Simulation of Smart Home Activity Datasets

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.
LanguageEnglish
Pages14162-14179
JournalSensors
Volume15
Issue number6
DOIs
Publication statusPublished - 16 Jun 2015

Fingerprint

sensors
Sensors
simulation
chronic conditions
Long-Term Care
environment simulation
Monitoring
recommendations
Quality of Life
availability
Technology
Delivery of Health Care
resources
Costs and Cost Analysis
Aging of materials
Datasets
Availability
costs
Population
Costs

Keywords

  • monitoring
  • simulation
  • smart environments
  • visualization

Cite this

Synnott, Jonathan ; Nugent, Chris ; Jeffers, Paul. / Simulation of Smart Home Activity Datasets. In: Sensors. 2015 ; Vol. 15, No. 6. pp. 14162-14179.
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abstract = "A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.",
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note = "Reference text: United Nations. World Population Ageing. 2013. Available online: http://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013.pdf (accessed on 5 March 2015). Pollack, M.E. Intelligent Technology for an Aging Population: The Use of AI to Assist Elders with Cognitive Impairment. AI Mag. 2005, 26, 9–24. [Google Scholar] British Heart Foundation. Sedentary Behaviour Evidence Briefing. Available online: http://www.bhfactive.org.uk/files/525/sedentary_evidence_briefing.pdf (accessed on 19 January 2015). Sedentary Behaviour Research Network. Letter to the Editor: Standardized Use of the Terms “Sedentary” and “Sedentary Behaviours”. Appl. Physiol. Nutr. Metab. 2012, 37, 540–542. [Google Scholar] Helal, S.; Lee, J.W.; Hossain, S.; Kim, E.; Hagras, H.; Cook, D. Persim—Simulator for Human Activities in Pervasive Spaces. In Proceedings of the 2011 Seventh International Conference on Intelligent Environments, Nottingham, UK, 25–28 July 2011; pp. 192–199. Helal, S.; Kim, E.; Hossain, S. Scalable Approaches to Activity Recognition Research. In Proceedings of the 8th International Conference Pervasive Workshop, Helsinki, Finland, 17–20 May 2010; pp. 450–453. Alzheimer’s Society. What Is Dementia? Available online: http://www.alzheimers.org.uk/site/scripts/documents_info.php?documentID=106 (accessed on 4 March 2015). Von Strauss, E.; Viitanen, M.; de Ronchi, D.; Winblad, B.; Fratiglioni, L. Aging and the Occurrence of Dementia. Arch. Neurol. 1999, 56, 587–592. [Google Scholar] [CrossRef] [PubMed] Alzheimer’s Disease International. World Alzheimer Report. 2009. Available online: http://www.alz.co.uk/research/files/WorldAlzheimerReport.pdf (accessed on 14 April 2015). Alzheimer’s Society. Alzheimer’s Society Dementia. 2012 Report. Available online: http://www.alzheimers.org.uk/site/scripts/download_info.php?fileID=1389A (accessed on 15 April 2015). Skubic, M.; Alexander, G.; Popescu, M.; Rantz, M.; Keller, J. A smart home application to eldercare: Current status and lessons learned. Technol. Health Care 2009, 17, 183–201. [Google Scholar] [PubMed] McGee-Lennon, M.R.; Gray, P. Keeping everyone happy: Multiple stakeholder requirements for home care technology. In Proceedings of the 3rd International ICST Conference on Pervasive Computing Technologies for Healthcare, London, UK, 1–3 April 2009; pp. 1–3. World Health Organization. Global Recommendations on Physical Activity for Health; WHO: Gevena, Switzerland, 2010. [Google Scholar] World Health Organization. What Is Moderate-Intensity and Vigorous-Intensity Physical Activity? Avaialble online: http://www.who.int/dietphysicalactivity/physical_activity_intensity/en/ (accessed on 19 January 2015). Matthews, C.E.; Chen, K.Y.; Freedson, P.S.; Buchowski, M.S.; Beech, B.M.; Pate, R.R.; Troiano, R.P. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am. J. Epidemiol. 2008, 167, 875–881. [Google Scholar] [CrossRef] [PubMed] Katzmarzyk, P.T. Physical activity, sedentary behavior, and health: Paradigm paralysis or paradigm shift? Diabetes 2010, 59, 2717–2725. [Google Scholar] [CrossRef] [PubMed] Pate, R.R.; O’Neill, J.R.; Lobelo, F. The evolving definition of “sedentary”. Exerc. Sport Sci. Rev. 2008, 36, 173–178. [Google Scholar] [CrossRef] [PubMed] Owen, N.; Healy, G.N.; Matthews, C.E.; Dunstan, D.W. Too much sitting: The population health science of sedentary behavior. Exerc. Sport Sci. Rev. 2010, 38, 105–113. [Google Scholar] [CrossRef] [PubMed] Healy, G.N.; Clark, B.K.; Winkler, E.A.; Gardiner, P.A.; Brown, W.J.; Matthews, C.E. Measurement of adults’ sedentary time in population-based studies. Am. J. Prev. Med. 2011, 41, 216–227. [Google Scholar] [CrossRef] [PubMed] Cook, D.J.; Das, S.K. How smart are our environments? An updated look at the state of the art. Pervasive Mob. Comput. 2007, 3, 53–73. [Google Scholar] [CrossRef] Noury, N.; Poujaud, J.; Fleury, A.; Nocua, R.; Haddidi, T.; Rumeau, P. Smart Sweet Home… A Pervasive Environment for Sensing our Daily Activity? In Activity Recognition in Pervasive Intelligent Environments; Chen, L., Nugent, C.D., Biswas, J., Hoey, J., Eds.; Atlantis Press: Paris, France, 2011; pp. 187–208. [Google Scholar] Helal, S.; Mann, W.; El-Zabadani, H.; King, J.; Kaddoura, Y.; Jansen, E. The Gator Tech Smart House: A programmable pervasive space. Computer 2005, 38, 50–60. [Google Scholar] [CrossRef] Niazmand, K.; Tonn, K.; Kalaras, A.; Kammermeier, S.; Boetzel, K.; Mehrkens, J.H.; Lueth, T.C. A measurement device for motion analysis of patients with Parkinson’s disease using sensor based smart clothes. 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Simulation of Smart Home Activity Datasets. / Synnott, Jonathan; Nugent, Chris; Jeffers, Paul.

