R. Hervás, David Ruiz-Carrasco, Tania Mondéjar, J. Bravo
In the last few years, gamification has been proven as an effective strategy to improve people's motivation and performance. Many authors have reported success examples of gamification in areas such as education, entertainment, health and business. This paper is focused on the use of gamification for health, specifically for the promotion of behavioral changes. Firstly, this paper describes a systematic review conducted to identify in the literature the gamification elements that are being used to promote behavioral change. The results of this systematic review evidence the broad terminology related to gamification elements, with different perspectives and levels of abstraction. Based on these results, a taxonomy for gamification mechanics has been proposed. The taxonomy identifies and classifies the most common gamification mechanics and relates them with psychological fundamentals on behavioral changes.
{"title":"Gamification mechanics for behavioral change: a systematic review and proposed taxonomy","authors":"R. Hervás, David Ruiz-Carrasco, Tania Mondéjar, J. Bravo","doi":"10.1145/3154862.3154939","DOIUrl":"https://doi.org/10.1145/3154862.3154939","url":null,"abstract":"In the last few years, gamification has been proven as an effective strategy to improve people's motivation and performance. Many authors have reported success examples of gamification in areas such as education, entertainment, health and business. This paper is focused on the use of gamification for health, specifically for the promotion of behavioral changes. Firstly, this paper describes a systematic review conducted to identify in the literature the gamification elements that are being used to promote behavioral change. The results of this systematic review evidence the broad terminology related to gamification elements, with different perspectives and levels of abstraction. Based on these results, a taxonomy for gamification mechanics has been proposed. The taxonomy identifies and classifies the most common gamification mechanics and relates them with psychological fundamentals on behavioral changes.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"82 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113961382","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}
Chris Baber, A. Khattab, J. Hermsdörfer, A. Wing, M. Russell
We explore the ways in which smart objects can be used to cue actions as part of coaching for Activities of Daily Living (ADL) following brain damage or injury, such as might arise following a stroke. In this case, appropriate actions are cued for a given context. The context is defined by the intention of the users, the state of the objects and the tasks for which these objects can be used. This requires objects to be instrumented so that they can recognize the actions that users perform. In order to provide appropriate cues, the objects also need to be able to display information to users, e.g., by changing their physical appearance or by providing auditory output. We discuss the ways in which information can be displayed to cue user action.
{"title":"Coaching through smart objects","authors":"Chris Baber, A. Khattab, J. Hermsdörfer, A. Wing, M. Russell","doi":"10.1145/3154862.3154938","DOIUrl":"https://doi.org/10.1145/3154862.3154938","url":null,"abstract":"We explore the ways in which smart objects can be used to cue actions as part of coaching for Activities of Daily Living (ADL) following brain damage or injury, such as might arise following a stroke. In this case, appropriate actions are cued for a given context. The context is defined by the intention of the users, the state of the objects and the tasks for which these objects can be used. This requires objects to be instrumented so that they can recognize the actions that users perform. In order to provide appropriate cues, the objects also need to be able to display information to users, e.g., by changing their physical appearance or by providing auditory output. We discuss the ways in which information can be displayed to cue user action.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131687483","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}
Gabriel Lugo Bustillo, M. Ibarra-Manzano, F. Ba, I. Cheng
In this paper, we take advantage of the free hand interaction technology as a medical tool, either in rehabilitation centers or at home, that allows the evaluation of patients with Parkinson's. We have created a virtual reality scene to engage the patient to feel in an activity that can be found in daily life, and use the Leap Motion controller tracking to evaluate and classify the tremor in the hands. A sample of 33 patients diagnosed with Parkinson's disease (PD) participated in the study. Three tests were performed per patient, the first two to evaluate the amplitude of the postural tremor in each hand, and the third to measure the time to complete a specific task. Analysis shows that our tool can be used effectively to classify the stage of Parkinson's disease.
