Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248621
A. O'Brien, Kevin McDaid, John Loane, Julie Doyle, B. O'Mullane
As the population lives longer, the number of people beyond the age of retirement is rising. Additional support is required to monitor the health of this aging population to ensure early detection of age related illnesses and accordingly to maintain wellbeing. Ambient sensors, such as those used in Great Northern Haven (GNH) residences, allow for continuous monitoring in an unobtrusive manner. In this paper we present visualisations of sensor data drawn from passive infra red (PIR) sensors located in three areas, i.e. the living room, hallway and main bedroom, in each of twelve independent living apartments. This representation of residents' movement can be used to infer changes in movement patterns over time.
{"title":"Visualisation of movement of older adults within their homes based on PIR sensor data","authors":"A. O'Brien, Kevin McDaid, John Loane, Julie Doyle, B. O'Mullane","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248621","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248621","url":null,"abstract":"As the population lives longer, the number of people beyond the age of retirement is rising. Additional support is required to monitor the health of this aging population to ensure early detection of age related illnesses and accordingly to maintain wellbeing. Ambient sensors, such as those used in Great Northern Haven (GNH) residences, allow for continuous monitoring in an unobtrusive manner. In this paper we present visualisations of sensor data drawn from passive infra red (PIR) sensors located in three areas, i.e. the living room, hallway and main bedroom, in each of twelve independent living apartments. This representation of residents' movement can be used to infer changes in movement patterns over time.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"216 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":"123535423","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.2012.248695
A. Kobsa, Yunan Chen, Tao Wang
Designing interactive systems that support health management among chronic care patients has become a research interest in the HCI community. These systems are meant to allow patients to act as normal people, without letting illnesses affect their everyday lives. In particular, various diary and logging applications have been developed to allow people to record, monitor and visualize their daily behaviors and the progress of their disease. One critical aspect of personal health management that such systems do not yet sufficiently support is the diversity of health behaviors among individuals. As previous research indicates, individuals vary greatly in how they manage their health and what “works for them” [1-3]. Discovering personal behavioral rules is critical for wellness and for health maintenance. Technologies that encourage and promote personal rule discovery are thus needed. In this paper, we present our ongoing research on developing such a system, and discuss preliminary findings from an initial user study.
{"title":"Discovering personal behavioral rules in a health management system","authors":"A. Kobsa, Yunan Chen, Tao Wang","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248695","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248695","url":null,"abstract":"Designing interactive systems that support health management among chronic care patients has become a research interest in the HCI community. These systems are meant to allow patients to act as normal people, without letting illnesses affect their everyday lives. In particular, various diary and logging applications have been developed to allow people to record, monitor and visualize their daily behaviors and the progress of their disease. One critical aspect of personal health management that such systems do not yet sufficiently support is the diversity of health behaviors among individuals. As previous research indicates, individuals vary greatly in how they manage their health and what “works for them” [1-3]. Discovering personal behavioral rules is critical for wellness and for health maintenance. Technologies that encourage and promote personal rule discovery are thus needed. In this paper, we present our ongoing research on developing such a system, and discuss preliminary findings from an initial user study.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"5 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":"121454086","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.2012.249083
Iván Zavala-Ibarra, J. Favela
Sarcopenia is defined as the loss of skeletal muscle mass that occurs with aging, and is recognized as a major contributing factor to disability and loss of independence in the elderly. Recent evidence shows that age-related loss of muscle strength and power (dynapenia), as well as muscle fatigue are a better predictor of disability. In this paper we describe three ambient casual games that have been designed to measure arm muscle strength and fatigue utilizing a custom-designed interaction device that provides a natural user interface for the games. The games are designed to be used frequently and for short periods of time. We conducted a formative evaluation of the games with 5 older adults to assess ease of use and their interest in playing them. We compare the results of traditional measures of muscle strength using a clinical dynamometer with those obtained using the videogame. The higher frequency with which measures can be obtained from playing the videogame can result in a more reliable and timely assessment of risks of dynapenia and frailty.
