. The avatar's level of physical activity is made to match the physical activity level of the user. This activity level is inferred using a decision-tree-based frequency analysis of the built-in phone accelerometers. User physical activity data collected is also sent via a mobile analytics platform (Countly) to be stored in a server. Also included in our demo is a simple website which pulls information from this server and places a user's avatar among other people's avatars. In this display a user can see how their avatar's physical activity compares to others', and observe their real-life physical activity behavior directly impacting the performance of their avatar in the virtual world.
{"title":"A glanceable mobile avatar for behavior change","authors":"Tylar Murray, L. Jaimes, E. Hekler, D. Spruijt-Metz, A. Raij","doi":"10.1145/2534088.2534093","DOIUrl":"https://doi.org/10.1145/2534088.2534093","url":null,"abstract":"We present a mobile avatar system designed to provide a constant user-avatar interface for health behavior change therapy. The presented Android application replaces the user's phone background with an animated avatar. The avatar's level of physical activity is made to match the physical activity level of the user. This activity level is inferred using a decision-tree-based frequency analysis of the built-in phone accelerometers. User physical activity data collected is also sent via a mobile analytics platform (Countly) to be stored in a server. Also included in our demo is a simple website which pulls information from this server and places a user's avatar among other people's avatars. In this display a user can see how their avatar's physical activity compares to others', and observe their real-life physical activity behavior directly impacting the performance of their avatar in the virtual world.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"605 1","pages":"16:1-16:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77450767","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}
S. Lee, Hassan Ghasemzadeh, B. Mortazavi, M. Lan, N. Alshurafa, Michael K. Ong, M. Sarrafzadeh
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.
{"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}
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.
{"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}
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. 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.
{"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}
A. Pande, Yunze Zeng, Aveek K. Das, P. Mohapatra, S. Miyamoto, E. Seto, E. Henricson, Jay J. Han
Accurate and online Energy Expenditure Estimation (EEE) utilizing small wearable sensors is a difficult task with most existing schemes. In this work, we focus on accurate EEE for tracking ambulatory activities of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE. Using Artificial Neural Networks, a machine learning technique, a generic regression model for EEE is built that yields upto 83% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 10%). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band).
{"title":"Accurate energy expenditure estimation using smartphone sensors","authors":"A. Pande, Yunze Zeng, Aveek K. Das, P. Mohapatra, S. Miyamoto, E. Seto, E. Henricson, Jay J. Han","doi":"10.1145/2534088.2534099","DOIUrl":"https://doi.org/10.1145/2534088.2534099","url":null,"abstract":"Accurate and online Energy Expenditure Estimation (EEE) utilizing small wearable sensors is a difficult task with most existing schemes. In this work, we focus on accurate EEE for tracking ambulatory activities of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE. Using Artificial Neural Networks, a machine learning technique, a generic regression model for EEE is built that yields upto 83% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 10%). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band).","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"30 1","pages":"19:1-19:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73064135","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}
M. Ouwerkerk, Pierre Dandine, D. Bolio, Rafal Kocielnik, J. Mercurio, H. Huijgen, J. Westerink
A novel wireless multi sensor bracelet has been developed. The design choices of the bracelet - based on insights obtained with a predecessor sensor bracelet -, as well as the rationale for the choice of sensors, are presented. The hardware and software architecture are described. An example of obtained sensor data is shown. The limited battery life of the performance optimized product software fell short of the one week design target. A power optimization of the software has been made, which meets the battery life design target. It is based on current consumption measurements, and optimized sensor timing. The tradeoffs between high performance - short battery life, and low performance - long battery life are analyzed. The learnings from recent field studies on work-related stress and affective health are discussed.
{"title":"Wireless multi sensor bracelet with discreet feedback","authors":"M. Ouwerkerk, Pierre Dandine, D. Bolio, Rafal Kocielnik, J. Mercurio, H. Huijgen, J. Westerink","doi":"10.1145/2534088.2534104","DOIUrl":"https://doi.org/10.1145/2534088.2534104","url":null,"abstract":"A novel wireless multi sensor bracelet has been developed. The design choices of the bracelet - based on insights obtained with a predecessor sensor bracelet -, as well as the rationale for the choice of sensors, are presented. The hardware and software architecture are described. An example of obtained sensor data is shown. The limited battery life of the performance optimized product software fell short of the one week design target. A power optimization of the software has been made, which meets the battery life design target. It is based on current consumption measurements, and optimized sensor timing. The tradeoffs between high performance - short battery life, and low performance - long battery life are analyzed. The learnings from recent field studies on work-related stress and affective health are discussed.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"35 1","pages":"6:1-6:8"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81551804","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}
Seulki Lee, C. Agell, Salvatore Polito, R. Vullers, J. Penders
A low power and convenient bio-impedance monitor, which relies on a proprietary ASIC to achieve low power performance, is shown. It can be used in several bio-impedance applications, especially in continuous and wearable applications thanks to its compact form factor and long battery life time. In this paper, we demonstrate its performance for respiration monitoring. The result is compared with that of the reference system, showing a high correlation factor of 0.91.