{"title":"Virtual reality and hand tracking system as a medical tool to evaluate patients with parkinson's","authors":"Gabriel Lugo Bustillo, M. Ibarra-Manzano, F. Ba, I. Cheng","doi":"10.1145/3154862.3154924","DOIUrl":"https://doi.org/10.1145/3154862.3154924","url":null,"abstract":"In this paper, we take advantage of the free hand interaction technology as a medical tool, either in rehabilitation centers or at home, that allows the evaluation of patients with Parkinson's. We have created a virtual reality scene to engage the patient to feel in an activity that can be found in daily life, and use the Leap Motion controller tracking to evaluate and classify the tremor in the hands. A sample of 33 patients diagnosed with Parkinson's disease (PD) participated in the study. Three tests were performed per patient, the first two to evaluate the amplitude of the postural tremor in each hand, and the third to measure the time to complete a specific task. Analysis shows that our tool can be used effectively to classify the stage of Parkinson's disease.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124568","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}
Evidence-based health promotion programs implement clinical practice guidelines built upon results of clinical trials with a definite number of participants, collected during a specific period of time. Wearable technologies allow for continuous observation of wellness parameters of multiple citizens, combined with monitoring of activities and context parameters involved in citizens' wellness. A statistical inference model can describe the relation between multidimensional activities and context parameters, the wellness of an individual and a comparable reference group, utilizing machine learning techniques and knowledge from continuous observations of multiple citizens. This paper presents a holistic concept of a coach system, namely eCoach, that combines specialized medical evidence available from randomized control trials, with individual and reference knowledge to create and reinforce wellness-based recommendations. The eCoach adapts these recommendations in a continuous personalized coaching dialog addressing citizen's needs and preferences.
{"title":"Conceptualization of a personalized ecoach for wellness promotion","authors":"Martin W. Gerdes, S. Martinez, D. Tjondronegoro","doi":"10.1145/3154862.3154930","DOIUrl":"https://doi.org/10.1145/3154862.3154930","url":null,"abstract":"Evidence-based health promotion programs implement clinical practice guidelines built upon results of clinical trials with a definite number of participants, collected during a specific period of time. Wearable technologies allow for continuous observation of wellness parameters of multiple citizens, combined with monitoring of activities and context parameters involved in citizens' wellness. A statistical inference model can describe the relation between multidimensional activities and context parameters, the wellness of an individual and a comparable reference group, utilizing machine learning techniques and knowledge from continuous observations of multiple citizens. This paper presents a holistic concept of a coach system, namely eCoach, that combines specialized medical evidence available from randomized control trials, with individual and reference knowledge to create and reinforce wellness-based recommendations. The eCoach adapts these recommendations in a continuous personalized coaching dialog addressing citizen's needs and preferences.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124778620","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}
In the area of advanced human-computer interaction, automatic gesture recognition is an important field. Motion data produced by the accelerometer of a smart watch can be utilized in hand gesture recognition. In this work we examine the use of a commodity smart watch and a smartphone as the capture and the processing units respectively, for recognizing gestures. We claim that if the proper gesture recognition algorithms are applied, the recognition of natural gestures i.e. 3-D gestures easily performed by an individual can be accurate enough to be useful in everyday life activities. Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) methodologies are utilized in this context and evaluated using a set of six 3-D natural gestures.
{"title":"Gesture recognition using symbolic aggregate approximation and dynamic time warping on motion data","authors":"A. Mezari, Ilias Maglogiannis","doi":"10.1145/3154862.3154927","DOIUrl":"https://doi.org/10.1145/3154862.3154927","url":null,"abstract":"In the area of advanced human-computer interaction, automatic gesture recognition is an important field. Motion data produced by the accelerometer of a smart watch can be utilized in hand gesture recognition. In this work we examine the use of a commodity smart watch and a smartphone as the capture and the processing units respectively, for recognizing gestures. We claim that if the proper gesture recognition algorithms are applied, the recognition of natural gestures i.e. 3-D gestures easily performed by an individual can be accurate enough to be useful in everyday life activities. Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) methodologies are utilized in this context and evaluated using a set of six 3-D natural gestures.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396119","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}
Physical activity yields affective benefits like mood improvement and a sense of accomplishment or a general sense of feeling good. However, existing interventions to promote physical activity typically do not make tracking or visualization of affective benefits a prominent part of the interface. We conducted a survey asking people about physical activity episodes that made them feel good and the impact of those episodes on their exercise intentions. We found that the affective benefits of exercise motivated respondents to become more active. In this paper, we report on the affective benefits that resulted from exercise, what users perceived as causing those affective benefits, and what impact feeling good from being active had on their intentions for future exercise. We discuss the implications of our findings for the design of interventions that use affective benefits to promote physical activity.