In: Sensors, Vol. 15, No. 6, 16.06.2015, p. 14162-14179.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Simulation of Smart Home Activity Datasets

AU - Synnott, Jonathan

AU - Nugent, Chris

AU - Jeffers, Paul

N1 - Reference text: United Nations. World Population Ageing. 2013. Available online: http://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013.pdf (accessed on 5 March 2015). Pollack, M.E. Intelligent Technology for an Aging Population: The Use of AI to Assist Elders with Cognitive Impairment. AI Mag. 2005, 26, 9–24. [Google Scholar] British Heart Foundation. Sedentary Behaviour Evidence Briefing. Available online: http://www.bhfactive.org.uk/files/525/sedentary_evidence_briefing.pdf (accessed on 19 January 2015). Sedentary Behaviour Research Network. Letter to the Editor: Standardized Use of the Terms “Sedentary” and “Sedentary Behaviours”. Appl. Physiol. Nutr. Metab. 2012, 37, 540–542. [Google Scholar] Helal, S.; Lee, J.W.; Hossain, S.; Kim, E.; Hagras, H.; Cook, D. Persim—Simulator for Human Activities in Pervasive Spaces. In Proceedings of the 2011 Seventh International Conference on Intelligent Environments, Nottingham, UK, 25–28 July 2011; pp. 192–199. Helal, S.; Kim, E.; Hossain, S. Scalable Approaches to Activity Recognition Research. In Proceedings of the 8th International Conference Pervasive Workshop, Helsinki, Finland, 17–20 May 2010; pp. 450–453. Alzheimer’s Society. What Is Dementia? Available online: http://www.alzheimers.org.uk/site/scripts/documents_info.php?documentID=106 (accessed on 4 March 2015). Von Strauss, E.; Viitanen, M.; de Ronchi, D.; Winblad, B.; Fratiglioni, L. Aging and the Occurrence of Dementia. Arch. Neurol. 1999, 56, 587–592. [Google Scholar] [CrossRef] [PubMed] Alzheimer’s Disease International. World Alzheimer Report. 2009. Available online: http://www.alz.co.uk/research/files/WorldAlzheimerReport.pdf (accessed on 14 April 2015). Alzheimer’s Society. Alzheimer’s Society Dementia. 2012 Report. Available online: http://www.alzheimers.org.uk/site/scripts/download_info.php?fileID=1389A (accessed on 15 April 2015). Skubic, M.; Alexander, G.; Popescu, M.; Rantz, M.; Keller, J. A smart home application to eldercare: Current status and lessons learned. Technol. Health Care 2009, 17, 183–201. [Google Scholar] [PubMed] McGee-Lennon, M.R.; Gray, P. Keeping everyone happy: Multiple stakeholder requirements for home care technology. In Proceedings of the 3rd International ICST Conference on Pervasive Computing Technologies for Healthcare, London, UK, 1–3 April 2009; pp. 1–3. World Health Organization. Global Recommendations on Physical Activity for Health; WHO: Gevena, Switzerland, 2010. [Google Scholar] World Health Organization. What Is Moderate-Intensity and Vigorous-Intensity Physical Activity? Avaialble online: http://www.who.int/dietphysicalactivity/physical_activity_intensity/en/ (accessed on 19 January 2015). Matthews, C.E.; Chen, K.Y.; Freedson, P.S.; Buchowski, M.S.; Beech, B.M.; Pate, R.R.; Troiano, R.P. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am. J. Epidemiol. 