Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-731-4-26
S. Artmann
{"title":"Well-Being in Physical Information Spacetime: Philosophical Observations on the Use of Pervasive Computing for Supporting Good Life","authors":"S. Artmann","doi":"10.3233/978-1-60750-731-4-26","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-26","url":null,"abstract":"","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115214001","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 : 1900-01-01DOI: 10.3233/978-1-60750-731-4-105
Kumari Wickramasinghe, M. Georgeff, Christian Guttmann, Ian E. Thomas, H. Schmidt
{"title":"Cost/Benefit Analysis of an Adherence Support Framework for Chronic Disease Management","authors":"Kumari Wickramasinghe, M. Georgeff, Christian Guttmann, Ian E. Thomas, H. Schmidt","doi":"10.3233/978-1-60750-731-4-105","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-105","url":null,"abstract":"","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123048216","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 : 1900-01-01DOI: 10.3233/978-1-60750-731-4-3
B. Gottfried, H. Aghajan
{"title":"Behaviour Monitoring and Interpretation - An Overview of Technologies Supporting the Well-Being of Humans","authors":"B. Gottfried, H. Aghajan","doi":"10.3233/978-1-60750-731-4-3","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-3","url":null,"abstract":"","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121891016","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 : 1900-01-01DOI: 10.3233/978-1-60750-731-4-147
Annika Peters, Thorsten P. Spexard, Marc Hanheide, P. Weiß
{"title":"Hey robot, get out of my way - A survey on a spatial and situational movement concept in HRI","authors":"Annika Peters, Thorsten P. Spexard, Marc Hanheide, P. Weiß","doi":"10.3233/978-1-60750-731-4-147","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-147","url":null,"abstract":"","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117207679","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 : 1900-01-01DOI: 10.3233/978-1-60750-731-4-131
X. Long, S. Pauws, M. Pijl, J. Lacroix, A. Goris, Ronald M. Aarts
The growing number of people adopting a sedentary lifestyle these days creates a serious need for effective physical activity promotion programs. Often, these programs monitor activity, provide feedback about activity and offer coaching to increase activity. Some programs rely on a human coach who creates an activity goal that is tailored to the characteristics of a participant. Throughout the program, the coach motivates the participant to reach his personal goal or adapt the goal, if needed. Both the timing and the content of the coaching are important for the coaching. Insights on the near future state on, for instance, behaviour and motivation of a participant can be helpful to realize an effective proactive coaching style that is personalized in terms of timing and content. As a first step towards providing these insights to a coach, this chapter discusses results of a study on predicting daily physical activity level (PAL) data from past data of participants in a lifestyle intervention program. A mobile body-worn activity monitor with a built-in triaxial accelerometer was used to record PAL data of a participant for a period of 13 weeks. Predicting future PAL data for all days in a given period was done by employing autoregressive integrated moving average (ARIMA) models on the PAL data from days in the period before. By using a newly proposed categorized-ARIMA (CARIMA) prediction method, we achieved a large reduction in computation time without a significant loss in prediction accuracy in comparison with traditional ARIMA models. In CARIMA, PAL data are categorized as stationary, trend or seasonal data by assessing their autocorrelation functions. Then, an ARIMA model that is most appropriate to these three categories is automatically selected based on an objective penalty function criterion. The results show that our CARIMA method performs well in terms of PAL prediction accuracy (~9% mean absolute percentage error), model parsimony and robustness.