{"title":"\"Move into another world of happy\": insights for designing affect-based physical activity interventions","authors":"Sonali R. Mishra, P. Klasnja","doi":"10.1145/3154862.3154880","DOIUrl":"https://doi.org/10.1145/3154862.3154880","url":null,"abstract":"Physical activity yields affective benefits like mood improvement and a sense of accomplishment or a general sense of feeling good. However, existing interventions to promote physical activity typically do not make tracking or visualization of affective benefits a prominent part of the interface. We conducted a survey asking people about physical activity episodes that made them feel good and the impact of those episodes on their exercise intentions. We found that the affective benefits of exercise motivated respondents to become more active. In this paper, we report on the affective benefits that resulted from exercise, what users perceived as causing those affective benefits, and what impact feeling good from being active had on their intentions for future exercise. We discuss the implications of our findings for the design of interventions that use affective benefits to promote physical activity.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130907875","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}
Ayse G. Büyüktür, M. Ackerman, Mark W. Newman, Pei-Yao Hung
Self-care in Spinal Cord Injury (SCI) is highly complex and individualized. Patients struggle to adapt to life with SCI, especially when they go home after rehabilitation. We conducted a field study to understand how self-care plans work for patients in their lived experience and what requirements there might be for an augmentative system. We found that patients develop their own self-care plans over time, and that routinization plays a key role in SCI self-care. Importantly, self-care activities exist in different states of routinization that have implications for the technological support that should be provided. Our findings suggest that self-care can be supported by different types of semi-automated tracking that account for the different routinization of activities, the collaborative nature of care, and the life-long, dynamic nature of this condition. The findings from our study also extend recent guidelines for semi-automated tracking in health.
{"title":"Design considerations for semi-automated tracking: self-care plans in spinal cord injury","authors":"Ayse G. Büyüktür, M. Ackerman, Mark W. Newman, Pei-Yao Hung","doi":"10.1145/3154862.3154870","DOIUrl":"https://doi.org/10.1145/3154862.3154870","url":null,"abstract":"Self-care in Spinal Cord Injury (SCI) is highly complex and individualized. Patients struggle to adapt to life with SCI, especially when they go home after rehabilitation. We conducted a field study to understand how self-care plans work for patients in their lived experience and what requirements there might be for an augmentative system. We found that patients develop their own self-care plans over time, and that routinization plays a key role in SCI self-care. Importantly, self-care activities exist in different states of routinization that have implications for the technological support that should be provided. Our findings suggest that self-care can be supported by different types of semi-automated tracking that account for the different routinization of activities, the collaborative nature of care, and the life-long, dynamic nature of this condition. The findings from our study also extend recent guidelines for semi-automated tracking in health.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125016047","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}
Joint angles are commonly measured in physical rehabilitation to evaluate joint function. Evidences showed that wearable inertial sensors can accurately quantify human motion information, however, the most advanced and accurate methodologies require the execution of complex calibration movements which are unsuitable to inexpert users and inadequate for a home context. This way, four different joint angles estimation methods requiring no calibration movement were developed in order to track the main human body joint angles in real time. IMUs mounted in bracelets were used to restrict sensor positioning on the limbs. For six different exercises, the estimated absolute and relative joint angles were evaluated against the marker-based video tracking software Kinovea ground-truth. Correlation analysis between estimated and ground-truth joint angles indicated a very strong and statistically significant correlation. The average error in estimated joint angles is below 5 degrees for all four methods employed, which may be an acceptable result for the rehabilitation at home scenario.
{"title":"Joint angles tracking for rehabilitation at home using inertial sensors: a feasibility study","authors":"Ana Pereira, V. Guimarães, I. Sousa","doi":"10.1145/3154862.3154888","DOIUrl":"https://doi.org/10.1145/3154862.3154888","url":null,"abstract":"Joint angles are commonly measured in physical rehabilitation to evaluate joint function. Evidences showed that wearable inertial sensors can accurately quantify human motion information, however, the most advanced and accurate methodologies require the execution of complex calibration movements which are unsuitable to inexpert users and inadequate for a home context. This way, four different joint angles estimation methods requiring no calibration movement were developed in order to track the main human body joint angles in real time. IMUs mounted in bracelets were used to restrict sensor positioning on the limbs. For six different exercises, the estimated absolute and relative joint angles were evaluated against the marker-based video tracking software Kinovea ground-truth. Correlation analysis between estimated and ground-truth joint angles indicated a very strong and statistically significant correlation. The average error in estimated joint angles is below 5 degrees for all four methods employed, which may be an acceptable result for the rehabilitation at home scenario.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116919531","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}
Josh Cherian, Vijay Rajanna, Daniel W. Goldberg, T. Hammond
Failing to brush one's teeth regularly can have surprisingly serious health consequences, from periodontal disease to coronary heart disease to pancreatic cancer. This problem is especially worrying when caring for the elderly and/or individuals with dementia, as they often forget or are unable to perform standard health activities such as brushing their teeth, washing their hands, and taking medication. To ensure that such individuals are correctly looked after they are placed under the supervision of caretakers or family members, simultaneously limiting their independence and placing an immense burden on their family members and caretakers. To address this problem we developed a non-invasive wearable system based on a wrist-mounted accelerometer to accurately identify when a person brushed their teeth. We tested the efficacy of our system with a month-long in-the-wild study and achieved an accuracy of 94% and an F-measure of 0.82.
