MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...最新文献
When it comes to rehabilitation and exercise, motivation can be a serious issue for older adults. Gamification is a trending concept that has been increasingly successful when it comes to encouraging certain user behaviors. Gamification makes use of game elements to engage and invoke desired behaviors in users. The heel raise exercise, otherwise known as calf raises, is a fundamental exercise that involves standing on toes and raising the heels. This work aims to expand on the development of a custom measurement device for heel raise physiotherapy that uses the concept of gamification to promote and motivate users to participate in heel raise exercises. The proposed solution is a game where players control an avatar to jump onto platforms by executing heel raises. In preliminary studies, the game has been evaluated to have some positive effects on older adults, such as increased motivation and the tendency to perform more repetitions of the exercise.
{"title":"Gamification of Heel Raise Plantarflexion Physiotherapy","authors":"Darren C. R. Goh, Alfred C. H. Tan, J. S. Lee","doi":"10.1145/3132635.3132638","DOIUrl":"https://doi.org/10.1145/3132635.3132638","url":null,"abstract":"When it comes to rehabilitation and exercise, motivation can be a serious issue for older adults. Gamification is a trending concept that has been increasingly successful when it comes to encouraging certain user behaviors. Gamification makes use of game elements to engage and invoke desired behaviors in users. The heel raise exercise, otherwise known as calf raises, is a fundamental exercise that involves standing on toes and raising the heels. This work aims to expand on the development of a custom measurement device for heel raise physiotherapy that uses the concept of gamification to promote and motivate users to participate in heel raise exercises. The proposed solution is a game where players control an avatar to jump onto platforms by executing heel raises. In preliminary studies, the game has been evaluated to have some positive effects on older adults, such as increased motivation and the tendency to perform more repetitions of the exercise.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88043855","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}
Wearable sensor systems can deliver promising solutions to automatic monitoring of ingestive behavior. This study presents an on-body sensor system and related signal processing techniques to classify different types of food intake sounds. A piezoelectric throat microphone is used to capture food consumption sounds from the neck. The recorded signals are firstly segmented and decomposed using the empirical mode decomposition (EMD) analysis. EMD has been a widely implemented tool to analyze non-stationary and non-linear signals by decomposing data into a series of sub-band oscillations known as intrinsic mode functions (IMFs). For each decomposed IMF signal, time and frequency domain features are then computed to provide a multi-resolution representation of the signal. The minimum redundancy maximum relevance (mRMR) principle is utilized to investigate the most representative features for the food intake classification task, which is carried out using the support vector machines. Experimental evaluations over selected groups of features and EMD achieve significant performance improvements compared to the baseline classification system without EMD.
{"title":"Empirical Mode Decomposition of Throat Microphone Recordings for Intake Classification","authors":"M. A. T. Turan, E. Erzin","doi":"10.1145/3132635.3132640","DOIUrl":"https://doi.org/10.1145/3132635.3132640","url":null,"abstract":"Wearable sensor systems can deliver promising solutions to automatic monitoring of ingestive behavior. This study presents an on-body sensor system and related signal processing techniques to classify different types of food intake sounds. A piezoelectric throat microphone is used to capture food consumption sounds from the neck. The recorded signals are firstly segmented and decomposed using the empirical mode decomposition (EMD) analysis. EMD has been a widely implemented tool to analyze non-stationary and non-linear signals by decomposing data into a series of sub-band oscillations known as intrinsic mode functions (IMFs). For each decomposed IMF signal, time and frequency domain features are then computed to provide a multi-resolution representation of the signal. The minimum redundancy maximum relevance (mRMR) principle is utilized to investigate the most representative features for the food intake classification task, which is carried out using the support vector machines. Experimental evaluations over selected groups of features and EMD achieve significant performance improvements compared to the baseline classification system without EMD.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84660893","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}
Susanne CJ Boll, T. Ebrahimi, C. Gurrin, R. Jain, Laleh Jalali, Jochen Meyer, N. O’Connor
We are delighted to welcome you to the Second International Workshop on Multimedia for Personal Health and Health Care (MMHealth), held on October 23, 2017 in conjunction with ACM Multimedia 2017 in Mountain View, USA. Multimedia and health is a new and developing field and has become an integral part of the tools and systems that are providing solutions t-o today's societal challenges in personal health and health care. Research in multimedia and health is driven by the current technological advancements in sensors and personalised healthcare. In recent years we can observe many applications of health care and personal health that are addressed by core multimedia research questions, such as monitoring daily life activities, developing lifestyle and behavioral profiles, physiological and cognitive multimedia--based monitoring for health status assessment, among many others. Applications can be as specific as the recognition of food to assess its nutritional content, multimodal visualization and correlation of lifestyle parameters to assess conditions such as dementia, or the development of personalized home assistants, used to help the elderly in their daily life, as society ages and the care ratio dwindles. There is an increasing number of research works that shows how core multimedia research has become an important technological enabler for addressing the societal questions of health. The special characteristic of the workshop is the objective of bringing together a challenging and important application domain and multimedia research. We received 19 papers (long and short) submissions for the main track of the workshop. All papers were reviewed by international experts in the field. The program chairs have accepted 9 full papers which makes an overall acceptance rate for full papers of 47% percent. In addition to this have been including 7 short papers from the authors of papers from the main track to enrich the workshop with an interactive experience in a poster session in the afternoon of the workshop.