{"title":"A low power and convenient bio-impedance monitor, and its application to respiration monitoring","authors":"Seulki Lee, C. Agell, Salvatore Polito, R. Vullers, J. Penders","doi":"10.1145/2534088.2534091","DOIUrl":"https://doi.org/10.1145/2534088.2534091","url":null,"abstract":"A low power and convenient bio-impedance monitor, which relies on a proprietary ASIC to achieve low power performance, is shown. It can be used in several bio-impedance applications, especially in continuous and wearable applications thanks to its compact form factor and long battery life time. In this paper, we demonstrate its performance for respiration monitoring. The result is compared with that of the reference system, showing a high correlation factor of 0.91.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"25 3 1","pages":"13:1-13:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88286293","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}
Viswam Nathan, Jian Wu, Chengzhi Zong, Yuan Zou, O. Dehzangi, Mary Reagor, R. Jafari
A mobile, easy to use, wireless dry contact EEG acquisition system is presented in this work. This system can potentially facilitate continuous in-home monitoring of electroencephalography (EEG) to diagnose ailments such as epilepsy. The system has also been validated with brain computer interface (BCI) paradigms that would enable physically disabled users to communicate.
{"title":"A 16-channel bluetooth enabled wearable EEG platform with dry-contact electrodes for brain computer interface","authors":"Viswam Nathan, Jian Wu, Chengzhi Zong, Yuan Zou, O. Dehzangi, Mary Reagor, R. Jafari","doi":"10.1145/2534088.2534098","DOIUrl":"https://doi.org/10.1145/2534088.2534098","url":null,"abstract":"A mobile, easy to use, wireless dry contact EEG acquisition system is presented in this work. This system can potentially facilitate continuous in-home monitoring of electroencephalography (EEG) to diagnose ailments such as epilepsy. The system has also been validated with brain computer interface (BCI) paradigms that would enable physically disabled users to communicate.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"2 1","pages":"17:1-17:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86152356","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}
R. Biswas, Peter Chang, H. Dharmasiri, G. Patel, A. Sabharwal
Asthma is a widespread chronic disease. Poor management of Asthma results in a large number of hospitalizations each year, the majority of which are avoidable through strict adherence to medication. AsthmaGuru is a system which aims to provide personalized guidance to users on their health state, with an aim to improve their compliance to medication. To achieve this aim, AsthmaGuru aggregates three forms of data: (a) automated and unobtrusive measurement of medication adherence using a low-power portable electronic attachment to an inhaler, (b) lung function measurement based on portable spirometry and (c) local air quality metrics. We leverage a custom low-power hardware platform for augmenting the inhalers and spirometry and develop a custom Android API for delay-tolerant data collection.
{"title":"AsthmaGuru: a framework to improve adherence to asthma medication","authors":"R. Biswas, Peter Chang, H. Dharmasiri, G. Patel, A. Sabharwal","doi":"10.1145/2534088.2534102","DOIUrl":"https://doi.org/10.1145/2534088.2534102","url":null,"abstract":"Asthma is a widespread chronic disease. Poor management of Asthma results in a large number of hospitalizations each year, the majority of which are avoidable through strict adherence to medication. AsthmaGuru is a system which aims to provide personalized guidance to users on their health state, with an aim to improve their compliance to medication. To achieve this aim, AsthmaGuru aggregates three forms of data: (a) automated and unobtrusive measurement of medication adherence using a low-power portable electronic attachment to an inhaler, (b) lung function measurement based on portable spirometry and (c) local air quality metrics. We leverage a custom low-power hardware platform for augmenting the inhalers and spirometry and develop a custom Android API for delay-tolerant data collection.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"8 1","pages":"11:1-11:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74522572","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}
Nonparametric model-based analytics personalized to the physiology of each patient are investigated for predictive monitoring of exacerbation in heart failure patients at home. Multivariate vital sign data are provided by means of continuous bio-signal acquisition with a mobile phone-based wearable sensor system worn by patients for several hours a day in the home ambulatory environment. Perturbation analysis demonstrates that individual patient physiological behavior is indeed effectively learned by the analytics, with high sensitivity to changes in physiological dynamics. Comparison of the analytics results with absence of unplanned medical events and self-reported wellness during regular patient follow-up demonstrate a very low false alert burden, suggesting this approach is efficient for remote clinical surveillance.
{"title":"Feasibility of personalized nonparametric analytics for predictive monitoring of heart failure patients using continuous mobile telemetry","authors":"R. M. Pipke, S. Wegerich, A. Saidi, J. Stehlik","doi":"10.1145/2534088.2534107","DOIUrl":"https://doi.org/10.1145/2534088.2534107","url":null,"abstract":"Nonparametric model-based analytics personalized to the physiology of each patient are investigated for predictive monitoring of exacerbation in heart failure patients at home. Multivariate vital sign data are provided by means of continuous bio-signal acquisition with a mobile phone-based wearable sensor system worn by patients for several hours a day in the home ambulatory environment. Perturbation analysis demonstrates that individual patient physiological behavior is indeed effectively learned by the analytics, with high sensitivity to changes in physiological dynamics. Comparison of the analytics results with absence of unplanned medical events and self-reported wellness during regular patient follow-up demonstrate a very low false alert burden, suggesting this approach is efficient for remote clinical surveillance.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"37 1","pages":"7:1-7:8"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87454659","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}
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