Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248717
Karen P. Tang, Sen H. Hirano, K. Cheng, Gillian R. Hayes
Preterm infants have significantly higher rates of functional limitations and are at risk for delays in cognitive, motor, and other skills. In this paper, we present the results of a qualitative design study to understand the needs of these families and their professional caregivers. These findings informed the design of Estrellita, a mobile wellness tool to support caregivers of preterm infants. We discuss several features of Estrellita that are designed to encourage flexible and consistent data monitoring. We also discuss our strategy for evaluating Estrellita in a long-term deployment study.
{"title":"Designing a mobile health tool for preterm infant wellness","authors":"Karen P. Tang, Sen H. Hirano, K. Cheng, Gillian R. Hayes","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248717","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248717","url":null,"abstract":"Preterm infants have significantly higher rates of functional limitations and are at risk for delays in cognitive, motor, and other skills. In this paper, we present the results of a qualitative design study to understand the needs of these families and their professional caregivers. These findings informed the design of Estrellita, a mobile wellness tool to support caregivers of preterm infants. We discuss several features of Estrellita that are designed to encourage flexible and consistent data monitoring. We also discuss our strategy for evaluating Estrellita in a long-term deployment study.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115759865","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248707
Salys Sultan, P. Mohan
This paper presents a user-centered approach taken for a new peer-facilitated mobile self-care application called Mobile DSMS. Mobile DSMS is a mobile application based on a framework for collaborative disease management using mobile technologies. It allows users to form virtual peer-support groups using their cell phones. The paper sets the stage by presenting the different types of peer-support available and explains how the existing remote model can be extended to include interactive features through the use of mobile technologies. A research protocol, comprising individual interviews and a focus group, was conducted using 21 users of the target group. This paper presents the user perceptions of the system's design; what worked and what did not work. It identifies some of the barriers and social implications associated with adoption of this new form of remote self-care support. It concludes by explaining how the outcomes of a forthcoming field study are expected to advance the area of CDM and HCI using mobile devices.
{"title":"Designing a peer-facilitated self-management mobile application: A user-centred approach","authors":"Salys Sultan, P. Mohan","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248707","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248707","url":null,"abstract":"This paper presents a user-centered approach taken for a new peer-facilitated mobile self-care application called Mobile DSMS. Mobile DSMS is a mobile application based on a framework for collaborative disease management using mobile technologies. It allows users to form virtual peer-support groups using their cell phones. The paper sets the stage by presenting the different types of peer-support available and explains how the existing remote model can be extended to include interactive features through the use of mobile technologies. A research protocol, comprising individual interviews and a focus group, was conducted using 21 users of the target group. This paper presents the user perceptions of the system's design; what worked and what did not work. It identifies some of the barriers and social implications associated with adoption of this new form of remote self-care support. It concludes by explaining how the outcomes of a forthcoming field study are expected to advance the area of CDM and HCI using mobile devices.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332370","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248704
R. Cornejo, D. Hernández, J. Favela, M. Tentori, S. Ochoa
Families are increasingly using Social Networking Sites (SNS) to keep in touch. Building upon our prior work and using the results from 6 participatory design sessions, we present the design of two ubiquitous exergames: GuessMyCaption and TakeAPhoto. These games use family memoirs available in SNS and natural interfaces to encourage older adults to exercise. We further describe the implementation of GuessMyCaption and the results of a 5-weeks deployment study with one older adult and 12 relatives. The system maintained the older adult engaged with her exercises while offering new opportunities for online and offline social encounters. We close discussing that the use of natural interfaces and family memorabilia facilitated the adoption of the game and catalyzed family social encounters.
{"title":"Persuading older adults to socialize and exercise through ambient games","authors":"R. Cornejo, D. Hernández, J. Favela, M. Tentori, S. Ochoa","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248704","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248704","url":null,"abstract":"Families are increasingly using Social Networking Sites (SNS) to keep in touch. Building upon our prior work and using the results from 6 participatory design sessions, we present the design of two ubiquitous exergames: GuessMyCaption and TakeAPhoto. These games use family memoirs available in SNS and natural interfaces to encourage older adults to exercise. We further describe the implementation of GuessMyCaption and the results of a 5-weeks deployment study with one older adult and 12 relatives. The system maintained the older adult engaged with her exercises while offering new opportunities for online and offline social encounters. We close discussing that the use of natural interfaces and family memorabilia facilitated the adoption of the game and catalyzed family social encounters.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124933719","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248694
S. Ananthanarayan, K. Siek
Given the world's obesity epidemic and battle with chronic illness, there is a growing body of research that suggests that a moderate physical lifestyle has significant impact on psychological and physical health. Wearable computing has the potential to encourage physical activity by increasing health awareness and persuading change through just-in-time feedback. This form of technology could help individuals manage lifestyle related factors and implement healthy routines. In this paper, we explore the benefits and tradeoffs of current wearable health technologies along with the persuasion methods employed by their designers to motivate healthy behavior change. We also discuss the challenges and limitations of implementing wearable technologies and suggest possible improvements.