2008, 167, 875–881. [Google Scholar] [CrossRef] [PubMed] Katzmarzyk, P.T. Physical activity, sedentary behavior, and health: Paradigm paralysis or paradigm shift? Diabetes 2010, 59, 2717–2725. [Google Scholar] [CrossRef] [PubMed] Pate, R.R.; O’Neill, J.R.; Lobelo, F. The evolving definition of “sedentary”. Exerc. Sport Sci. Rev. 2008, 36, 173–178. [Google Scholar] [CrossRef] [PubMed] Owen, N.; Healy, G.N.; Matthews, C.E.; Dunstan, D.W. Too much sitting: The population health science of sedentary behavior. Exerc. Sport Sci. Rev. 2010, 38, 105–113. [Google Scholar] [CrossRef] [PubMed] Healy, G.N.; Clark, B.K.; Winkler, E.A.; Gardiner, P.A.; Brown, W.J.; Matthews, C.E. Measurement of adults’ sedentary time in population-based studies. Am. J. Prev. Med. 2011, 41, 216–227. [Google Scholar] [CrossRef] [PubMed] Cook, D.J.; Das, S.K. How smart are our environments? An updated look at the state of the art. Pervasive Mob. Comput. 2007, 3, 53–73. [Google Scholar] [CrossRef] Noury, N.; Poujaud, J.; Fleury, A.; Nocua, R.; Haddidi, T.; Rumeau, P. Smart Sweet Home… A Pervasive Environment for Sensing our Daily Activity? In Activity Recognition in Pervasive Intelligent Environments; Chen, L., Nugent, C.D., Biswas, J., Hoey, J., Eds.; Atlantis Press: Paris, France, 2011; pp. 187–208. [Google Scholar] Helal, S.; Mann, W.; El-Zabadani, H.; King, J.; Kaddoura, Y.; Jansen, E. The Gator Tech Smart House: A programmable pervasive space. Computer 2005, 38, 50–60. [Google Scholar] [CrossRef] Niazmand, K.; Tonn, K.; Kalaras, A.; Kammermeier, S.; Boetzel, K.; Mehrkens, J.H.; Lueth, T.C. A measurement device for motion analysis of patients with Parkinson’s disease using sensor based smart clothes. In Proceedings of the 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Dublin, Ireland, 23–26 May 2011; pp. 9–16. Counsell, J.; Puybaraud, M.-C. Real-Time Data and Analysis of the Use of Office Space. In Proceedings of the 2007 11th International Conference Information Visualization (IV ’07), Zurich, Switzerland, 4–6 July 2007; pp. 579–583. Ivanov, Y.A.; Wren, C.R.; Sorokin, A.; Kaur, I. Visualizing the History of Living Spaces. IEEE Trans. Vis. Comput. Graph. 2007, 13, 1153–1160. [Google Scholar] [CrossRef] [PubMed] Chowdhury, A.K.R.; Chellappa, R. A Factorization Approach for Activity Recognition. In Proceedings of the 2003 Conference on Computer Vision and Pattern Recognition Workshop, Madison, WI, USA, 16–22 June 2003; Volume 4, p. 41. Georis, B. A video interpretation platform applied to bank agency monitoring. In Proceedings of the Intelligent Distributed Surveillance Systems (IDSS-04), London, UK, 23 February 2004; Volume 2004, pp. 46–50. Demiris, G.; Hensel, B.K.; Skubic, M.; Rantz, M. Senior residents’ perceived need of and preferences for “smart home” sensor technologies. Int. J. Technol. Assess. Health Care 2008, 24, 120–124. [Google Scholar] [CrossRef] [PubMed] Paré, G.; Jaana, M.; Sicotte, C. Systematic review of home telemonitoring for chronic diseases: The evidence base. J. Am. Med. Inform. Assoc. 2007, 14, 269–277. [Google Scholar] [CrossRef] [PubMed] Gil, N.; Hine, N.; Judson, A. Lifestyle Monitoring System to Improve the Well-being of the Elderly. Rev. Av. Sist. Inform. 2006, 3, 39–44. [Google Scholar] Chan, M.; Campo, E.; Estève, D.; Fourniols, J.-Y. Smart homes—Current features and future perspectives. Maturitas 2009, 64, 90–97. 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PY - 2015/6/16

Y1 - 2015/6/16

N2 - A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

AB - A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

KW - monitoring

KW - simulation

KW - smart environments

KW - visualization

U2 - 10.3390/s150614162

DO - 10.3390/s150614162

M3 - Article

VL - 15

SP - 14162

EP - 14179

JO - Sensors

T2 - Sensors

JF - Sensors

SN - 1424-8220

IS - 6

ER -