{"title":"Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care","authors":"Susanne CJ Boll, T. Ebrahimi, C. Gurrin, R. Jain, Laleh Jalali, Jochen Meyer, N. O’Connor","doi":"10.1145/3132635","DOIUrl":"https://doi.org/10.1145/3132635","url":null,"abstract":"We are delighted to welcome you to the Second International Workshop on Multimedia for Personal Health and Health Care (MMHealth), held on October 23, 2017 in conjunction with ACM Multimedia 2017 in Mountain View, USA. \u0000 \u0000Multimedia and health is a new and developing field and has become an integral part of the tools and systems that are providing solutions t-o today's societal challenges in personal health and health care. Research in multimedia and health is driven by the current technological advancements in sensors and personalised healthcare. In recent years we can observe many applications of health care and personal health that are addressed by core multimedia research questions, such as monitoring daily life activities, developing lifestyle and behavioral profiles, physiological and cognitive multimedia--based monitoring for health status assessment, among many others. Applications can be as specific as the recognition of food to assess its nutritional content, multimodal visualization and correlation of lifestyle parameters to assess conditions such as dementia, or the development of personalized home assistants, used to help the elderly in their daily life, as society ages and the care ratio dwindles. There is an increasing number of research works that shows how core multimedia research has become an important technological enabler for addressing the societal questions of health. The special characteristic of the workshop is the objective of bringing together a challenging and important application domain and multimedia research. \u0000 \u0000We received 19 papers (long and short) submissions for the main track of the workshop. All papers were reviewed by international experts in the field. The program chairs have accepted 9 full papers which makes an overall acceptance rate for full papers of 47% percent. In addition to this have been including 7 short papers from the authors of papers from the main track to enrich the workshop with an interactive experience in a poster session in the afternoon of the workshop.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89134171","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 recent years, many methods and systems for automated recognition of human emotional states were proposed. Most of them are trying to recognize emotions based on physiological signals such as galvanic skin response (GSR), electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), photoplethysmogram (PPG), respiration, skin temperature etc. Measuring all these signals is quite impractical for real-life use and in this research, we decided to acquire and analyse only GSR and PPG signals because of its suitability for implementation on a simple wearable device that can collect signals from a person without compromising comfort and privacy. For this purpose, we used the lightweight, small and compact Shimmer3 sensor. We developed complete application with database storage to elicit participant»s emotions using pictures from the Geneva affective picture database (GAPED) database. In the post-processing process, we used typical statistical parameters and power spectral density (PSD) as features and support vector machine (SVM) and k-nearest neighbours (KNN) as classifiers. We built single-user and multi-user emotion classification models to compare the results. As expected, we got better average accuracies on a single-user model than on the multi-user model. Our results also show that a single-user based emotion detection model could potentially be used in real-life scenario considering environments conditions.