{"title":"Persuasive wearable technology design for health and wellness","authors":"S. Ananthanarayan, K. Siek","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248694","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248694","url":null,"abstract":"Given the world's obesity epidemic and battle with chronic illness, there is a growing body of research that suggests that a moderate physical lifestyle has significant impact on psychological and physical health. Wearable computing has the potential to encourage physical activity by increasing health awareness and persuading change through just-in-time feedback. This form of technology could help individuals manage lifestyle related factors and implement healthy routines. In this paper, we explore the benefits and tradeoffs of current wearable health technologies along with the persuasion methods employed by their designers to motivate healthy behavior change. We also discuss the challenges and limitations of implementing wearable technologies and suggest possible improvements.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125099382","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248684
M. McGee-Lennon, A. Smeaton, S. Brewster
Technology for care at home is an important factor in supporting our ageing population. These technologies need to be both accessible and acceptable to a wide variety of users if they are to be taken up and successfully used in people's homes. This paper describes the user-centered co-design and evaluation of a multimodal reminder system for the home deployed on mobile devices. Six co-design sessions (N=25 users) were carried out with groups of older users to investigate the best methods and techniques for configuring reminders and how they should be delivered within the home. Both sketches and implemented prototypes were used to gather qualitative feedback on a variety of interaction features and techniques to find what worked best for an older user group. We present the findings from the sessions in terms of the re-design of a personalisable multimodal reminder system. We also present the co-design process used and go on to discuss the value this method adds to the design and evaluation of home care technologies for older users.
{"title":"Designing home care reminder systems: Lessons learned through co-design with older users","authors":"M. McGee-Lennon, A. Smeaton, S. Brewster","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248684","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248684","url":null,"abstract":"Technology for care at home is an important factor in supporting our ageing population. These technologies need to be both accessible and acceptable to a wide variety of users if they are to be taken up and successfully used in people's homes. This paper describes the user-centered co-design and evaluation of a multimodal reminder system for the home deployed on mobile devices. Six co-design sessions (N=25 users) were carried out with groups of older users to investigate the best methods and techniques for configuring reminders and how they should be delivered within the home. Both sketches and implemented prototypes were used to gather qualitative feedback on a variety of interaction features and techniques to find what worked best for an older user group. We present the findings from the sessions in terms of the re-design of a personalisable multimodal reminder system. We also present the co-design process used and go on to discuss the value this method adds to the design and evaluation of home care technologies for older users.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126714516","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248674
Aleksandra Sarcevic, Nadir Weibel, James Hollan, R. Burd
We conducted a study in a pediatric trauma center to elicit design requirements for the TraumaPen system-a mixed paper-digital interface using a digital pen and a wall display-to support situation awareness during trauma resuscitation. In this paper, we describe the field research that informed the initial system prototype and then present findings from two studies in which the prototype was used to further explore the application area. Our results showed the potential for digital pen technology in supporting teamwork in the dynamic and safety-critical setting of the trauma bay, but also revealed several limitations of this technology. We conclude by discussing challenges and requirements for the use of paper-digital interfaces in assisting fast-paced, collaborative work processes.
