In this paper, we argue for expanded research into an area called Socio-Physical Analytics, that focuses on combining the behavioral insight gained from mobile-sensing based monitoring of physical behavior with the inter-personal relationships and preferences deduced from online social networks. We highlight some of the research challenges in combining these heterogeneous data sources and then describe some examples of our ongoing work (based on real-world data being collected at SMU) that illustrate two aspects of socio-physical analytics: (a) how additional demographic and online analytics based attributes can potentially provide better insights into the preferences and behaviors of individuals or groups (in terms of movement prediction and understanding of physical vs. online interactions), and (b) how online and physical interactions can help us discover latent characteristics of physical spaces and entities.
{"title":"Socio-physical analytics: challenges & opportunities","authors":"Archan Misra, Kasthuri Jayarajah, Shriguru Nayak, Philips Kokoh Prasetyo, Ee-Peng Lim","doi":"10.1145/2611264.2611265","DOIUrl":"https://doi.org/10.1145/2611264.2611265","url":null,"abstract":"In this paper, we argue for expanded research into an area called Socio-Physical Analytics, that focuses on combining the behavioral insight gained from mobile-sensing based monitoring of physical behavior with the inter-personal relationships and preferences deduced from online social networks. We highlight some of the research challenges in combining these heterogeneous data sources and then describe some examples of our ongoing work (based on real-world data being collected at SMU) that illustrate two aspects of socio-physical analytics: (a) how additional demographic and online analytics based attributes can potentially provide better insights into the preferences and behaviors of individuals or groups (in terms of movement prediction and understanding of physical vs. online interactions), and (b) how online and physical interactions can help us discover latent characteristics of physical spaces and entities.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132102437","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}
Mobile phones are a pervasive platform for opportunistic sensing of social and health related behaviors. In this talk, I discuss how sensor data from mobile phones can be used to model and predict health outcomes. The talk starts with a review of research at the MIT Media Lab, and then transitions into how Ginger.io has built a commercial platform to collect, annotate, analyze and drive healthcare interventions at scale, deployed with major US hospital systems and healthcare providers. The Ginger.io three-part platform -- patient app, behavioral analytics engine, and provider dashboard -- applies this technology to give care providers a window into their patients' health between office visits. Our mobile app uses smartphone sensors to passively collect information about a patient's daily patterns. Using this data, our machine learning models are able to detect at-risk patients significantly better than the standard of care. Any concerning changes in behavior are communicated to the provider through our simple, action-oriented web dashboard. Ginger.io is part of the care solutions at institutions such as Kaiser Permanente, Novant Health, UCSF, Duke Medical and Cincinnati Children's.
{"title":"Physical analytics to model health behaviors","authors":"Anmol Madan","doi":"10.1145/2611264.2611272","DOIUrl":"https://doi.org/10.1145/2611264.2611272","url":null,"abstract":"Mobile phones are a pervasive platform for opportunistic sensing of social and health related behaviors. In this talk, I discuss how sensor data from mobile phones can be used to model and predict health outcomes. The talk starts with a review of research at the MIT Media Lab, and then transitions into how Ginger.io has built a commercial platform to collect, annotate, analyze and drive healthcare interventions at scale, deployed with major US hospital systems and healthcare providers. The Ginger.io three-part platform -- patient app, behavioral analytics engine, and provider dashboard -- applies this technology to give care providers a window into their patients' health between office visits. Our mobile app uses smartphone sensors to passively collect information about a patient's daily patterns. Using this data, our machine learning models are able to detect at-risk patients significantly better than the standard of care. Any concerning changes in behavior are communicated to the provider through our simple, action-oriented web dashboard. Ginger.io is part of the care solutions at institutions such as Kaiser Permanente, Novant Health, UCSF, Duke Medical and Cincinnati Children's.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121142703","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}
Seungyeop Han, R. Nandakumar, Matthai Philipose, A. Krishnamurthy, D. Wetherall
Emerging wearable devices provide a new opportunity for mobile context-aware applications to use continuous audio/video sensing data as primitive inputs. Due to the high-datarate and compute-intensive nature of the inputs, it is important to design frameworks and applications to be efficient. We present the GlimpseData framework to collect and analyze data for studying continuous high-datarate mobile perception. As a case study, we show that we can use low-powered sensors as a filter to avoid sensing and processing video for face detection. Our relatively simple mechanism avoids processing roughly 60% of video frames while missing only 10% of frames with faces in them.
{"title":"GlimpseData: towards continuous vision-based personal analytics","authors":"Seungyeop Han, R. Nandakumar, Matthai Philipose, A. Krishnamurthy, D. Wetherall","doi":"10.1145/2611264.2611269","DOIUrl":"https://doi.org/10.1145/2611264.2611269","url":null,"abstract":"Emerging wearable devices provide a new opportunity for mobile context-aware applications to use continuous audio/video sensing data as primitive inputs. Due to the high-datarate and compute-intensive nature of the inputs, it is important to design frameworks and applications to be efficient. We present the GlimpseData framework to collect and analyze data for studying continuous high-datarate mobile perception. As a case study, we show that we can use low-powered sensors as a filter to avoid sensing and processing video for face detection. Our relatively simple mechanism avoids processing roughly 60% of video frames while missing only 10% of frames with faces in them.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131685820","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: Wireless tracking","authors":"Archan Misra","doi":"10.1145/3255792","DOIUrl":"https://doi.org/10.1145/3255792","url":null,"abstract":"","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123569504","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: Human and social sensing","authors":"Ramón Cáceres","doi":"10.1145/3255791","DOIUrl":"https://doi.org/10.1145/3255791","url":null,"abstract":"","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125730285","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}