{"title":"Did you remember to brush?: a noninvasive wearable approach to recognizing brushing teeth for elderly care","authors":"Josh Cherian, Vijay Rajanna, Daniel W. Goldberg, T. Hammond","doi":"10.1145/3154862.3154866","DOIUrl":"https://doi.org/10.1145/3154862.3154866","url":null,"abstract":"Failing to brush one's teeth regularly can have surprisingly serious health consequences, from periodontal disease to coronary heart disease to pancreatic cancer. This problem is especially worrying when caring for the elderly and/or individuals with dementia, as they often forget or are unable to perform standard health activities such as brushing their teeth, washing their hands, and taking medication. To ensure that such individuals are correctly looked after they are placed under the supervision of caretakers or family members, simultaneously limiting their independence and placing an immense burden on their family members and caretakers. To address this problem we developed a non-invasive wearable system based on a wrist-mounted accelerometer to accurately identify when a person brushed their teeth. We tested the efficacy of our system with a month-long in-the-wild study and achieved an accuracy of 94% and an F-measure of 0.82.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130365209","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}
Petar Velickovic, N. Lane, S. Bhattacharya, A. Chieh, O. Bellahsen, M. Vegreville
People are naturally sensitive to the sharing of their health data collected by various connected consumer devices (e.g., smart scales, sleep trackers) with third parties. However, sharing this data to compute aggregate statistics and comparisons is a basic building block for a range of medical studies based on large-scale consumer devices; such studies have the potential to transform how we study disease and behavior. Furthermore, informing users as to how their health measurements and activities compare with friends, demographic peers and globally has been shown to be a powerful tool for behavior change and management in individuals. While experienced organizations can safely perform aggregate user health analysis, there is a significant need for new privacy-preserving mechanisms that enable people to engage in the same way even with untrusted third parties (e.g., small/recently established organizations). In this work, we propose a new approach to this problem grounded in the use of deep distributed behavior models. These are discriminative deep learning models that can approximate the calculation of various aggregate functions. Models are bootstrapped with training data from a modestly sized cohort and then distributed directly to personal devices to estimate, for example, how the user (perhaps in terms of daily step counts) ranks/compares to various demographics ranges (like age and sex). Critically, the user's own data now never has to leave the device. We validate this method using a 1.2M-user 22-month dataset that spans body-weight, sleep hours and step counts collected by devices from Nokia Digital Health - Withings. Experiments show our framework remains accurate for a range of commonly used statistical aggregate functions. This result opens a powerful new paradigm for privacy-preserving analytics under which user data largely remains on personal devices, overcoming a variety of potential privacy risks.
{"title":"Scaling health analytics to millions without compromising privacy using deep distributed behavior models","authors":"Petar Velickovic, N. Lane, S. Bhattacharya, A. Chieh, O. Bellahsen, M. Vegreville","doi":"10.1145/3154862.3154873","DOIUrl":"https://doi.org/10.1145/3154862.3154873","url":null,"abstract":"People are naturally sensitive to the sharing of their health data collected by various connected consumer devices (e.g., smart scales, sleep trackers) with third parties. However, sharing this data to compute aggregate statistics and comparisons is a basic building block for a range of medical studies based on large-scale consumer devices; such studies have the potential to transform how we study disease and behavior. Furthermore, informing users as to how their health measurements and activities compare with friends, demographic peers and globally has been shown to be a powerful tool for behavior change and management in individuals. While experienced organizations can safely perform aggregate user health analysis, there is a significant need for new privacy-preserving mechanisms that enable people to engage in the same way even with untrusted third parties (e.g., small/recently established organizations). In this work, we propose a new approach to this problem grounded in the use of deep distributed behavior models. These are discriminative deep learning models that can approximate the calculation of various aggregate functions. Models are bootstrapped with training data from a modestly sized cohort and then distributed directly to personal devices to estimate, for example, how the user (perhaps in terms of daily step counts) ranks/compares to various demographics ranges (like age and sex). Critically, the user's own data now never has to leave the device. We validate this method using a 1.2M-user 22-month dataset that spans body-weight, sleep hours and step counts collected by devices from Nokia Digital Health - Withings. Experiments show our framework remains accurate for a range of commonly used statistical aggregate functions. This result opens a powerful new paradigm for privacy-preserving analytics under which user data largely remains on personal devices, overcoming a variety of potential privacy risks.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"58 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114060278","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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