{"title":"Wearable Emotion Recognition System based on GSR and PPG Signals","authors":"G. Udovicic, Jurica Derek, M. Russo, M. Sikora","doi":"10.1145/3132635.3132641","DOIUrl":"https://doi.org/10.1145/3132635.3132641","url":null,"abstract":"In recent years, many methods and systems for automated recognition of human emotional states were proposed. Most of them are trying to recognize emotions based on physiological signals such as galvanic skin response (GSR), electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), photoplethysmogram (PPG), respiration, skin temperature etc. Measuring all these signals is quite impractical for real-life use and in this research, we decided to acquire and analyse only GSR and PPG signals because of its suitability for implementation on a simple wearable device that can collect signals from a person without compromising comfort and privacy. For this purpose, we used the lightweight, small and compact Shimmer3 sensor. We developed complete application with database storage to elicit participant»s emotions using pictures from the Geneva affective picture database (GAPED) database. In the post-processing process, we used typical statistical parameters and power spectral density (PSD) as features and support vector machine (SVM) and k-nearest neighbours (KNN) as classifiers. We built single-user and multi-user emotion classification models to compare the results. As expected, we got better average accuracies on a single-user model than on the multi-user model. Our results also show that a single-user based emotion detection model could potentially be used in real-life scenario considering environments conditions.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91210217","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}
Personal sensors are now ubiquitous and they can be wearable, they can be carried or they can be in situ and fixed into our homes or workplaces. The major factors influencing the growth in personal sensing include that they are smaller, smarter, cheaper, require less energy and they integrate with consumer devices. The major benefits of personal sensing are in the healthcare sector with secondary uses in sports and performance and in long-term monitoring of vulnerable populations, like the aged. So what do we usually do with the data generated from personal sensing? We count steps taken, measure distance walked, add up energy expenditure, assess sleep quality and that's about it. We can also longitudinally track our behaviour and detect changes, but we tend to do this only for cases like following a weight loss or a smoking cessation program or improving our food intake. Then, outside such motivational scenarios, we get bored and stop using them. Sometimes personal sensors use aspects of human behaviour by engaging us in competitions with others, or setting goals for ourselves. Strava is an example sensor for running and cycling that encourages its users to form part of a (virtual) community and to engage with others through social media. Beyond that we do not use our personal sensing data for any real value, for example to monitor our health or to form part of our annual medical check-up, for example. It is a fact that human lifestyles have in-built periodicities of various frequencies... daily, weekly, monthly, seasonal, and annual. The 24h periodicity is the most important, and dominant and disruptions to our 24h periodicity do cause us harm. For example, jet lag disruption includes us fatigue, malaise and poor concentration, all caused by deviation from our circadian rhythm. Using wearable sensors to collect data we can detect these periodicities. Not only can we detect but we can also measure the strength or intensity of the 24h periodicity over a time period. Using wrist-worn accelerometer data gathered from subjects over a 3-month period we measured the strength of their 24h periodicity and found correlation between shifts in periodicity intensity and some cardio-metabolic biomarkers which are health-related quality of life indices including LDL cholesterol, triglycerides, hc-CRP (C-Reactive Proteins, indicators of inflammation). This is a surprising result showing cardio-metabolic health feedback based on data-driven analytics of accelerometer data. This example highlights that we have much more to do to really maximise value from personal sensing data.
{"title":"Insights from Data Analytics Into Our Personal Sensor Data","authors":"A. Smeaton","doi":"10.1145/3132635.3132644","DOIUrl":"https://doi.org/10.1145/3132635.3132644","url":null,"abstract":"Personal sensors are now ubiquitous and they can be wearable, they can be carried or they can be in situ and fixed into our homes or workplaces. The major factors influencing the growth in personal sensing include that they are smaller, smarter, cheaper, require less energy and they integrate with consumer devices. The major benefits of personal sensing are in the healthcare sector with secondary uses in sports and performance and in long-term monitoring of vulnerable populations, like the aged. So what do we usually do with the data generated from personal sensing? We count steps taken, measure distance walked, add up energy expenditure, assess sleep quality and that's about it. We can also longitudinally track our behaviour and detect changes, but we tend to do this only for cases like following a weight loss or a smoking cessation program or