Assessing the Quality of Activities in a Smart Environment

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.

Keywords

Activities of daily living - smart homes - activity recognition - health monitoring - machine learning

• 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.
• 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.
• 3 Cook DJ, Das SK. editors. Smart Environments: Technology, Protocols, and Applications. Wiley; 2004
• 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.
• 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.
• 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.
• 7 Sanchez D, Tentori M, Favela J. Activity recognition for the smart hospital. IEEE Intelligent Systems 2008; 23 (02) 50-57.
• 8 Wren C, Munguia-Tapia E. Toward scalable activity recognition for sensor networks. Proceedings of the Workshop on Location and Context-Awareness, 2006
• 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.
• 10 Schmitter-Edgecombe M, Woo E, Greeley D. Memory deficits, everyday functioning, and mild cognitive impairment. Proceedings of the Annual Rehabilitation Psychology Conference, 2008
• 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
• 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.
• 13 Fawcett T. ROC graphs: Notes and practical considerations for researchers. HP Labs Technical Report HPL-2003-4, 2004