{"title":"Assessing muscle disease related to aging using ambient videogames","authors":"Iván Zavala-Ibarra, J. Favela","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.249083","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.249083","url":null,"abstract":"Sarcopenia is defined as the loss of skeletal muscle mass that occurs with aging, and is recognized as a major contributing factor to disability and loss of independence in the elderly. Recent evidence shows that age-related loss of muscle strength and power (dynapenia), as well as muscle fatigue are a better predictor of disability. In this paper we describe three ambient casual games that have been designed to measure arm muscle strength and fatigue utilizing a custom-designed interaction device that provides a natural user interface for the games. The games are designed to be used frequently and for short periods of time. We conducted a formative evaluation of the games with 5 older adults to assess ease of use and their interest in playing them. We compare the results of traditional measures of muscle strength using a clinical dynamometer with those obtained using the videogame. The higher frequency with which measures can be obtained from playing the videogame can result in a more reliable and timely assessment of risks of dynapenia and frailty.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"17 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":"121282199","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.2012.248705
N. Ramanathan, F. Alquaddoomi, H. Falaki, D. George, C. Hsieh, J. Jenkins, C. Ketcham, B. Longstaff, J. Ooms, J. Selsky, H. Tangmunarunkit, D. Estrin
We present ohmage, a mobile to web platform that records, analyzes, and visualizes data from both prompted experience samples entered by the user, as well as continuous streams of data passively collected from sensors onboard the mobile device. ohmage has been used in a number of research health studies. Key challenges in these deployments are engaging participants to sustain data collection in long-lived campaigns, conserving battery power, and extracting accurate inferences from the collected streams. To address these challenges, we have incorporated feedback from hundreds of behavioral and technology researchers, focus group participants, and end-users of the system in an iterative design process. We summarize this rich feedback, and present the resulting system.
{"title":"ohmage: An open mobile system for activity and experience sampling","authors":"N. Ramanathan, F. Alquaddoomi, H. Falaki, D. George, C. Hsieh, J. Jenkins, C. Ketcham, B. Longstaff, J. Ooms, J. Selsky, H. Tangmunarunkit, D. Estrin","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248705","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248705","url":null,"abstract":"We present ohmage, a mobile to web platform that records, analyzes, and visualizes data from both prompted experience samples entered by the user, as well as continuous streams of data passively collected from sensors onboard the mobile device. ohmage has been used in a number of research health studies. Key challenges in these deployments are engaging participants to sustain data collection in long-lived campaigns, conserving battery power, and extracting accurate inferences from the collected streams. To address these challenges, we have incorporated feedback from hundreds of behavioral and technology researchers, focus group participants, and end-users of the system in an iterative design process. We summarize this rich feedback, and present the resulting system.","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":"115617755","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.2012.248676
S. Chiriac, Natalie Röll, Javier Parada, B. Saurer
Existing health monitoring systems detect either acute health issues or long-term deterioration of health. This paper presents a validation concept for sensors and algorithms that detect emergencies and monitor long term health development. The reviewed ambient monitoring system combines a short term rule based and a long term scoring approach that are validated in a study with 100 households. The resulting dataset is complemented by diaries and participation of care givers. Care givers monitor the health state through questionnaires and by rating generated alerts and warnings. The validation benefits from this multilateral approach and points towards a further integration of long and short-term monitoring.
{"title":"Towards combining validation concepts for short and long-term ambient health monitoring","authors":"S. Chiriac, Natalie Röll, Javier Parada, B. Saurer","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248676","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248676","url":null,"abstract":"Existing health monitoring systems detect either acute health issues or long-term deterioration of health. This paper presents a validation concept for sensors and algorithms that detect emergencies and monitor long term health development. The reviewed ambient monitoring system combines a short term rule based and a long term scoring approach that are validated in a study with 100 households. The resulting dataset is complemented by diaries and participation of care givers. Care givers monitor the health state through questionnaires and by rating generated alerts and warnings. The validation benefits from this multilateral approach and points towards a further integration of long and short-term monitoring.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"788 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":"123901784","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.2012.248708
Salys Sultan, P. Mohan
This paper presents a mobile health system called Mobile DSMS which is based on the collaborative disease management framework using mobile technologies. Mobile DSMS allows patients with similar disease management interests to virtually gather and share experiences, ask questions and provide support and problem-solve remotely through the use of mobile devices.