{"title":"A paper-digital interface for information capture and display in time-critical medical work","authors":"Aleksandra Sarcevic, Nadir Weibel, James Hollan, R. Burd","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248674","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248674","url":null,"abstract":"We conducted a study in a pediatric trauma center to elicit design requirements for the TraumaPen system-a mixed paper-digital interface using a digital pen and a wall display-to support situation awareness during trauma resuscitation. In this paper, we describe the field research that informed the initial system prototype and then present findings from two studies in which the prototype was used to further explore the application area. Our results showed the potential for digital pen technology in supporting teamwork in the dynamic and safety-critical setting of the trauma bay, but also revealed several limitations of this technology. We conclude by discussing challenges and requirements for the use of paper-digital interfaces in assisting fast-paced, collaborative work processes.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121381039","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248610
P. Yanik, J. Manganelli, J. Merino, Anthony Threatt, J. Brooks, K. Green, I. Walker
Recognition of human gestures is an active area of research integral to the development of intuitive human-machine interfaces for ubiquitous computing and assistive robotics. In particular, such systems are key to effective environmental designs which facilitate aging in place. Typically, gesture recognition takes the form of template matching in which the human participant is expected to emulate a choreographed motion as prescribed by the researchers. The robotic response is then a one-to-one mapping of the template classification to a library of distinct responses. In this paper, we explore a recognition scheme based on the Growing Neural Gas (GNG) algorithm which places no initial constraints on the user to perform gestures in a specific way. Skeletal depth data collected using the Microsoft Kinect sensor is clustered by GNG and used to refine a robotic response associated with the selected GNG reference node. We envision a supervised learning paradigm similar to the training of a service animal in which the response of the robot is seen to converge upon the user's desired response by taking user feedback into account. This paper presents initial results which show that GNG effectively differentiates between gestured commands and that, using automated (policy based) feedback, the system provides improved responses over time.
{"title":"Use of kinect depth data and Growing Neural Gas for gesture based robot control","authors":"P. Yanik, J. Manganelli, J. Merino, Anthony Threatt, J. Brooks, K. Green, I. Walker","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248610","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248610","url":null,"abstract":"Recognition of human gestures is an active area of research integral to the development of intuitive human-machine interfaces for ubiquitous computing and assistive robotics. In particular, such systems are key to effective environmental designs which facilitate aging in place. Typically, gesture recognition takes the form of template matching in which the human participant is expected to emulate a choreographed motion as prescribed by the researchers. The robotic response is then a one-to-one mapping of the template classification to a library of distinct responses. In this paper, we explore a recognition scheme based on the Growing Neural Gas (GNG) algorithm which places no initial constraints on the user to perform gestures in a specific way. Skeletal depth data collected using the Microsoft Kinect sensor is clustered by GNG and used to refine a robotic response associated with the selected GNG reference node. We envision a supervised learning paradigm similar to the training of a service animal in which the response of the robot is seen to converge upon the user's desired response by taking user feedback into account. This paper presents initial results which show that GNG effectively differentiates between gestured commands and that, using automated (policy based) feedback, the system provides improved responses over time.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124480630","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248759
M. Popescu, Benjapon Hotrabhavananda, Michael Moore, M. Skubic
Falling is a common health problem for elderly. It is reported that about 12 million adults 65 and older fall each year in the United States. To address this problem, at the Center for Eldercare and Rehabilitation Technologies in the University of Missouri we are investigating multiple fall detection systems. In this paper, we present an automatic fall detection system called VAMPIR based on a vertical array of multiple passive infrared (PIR) sensors. PIR sensors provide an inexpensive way to recognize human activity based on its infrared signature. To differentiate between falls and other human activities such as walking, sitting on a chair, bending over etc., we used a pattern recognition algorithm based on hidden Markov models (HMM). We obtained encouraging classification results on a pilot dataset that contained 42 falls and multiple non-fall human activities performed by trained stunt actors.
{"title":"VAMPIR- an automatic fall detection system using a vertical PIR sensor array","authors":"M. Popescu, Benjapon Hotrabhavananda, Michael Moore, M. Skubic","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248759","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248759","url":null,"abstract":"Falling is a common health problem for elderly. It is reported that about 12 million adults 65 and older fall each year in the United States. To address this problem, at the Center for Eldercare and Rehabilitation Technologies in the University of Missouri we are investigating multiple fall detection systems. In this paper, we present an automatic fall detection system called VAMPIR based on a vertical array of multiple passive infrared (PIR) sensors. PIR sensors provide an inexpensive way to recognize human activity based on its infrared signature. To differentiate between falls and other human activities such as walking, sitting on a chair, bending over etc., we used a pattern recognition algorithm based on hidden Markov models (HMM). We obtained encouraging classification results on a pilot dataset that contained 42 falls and multiple non-fall human activities performed by trained stunt actors.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134213716","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248670
R. Bouhenguel, I. Mahgoub
Today small, battery-operated electrocardiograph devices, known as Ambulatory Event Monitors, are used to monitor the heart's rhythm and activity. These on-body healthcare devices typically require a long battery life and moreover efficient detection algorithms. They need the ability to automatically assess atrial fibrillation (A-Fib) risk, and detect the onset of A-Fib from EKG recordings for further clinical diagnosis and treatment. The focus of this paper is the design of a real-time early detection algorithm cascaded with an A-Fib risk assessment algorithm. We compare accuracy of machine learning schemes such as J48, Naïve Bayes, and Logistic Regression and choose the best algorithm to classify A-Fib from EKG medical data. Though all three algorithms have similar accuracy, the Logistic Regression model is selected for its easy portability to mobile devices. A-Fib risk factor is used to determine a monitoring schedule where the detection algorithm is triggered by the age dependent A-Fib incidence rate inside a circadian prevalence window. The design may provide a great public health benefit by predicting A-Fib risk and detecting A-Fib in order to prevent strokes and heart attacks. It also shows promising results in helping meet the needs for energy efficient real-time A-Fib monitoring, detecting and reporting.