improving our food intake. Then, outside such motivational scenarios, we get bored and stop using them. Sometimes personal sensors use aspects of human behaviour by engaging us in competitions with others, or setting goals for ourselves. Strava is an example sensor for running and cycling that encourages its users to form part of a (virtual) community and to engage with others through social media. Beyond that we do not use our personal sensing data for any real value, for example to monitor our health or to form part of our annual medical check-up, for example. It is a fact that human lifestyles have in-built periodicities of various frequencies... daily, weekly, monthly, seasonal, and annual. The 24h periodicity is the most important, and dominant and disruptions to our 24h periodicity do cause us harm. For example, jet lag disruption includes us fatigue, malaise and poor concentration, all caused by deviation from our circadian rhythm. Using wearable sensors to collect data we can detect these periodicities. Not only can we detect but we can also measure the strength or intensity of the 24h periodicity over a time period. Using wrist-worn accelerometer data gathered from subjects over a 3-month period we measured the strength of their 24h periodicity and found correlation between shifts in periodicity intensity and some cardio-metabolic biomarkers which are health-related quality of life indices including LDL cholesterol, triglycerides, hc-CRP (C-Reactive Proteins, indicators of inflammation). This is a surprising result showing cardio-metabolic health feedback based on data-driven analytics of accelerometer data. This example highlights that we have much more to do to really maximise value from personal sensing data.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80988426","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}
E. Chatzilari, G. Liaros, K. Georgiadis, S. Nikolopoulos, Y. Kompatsiaris
In this paper we propose a novel method for SSVEP classification that combines the benefits of the inherently multi-channel CCA, the state-of-the-art method for detecting SSVEPs, with the robust SVMs, one of the most popular machine learning algorithms. The employment of SVMs, except for the benefit of robustness, provides us also with a confidence score allowing to dynamically trade-off the trial length with the accuracy of the classifier, and vice versa. By balancing this trade-off we are able to offer personalized self-paced BCIs that maximize the ITR of the system. Furthermore, we propose to perturb the template frequencies of CCA so as to accommodate with real world BCI applications requirements, where the environmental conditions may not be ideal compared to existing methods that rely on the assumption of soundproof and distraction-free environments.
{"title":"Combining the Benefits of CCA and SVMs for SSVEP-based BCIs in Real-world Conditions","authors":"E. Chatzilari, G. Liaros, K. Georgiadis, S. Nikolopoulos, Y. Kompatsiaris","doi":"10.1145/3132635.3132636","DOIUrl":"https://doi.org/10.1145/3132635.3132636","url":null,"abstract":"In this paper we propose a novel method for SSVEP classification that combines the benefits of the inherently multi-channel CCA, the state-of-the-art method for detecting SSVEPs, with the robust SVMs, one of the most popular machine learning algorithms. The employment of SVMs, except for the benefit of robustness, provides us also with a confidence score allowing to dynamically trade-off the trial length with the accuracy of the classifier, and vice versa. By balancing this trade-off we are able to offer personalized self-paced BCIs that maximize the ITR of the system. Furthermore, we propose to perturb the template frequencies of CCA so as to accommodate with real world BCI applications requirements, where the environmental conditions may not be ideal compared to existing methods that rely on the assumption of soundproof and distraction-free environments.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88096167","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}
J. Kuklyte, Leonardo Gualano, Ghanashyama Prabhu, K. Venkataraman, Deirdre M J Walsh, C. Woods, Kieran Moran, N. O’Connor
The third phase of the recovery from cardiovascular disease (CVD) is an exercise-based rehabilitation programme. However, adherence to an exercise regime is typically not maintained by the patient for a variety of reasons such as lack of time, financial constraints, etc. In order to facilitate patients to perform their exercises from the comfort of their home and at their own convenience, we have developed a mobile application, termed MedFit. It provides access to a tailored suite of exercises along with easy to understand guidance from audio and video instructions. Two types of wearable sensors are utilized to provide motivational feedback. Fitbit, a commercially available activity and fitness tracker, is used to provide in-depth feedback for self-monitoring over longer periods of time (e.g. day, week, month), whereas the Shimmer wireless sensing platform provides the data for near real-time feedback on the quality of the exercises performed. MedFit is a simple and intuitive mobile application designed to provide the motivation and tools for patients to help ensure faster recovery from the trauma caused by CVD. In this paper we describe features available in the MedFit application and the overall motivation behind the project.
