Assessing the Quality of Activities in a Smart Environment

 

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Summary

Objectives: Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of activities of daily living that individuals perform in their own homes.

Methods: We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity.

Results: We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task.

Conclusions: Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.

• References

• 1 Rialle V, Ollivet C, Guigui C, Herve C. What do family caregivers of Alzheimer’s disease patients desire in smart home technologies?. Methods Inf Med 2008; 47: 63-69.
  Article in Thieme ConnectPubMedGoogle Scholar
• 2 Wadley V, Okonkwo O, Crowe M, Ross-Meadows LA. Mild Cognitive Impairment and everyday function: Evidence of reduced speed in performing instrumental activities of daily living. American Journal of Geriatric Psychiatry 2007; 16 (05) 416-424.
  PubMedGoogle Scholar
• 3 Cook DJ, Das SK. editors. Smart Environments: Technology, Protocols, and Applications. Wiley; 2004
  Google Scholar
• 4 Doctor F, Hagras H, Callaghan V. A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments. IEEE Transactions on Systems, Man, and Cybernetics, Part A. 2005; 35 (01) 55-56.
  CrossrefPubMedGoogle Scholar
• 5 Mihailidis A, Barbenl J, Fernie G. The efficacy of an intelligent cognitive orthosis to facilitate hand-washing by persons with moderate-to-severe dementia. Neuropsychological Rehabilitation 2004; 14 1/2 135-171.
  CrossrefPubMedGoogle Scholar
• 6 Philipose M, Fishkin K, Perkowitz M, Patterson D, Fox D, Kautz H, Hahnel D. Inferring activities from interactions with objects. IEEE Pervasive Computing 2004; 3 (04) 50-57.
  CrossrefPubMedGoogle Scholar
• 7 Sanchez D, Tentori M, Favela J. Activity recognition for the smart hospital. IEEE Intelligent Systems 2008; 23 (02) 50-57.
  PubMedGoogle Scholar
• 8 Wren C, Munguia-Tapia E. Toward scalable activity recognition for sensor networks. Proceedings of the Workshop on Location and Context-Awareness, 2006
  PubMedGoogle Scholar
• 9 Reisberg B, Finkel S, Overall J, Schmidt-Gollas N, Kanowski S, Lehfeld H. et al. The Alzheimer’s disease activities of daily living international scale (ASL-IS). International Psychogeriatrics. 2001; 13: 163-181.
  CrossrefPubMedGoogle Scholar
• 10 Schmitter-Edgecombe M, Woo E, Greeley D. Memory deficits, everyday functioning, and mild cognitive impairment. Proceedings of the Annual Rehabilitation Psychology Conference, 2008
  PubMedGoogle Scholar
• 11 Maurer U, Smailagic A, Siewiorek D, Deisher M. Activity recognition and monitoring using multiple sensors on different body positions. Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, 2006
  PubMedGoogle Scholar
• 12 Liao L, Fox D, Kautz H. Location-based activity recognition using relational Markov networks. Proceedings of the International Joint Conference on Artificial Intelligence, 2005 pp 773-778.
  PubMedGoogle Scholar
• 13 Fawcett T. ROC graphs: Notes and practical considerations for researchers. HP Labs Technical Report HPL-2003-4, 2004
  PubMedGoogle Scholar