{"title":"A risk and Incidence Based Atrial Fibrillation Detection Scheme for wearable healthcare computing devices","authors":"R. Bouhenguel, I. Mahgoub","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248670","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248670","url":null,"abstract":"Today small, battery-operated electrocardiograph devices, known as Ambulatory Event Monitors, are used to monitor the heart's rhythm and activity. These on-body healthcare devices typically require a long battery life and moreover efficient detection algorithms. They need the ability to automatically assess atrial fibrillation (A-Fib) risk, and detect the onset of A-Fib from EKG recordings for further clinical diagnosis and treatment. The focus of this paper is the design of a real-time early detection algorithm cascaded with an A-Fib risk assessment algorithm. We compare accuracy of machine learning schemes such as J48, Naïve Bayes, and Logistic Regression and choose the best algorithm to classify A-Fib from EKG medical data. Though all three algorithms have similar accuracy, the Logistic Regression model is selected for its easy portability to mobile devices. A-Fib risk factor is used to determine a monitoring schedule where the detection algorithm is triggered by the age dependent A-Fib incidence rate inside a circadian prevalence window. The design may provide a great public health benefit by predicting A-Fib risk and detecting A-Fib in order to prevent strokes and heart attacks. It also shows promising results in helping meet the needs for energy efficient real-time A-Fib monitoring, detecting and reporting.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132739734","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}
Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.249068
Qiaojun Wang, Kai Zhang, I. Marsic, J. Li, F. Mörchen
Sensor networks provide a concise picture of complex systems and have been widely applied in health care domain. One typical scenario is to deploy sensors at different locations of human body and analyze the sensor measurements collectively to perform diagnosis of diseases. In this work, we are interested in differentiating peripheral arterial disease (PAD) patients from healthy people by monitoring peripheral blood pressure waveforms using electric sensors. PAD is an important cause of heart disease, which causes no significant symptoms until in a late stage. Therefore its early detection is of significant clinical values. Currently, PAD diagnosis either require large equipment or complicated, invasive sensor deployment, which is highly undesired in terms of medical expenses and safety considerations. To solve this problem, we present a novel approach to address the issue of high deployment cost in PAD detection via sensor networks. Assuming we are given many possibilities for sensor placement, each with different deployment cost, our goal is to select a small number of sensors with minimal costs while delivering accurate diagnosis. We solve this problem by treating each sensor as a feature, and designing a budget-constrained feature selection scheme to choose a compact, optimal subset of sensors, inducing very low deployment cost in terms of invasive treatment, while giving competitive classification accuracy compared with state-of-the-art feature selection method.
{"title":"Patient-friendly detection of early peripheral arterial diseases (PAD) by budgeted sensor selection","authors":"Qiaojun Wang, Kai Zhang, I. Marsic, J. Li, F. Mörchen","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.249068","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.249068","url":null,"abstract":"Sensor networks provide a concise picture of complex systems and have been widely applied in health care domain. One typical scenario is to deploy sensors at different locations of human body and analyze the sensor measurements collectively to perform diagnosis of diseases. In this work, we are interested in differentiating peripheral arterial disease (PAD) patients from healthy people by monitoring peripheral blood pressure waveforms using electric sensors. PAD is an important cause of heart disease, which causes no significant symptoms until in a late stage. Therefore its early detection is of significant clinical values. Currently, PAD diagnosis either require large equipment or complicated, invasive sensor deployment, which is highly undesired in terms of medical expenses and safety considerations. To solve this problem, we present a novel approach to address the issue of high deployment cost in PAD detection via sensor networks. Assuming we are given many possibilities for sensor placement, each with different deployment cost, our goal is to select a small number of sensors with minimal costs while delivering accurate diagnosis. We solve this problem by treating each sensor as a feature, and designing a budget-constrained feature selection scheme to choose a compact, optimal subset of sensors, inducing very low deployment cost in terms of invasive treatment, while giving competitive classification accuracy compared with state-of-the-art feature selection method.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133238873","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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