{"title":"A peer-facilitated diabetes self-care management support system using mobile telephony","authors":"Salys Sultan, P. Mohan","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248708","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248708","url":null,"abstract":"This paper presents a mobile health system called Mobile DSMS which is based on the collaborative disease management framework using mobile technologies. Mobile DSMS allows patients with similar disease management interests to virtually gather and share experiences, ask questions and provide support and problem-solve remotely through the use of mobile devices.","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":"126720730","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.2012.248715
Adity U. Mutsuddi, Kay Connelly
Many studies have found text messaging to be a promising medium for healthcare delivery. However, since the studies that successfully used text messages for encouraging physical activity were all short term (10 days to 3 weeks) and conducted with a small sample (n≤15), we do not know if people will not be motivated by these technologies after the novelty effect dies. In this paper, we present the results from a study conducted for a longer term (3 months) with a larger sample size (n=28) to discover if text messages are effective for encouraging physical activity once the novelty effect of the technology wears off. We chose a population of young adults (age 18-24) as they are one of the heaviest users of text messages. Measures of analysis included number of steps, message ratings, level of motivation and interviews. Our findings suggest that text messages are a good way for encouraging physical activity in young adults, even after the novelty effect wears off.
{"title":"Text messages for encouraging physical activity Are they effective after the novelty effect wears off?","authors":"Adity U. Mutsuddi, Kay Connelly","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248715","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248715","url":null,"abstract":"Many studies have found text messaging to be a promising medium for healthcare delivery. However, since the studies that successfully used text messages for encouraging physical activity were all short term (10 days to 3 weeks) and conducted with a small sample (n≤15), we do not know if people will not be motivated by these technologies after the novelty effect dies. In this paper, we present the results from a study conducted for a longer term (3 months) with a larger sample size (n=28) to discover if text messages are effective for encouraging physical activity once the novelty effect of the technology wears off. We chose a population of young adults (age 18-24) as they are one of the heaviest users of text messages. Measures of analysis included number of steps, message ratings, level of motivation and interviews. Our findings suggest that text messages are a good way for encouraging physical activity in young adults, even after the novelty effect wears off.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"56 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120919443","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.2012.248671
Amy S. Hwang, K. Truong, Alex Mihailidis
Smart home technologies hold potential to help older adults with dementia complete activities independently, while alleviating the burden placed on their informal caregivers. Toward this goal, more research focus is needed to involve informal caregivers in the needs analysis and design of smart home user interfaces, for which they are likely to become primary users in the future. In this paper, we present our participatory design approach and the tensions that challenge user interface design for the intelligent COACH system that assists with activities of daily living in the home. Future work should involve older adults with dementia and other care stakeholders in user-centered design processes, and the development of systematic, interdisciplinary approaches for user interface design.
{"title":"Using participatory design to determine the needs of informal caregivers for smart home user interfaces","authors":"Amy S. Hwang, K. Truong, Alex Mihailidis","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248671","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248671","url":null,"abstract":"Smart home technologies hold potential to help older adults with dementia complete activities independently, while alleviating the burden placed on their informal caregivers. Toward this goal, more research focus is needed to involve informal caregivers in the needs analysis and design of smart home user interfaces, for which they are likely to become primary users in the future. In this paper, we present our participatory design approach and the tensions that challenge user interface design for the intelligent COACH system that assists with activities of daily living in the home. Future work should involve older adults with dementia and other care stakeholders in user-centered design processes, and the development of systematic, interdisciplinary approaches for user interface design.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"36 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":"115273455","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.2012.248722
Folami T. Alamudun, Jongyoon Choi, R. Gutierrez-Osuna, H. Khan, B. Ahmed
The ability to monitor stress levels in daily life can provide valuable information to patients and their caretakers, help identify potential stressors, determine appropriate interventions, and monitor their effectiveness. Wearable sensor technology makes it now possible to measure non-invasively a number of physiological correlates of stress, from skin conductance to heart rate variability. These measures, however, show large individual differences and are also correlated with the physical activity of the subject. In this paper, we propose two multivariate signal processing techniques to reduce the effect of both forms of interference. The first method is an unsupervised technique that removes any systematic variation that is orthogonal to the dependent variable, in this case physiological stress. In contrast, the second method is a supervised technique that first projects the data into a subspace that emphasizes these systematic variations, and then removes them from the data. The two methods were validated on an experimental dataset containing physiological recordings from multiple subjects performing physical and/or mental activities. When compared to z-score normalization, the standard method for removing individual differences, our methods can reduce stress prediction errors by as much as 50%.