{"title":"MedFit: A Mobile Application for Patients in CVD Recovery","authors":"J. Kuklyte, Leonardo Gualano, Ghanashyama Prabhu, K. Venkataraman, Deirdre M J Walsh, C. Woods, Kieran Moran, N. O’Connor","doi":"10.1145/3132635.3132651","DOIUrl":"https://doi.org/10.1145/3132635.3132651","url":null,"abstract":"The third phase of the recovery from cardiovascular disease (CVD) is an exercise-based rehabilitation programme. However, adherence to an exercise regime is typically not maintained by the patient for a variety of reasons such as lack of time, financial constraints, etc. In order to facilitate patients to perform their exercises from the comfort of their home and at their own convenience, we have developed a mobile application, termed MedFit. It provides access to a tailored suite of exercises along with easy to understand guidance from audio and video instructions. Two types of wearable sensors are utilized to provide motivational feedback. Fitbit, a commercially available activity and fitness tracker, is used to provide in-depth feedback for self-monitoring over longer periods of time (e.g. day, week, month), whereas the Shimmer wireless sensing platform provides the data for near real-time feedback on the quality of the exercises performed. MedFit is a simple and intuitive mobile application designed to provide the motivation and tools for patients to help ensure faster recovery from the trauma caused by CVD. In this paper we describe features available in the MedFit application and the overall motivation behind the project.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83994559","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}
{"title":"Session details: Understanding and Promoting Personal Health","authors":"Jochen Meyer","doi":"10.1145/3247928","DOIUrl":"https://doi.org/10.1145/3247928","url":null,"abstract":"","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76783647","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}
Edward Y. Chang, Meng-Hsi Wu, Kai-Fu Tang, Hao-Cheng Kao, Chun-Nan Chou
The DeepQ tricorder device developed by HTC from 2013 to 2016 was entered in the Qualcomm Tricorder XPRIZE competition and awarded the second prize in April 2017. This paper presents DeepQ»s three modules powered by artificial intelligence: symptom checker, optical sense, and vital sense. We depict both their initial design and ongoing enhancements.
{"title":"Artificial Intelligence in XPRIZE DeepQ Tricorder","authors":"Edward Y. Chang, Meng-Hsi Wu, Kai-Fu Tang, Hao-Cheng Kao, Chun-Nan Chou","doi":"10.1145/3132635.3132637","DOIUrl":"https://doi.org/10.1145/3132635.3132637","url":null,"abstract":"The DeepQ tricorder device developed by HTC from 2013 to 2016 was entered in the Qualcomm Tricorder XPRIZE competition and awarded the second prize in April 2017. This paper presents DeepQ»s three modules powered by artificial intelligence: symptom checker, optical sense, and vital sense. We depict both their initial design and ongoing enhancements.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"17 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83224616","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 this article, we identify and discuss challenges imposed on technological research by emerging developments in health and wellbeing. We see an increasing importance of digital health literacy, the convergence of medicine and daily life, a shift from individual health to community care, a growth of personalized medicine, and the impact of internet of things on health. These developments mean challenges for technical research, such as the need, but also difficulties of interdisciplinarity, or the need to translate personal health data into medical information. Today's research approaches are not always best suited to deal with the challenges, e.g. of conducting real long term intervention studies, or taking into account regulatory issues. We propose a joint campaign by HCI, AI, UX and machine learning researchers, engineers, clinicians, regulatory bodies and all other interested parties in these subjects.
{"title":"Research Challenges of Emerging Technologies Supporting Life-Long Health and Wellbeing","authors":"Jochen Meyer, Parisa Eslambolchilar","doi":"10.1145/3132635.3132639","DOIUrl":"https://doi.org/10.1145/3132635.3132639","url":null,"abstract":"In this article, we identify and discuss challenges imposed on technological research by emerging developments in health and wellbeing. We see an increasing importance of digital health literacy, the convergence of medicine and daily life, a shift from individual health to community care, a growth of personalized medicine, and the impact of internet of things on health. These developments mean challenges for technical research, such as the need, but also difficulties of interdisciplinarity, or the need to translate personal health data into medical information. Today's research approaches are not always best suited to deal with the challenges, e.g. of conducting real long term intervention studies, or taking into account regulatory issues. We propose a joint campaign by HCI, AI, UX and machine learning researchers, engineers, clinicians, regulatory bodies and all other interested parties in these subjects.","PeriodicalId":92693,"journal":{"name":"MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...","volume":"64 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77601201","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}
MMHealth'17 : proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care : October 23, 2017, Mountain View, CA, USA. ACM Workshop on Multimedia for Personal Health and Health Care (2nd : 2017 : Mount...
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