{"title":"Predicting Daily Physical Activity in a Lifestyle Intervention Program","authors":"X. Long, S. Pauws, M. Pijl, J. Lacroix, A. Goris, Ronald M. Aarts","doi":"10.3233/978-1-60750-731-4-131","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-131","url":null,"abstract":"The growing number of people adopting a sedentary lifestyle these days creates a serious need for effective physical activity promotion programs. Often, these programs monitor activity, provide feedback about activity and offer coaching to increase activity. Some programs rely on a human coach who creates an activity goal that is tailored to the characteristics of a participant. Throughout the program, the coach motivates the participant to reach his personal goal or adapt the goal, if needed. Both the timing and the content of the coaching are important for the coaching. Insights on the near future state on, for instance, behaviour and motivation of a participant can be helpful to realize an effective proactive coaching style that is personalized in terms of timing and content. As a first step towards providing these insights to a coach, this chapter discusses results of a study on predicting daily physical activity level (PAL) data from past data of participants in a lifestyle intervention program. A mobile body-worn activity monitor with a built-in triaxial accelerometer was used to record PAL data of a participant for a period of 13 weeks. Predicting future PAL data for all days in a given period was done by employing autoregressive integrated moving average (ARIMA) models on the PAL data from days in the period before. By using a newly proposed categorized-ARIMA (CARIMA) prediction method, we achieved a large reduction in computation time without a significant loss in prediction accuracy in comparison with traditional ARIMA models. In CARIMA, PAL data are categorized as stationary, trend or seasonal data by assessing their autocorrelation functions. Then, an ARIMA model that is most appropriate to these three categories is automatically selected based on an objective penalty function criterion. The results show that our CARIMA method performs well in terms of PAL prediction accuracy (~9% mean absolute percentage error), model parsimony and robustness.","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"375 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124911192","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 : 1900-01-01DOI: 10.3233/978-1-60750-731-4-65
Aaron S. Crandall, D. Cook
Once a smart home system moves to a multi-resident situation, it becomes significantly more important that individuals are tracked in some manner. By tracking individuals the events received from the sensor platform can then be separated into different streams and acted on independently by other tools within the smart home system. This process improves activity detection, history building and personalized interaction with the intelligent space. Historically, tracking has been primarily approached through a carried wireless device or an imaging system, such as video cameras. These are complicated approaches and still do not always effectively address the problem. Additionally, both of these solutions pose social problems to implement in private homes over long periods of time. This paper introduces and explores a Bayesian Updating method of tracking individuals through the space that leverages the Center for Advanced Studies in Adaptive Systems (CASAS) technology platform of pervasive and passive sensors. This approach does not require the residents to maintain a wireless device, nor does it incorporate rich sensors with the social privacy issues.
{"title":"Tracking Systems for Multiple Smart Home Residents","authors":"Aaron S. Crandall, D. Cook","doi":"10.3233/978-1-60750-731-4-65","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-65","url":null,"abstract":"Once a smart home system moves to a multi-resident situation, it becomes significantly more important that individuals are tracked in some manner. By tracking individuals the events received from the sensor platform can then be separated into different streams and acted on independently by other tools within the smart home system. This process improves activity detection, history building and personalized interaction with the intelligent space. Historically, tracking has been primarily approached through a carried wireless device or an imaging system, such as video cameras. These are complicated approaches and still do not always effectively address the problem. Additionally, both of these solutions pose social problems to implement in private homes over long periods of time. This paper introduces and explores a Bayesian Updating method of tracking individuals through the space that leverages the Center for Advanced Studies in Adaptive Systems (CASAS) technology platform of pervasive and passive sensors. This approach does not require the residents to maintain a wireless device, nor does it incorporate rich sensors with the social privacy issues.","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130454002","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 : 1900-01-01DOI: 10.3233/978-1-60750-731-4-166
A. Khalili, Chen Wu, H. Aghajan
{"title":"Towards Adaptive and User-Centric Smart Home Applications","authors":"A. Khalili, Chen Wu, H. Aghajan","doi":"10.3233/978-1-60750-731-4-166","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-166","url":null,"abstract":"","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130488095","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 : 1900-01-01DOI: 10.3233/978-1-60750-731-4-11
Daniel M. Johnson, F. Huppert
{"title":"Information Communication Technology as a Means of Enhancing the Well-being of Older People","authors":"Daniel M. Johnson, F. Huppert","doi":"10.3233/978-1-60750-731-4-11","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-11","url":null,"abstract":"","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130281436","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 : 1900-01-01DOI: 10.3233/978-1-60750-731-4-83
Sebastian J. F. Fudickar, Bettina Schnor, Juliane Felber, Franz J. Neyer, M. Lenz, Manfred Stede
{"title":"KopAL - An Orientation System For Patients With Dementia","authors":"Sebastian J. F. Fudickar, Bettina Schnor, Juliane Felber, Franz J. Neyer, M. Lenz, Manfred Stede","doi":"10.3233/978-1-60750-731-4-83","DOIUrl":"https://doi.org/10.3233/978-1-60750-731-4-83","url":null,"abstract":"","PeriodicalId":273135,"journal":{"name":"Behaviour Monitoring and Interpretation","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129787802","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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