{"title":"Removal of subject-dependent and activity-dependent variation in physiological measures of stress","authors":"Folami T. Alamudun, Jongyoon Choi, R. Gutierrez-Osuna, H. Khan, B. Ahmed","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248722","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248722","url":null,"abstract":"The ability to monitor stress levels in daily life can provide valuable information to patients and their caretakers, help identify potential stressors, determine appropriate interventions, and monitor their effectiveness. Wearable sensor technology makes it now possible to measure non-invasively a number of physiological correlates of stress, from skin conductance to heart rate variability. These measures, however, show large individual differences and are also correlated with the physical activity of the subject. In this paper, we propose two multivariate signal processing techniques to reduce the effect of both forms of interference. The first method is an unsupervised technique that removes any systematic variation that is orthogonal to the dependent variable, in this case physiological stress. In contrast, the second method is a supervised technique that first projects the data into a subspace that emphasizes these systematic variations, and then removes them from the data. The two methods were validated on an experimental dataset containing physiological recordings from multiple subjects performing physical and/or mental activities. When compared to z-score normalization, the standard method for removing individual differences, our methods can reduce stress prediction errors by as much as 50%.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"33 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":"122675167","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.2012.248689
A. Matic, V. Osmani, Alban Maxhuni, O. Mayora-Ibarra
The level of participation in social interactions has been shown to have an impact on various health outcomes, while it also reflects the overall wellbeing status. In health sciences the standard practice for measuring the amount of social activity relies on periodical self-reports that suffer from memory dependence, recall bias and the current mood. In this regard, the use of sensor-based detection of social interactions has the potential to overcome the limitations of self-reporting methods that have been used for decades in health related sciences. However, the current systems have mainly relied on external infrastructures, which are confined within specific location or on specialized devices typically not-available off the shelf. On the other hand, mobile phone based solutions are often limited in accuracy or in capturing social interactions that occur on small time and spatial scales. The work presented in this paper relies on widely available mobile sensing technologies, namely smart phones utilized for recognizing spatial settings between subjects and the accelerometer used for speech activity identification. We evaluate the two sensing modalities both separately and in fusion, demonstrating high accuracy in detecting social interactions on small spatio-temporal scale.
{"title":"Multi-modal mobile sensing of social interactions","authors":"A. Matic, V. Osmani, Alban Maxhuni, O. Mayora-Ibarra","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248689","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248689","url":null,"abstract":"The level of participation in social interactions has been shown to have an impact on various health outcomes, while it also reflects the overall wellbeing status. In health sciences the standard practice for measuring the amount of social activity relies on periodical self-reports that suffer from memory dependence, recall bias and the current mood. In this regard, the use of sensor-based detection of social interactions has the potential to overcome the limitations of self-reporting methods that have been used for decades in health related sciences. However, the current systems have mainly relied on external infrastructures, which are confined within specific location or on specialized devices typically not-available off the shelf. On the other hand, mobile phone based solutions are often limited in accuracy or in capturing social interactions that occur on small time and spatial scales. The work presented in this paper relies on widely available mobile sensing technologies, namely smart phones utilized for recognizing spatial settings between subjects and the accelerometer used for speech activity identification. We evaluate the two sensing modalities both separately and in fusion, demonstrating high accuracy in detecting social interactions on small spatio-temporal scale.","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":"128057733","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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