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Reasoning support for risk prediction and prevention in independent living

Published online by Cambridge University Press:  28 January 2011

A. MILEO ,
D. MERICO  and
R. BISIANI
A. MILEO
Affiliation:
NOMADIS Research Lab. Department of Informatics, Systems and Communication University of Milan-Bicocca, Viale Sarca 336/14, I–20126 Milan, Italy (e-mail: alessandra.mileo@disco.unimib.it, davide.merico@disco.unimib.it, roberto.bisiami@disco.unimib.it)
D. MERICO
Affiliation:
NOMADIS Research Lab. Department of Informatics, Systems and Communication University of Milan-Bicocca, Viale Sarca 336/14, I–20126 Milan, Italy (e-mail: alessandra.mileo@disco.unimib.it, davide.merico@disco.unimib.it, roberto.bisiami@disco.unimib.it)
R. BISIANI
Affiliation:
NOMADIS Research Lab. Department of Informatics, Systems and Communication University of Milan-Bicocca, Viale Sarca 336/14, I–20126 Milan, Italy (e-mail: alessandra.mileo@disco.unimib.it, davide.merico@disco.unimib.it, roberto.bisiami@disco.unimib.it)
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Abstract

In recent years there has been a growing interest in solutions for the delivery of clinical care for the elderly because of the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. We prototyped a system called Secure and INDependent lIving (SINDI), focused on (a) collecting a limited amount of data about the person and the environment through Wireless Sensor Networks (WSN), and (b) inferring from these data enough information to support caregivers in understanding patients' well-being and in predicting possible evolutions of their health. Our hierarchical logic-based model of health combines data from different sources, sensor data, tests results, common-sense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. This paper describes how these issues have been targeted in the application scenario of the SINDI system.

Keywords

answer set programmingwireless sensor networksindependent livingpredictioncontext-awarenessknowledge representationdependency graph
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 11 , Special Issue 2-3: The 24th International Conference on Logic Programming (ICLP 2008) , March 2011 , pp. 361 - 395
Copyright
Copyright © Cambridge University Press 2011

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References

Akyildiz, I. F., Weilian, S., Sankarasubramaniam, Y. and Cayirci, E. E. 2002. A survey on sensor networks. IEEE Communications Magazine 40 (8), 102–114.CrossRefGoogle Scholar
Al-Omari, S. and Shi, W. 2007. Toward highly available WSNS for assisted living. In Proceedings of ACM/SIGMOBILE HealthNet 2007, San Juan, Puerto Rico. ACM Press, New York, NY, USA, 31–36.Google Scholar
Bo, W., Han-ying, H. and Wen, F. 2008. An improved leach protocol for data gathering and aggregation in wireless sensor networks. International Conference on Computer and Electrical Engineering, 2008, Phuket, Thailand. IEEE Computer Society, Washington DC, USA, 398–401.CrossRefGoogle Scholar
Boger, J., Hoey, J., Poupart, P., Boutilier, C., Fernie, G. and Mihailidis, A. 2006. A planning system based on Markov decision processes to guide people with dementia through activities of daily living. IEEE Transactions on Information Technology in Biomedicine 10 (2), 323–333.CrossRefGoogle ScholarPubMed
Brewka, G., Niemelä, I. and Syrjänen, T. 2004. Logic programs with ordered disjunction. Computational Intelligence, 20 (2), 333–357.CrossRefGoogle Scholar
Connell, B. R. and Wolf, S. L. 1997. Environmental and behavioral circumstances associated with falls at home among healthy elderly individuals. Archives of Physical Medicine and Rehabilitation 78, 179–186.CrossRefGoogle Scholar
Fleming, K. C., Evans, J. M., Weber, D. C. and Chutka, D. S. 1995. Practical functional assessment of elderly persons: A primary-care approach. Mayo Clinic Proceedings 70 (9), 890–910.CrossRefGoogle Scholar
Folstein, M. F., Folstein, S. E. and McHugh, P. R. 1975. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Psychiatric Research 12 (3)189–198.CrossRefGoogle ScholarPubMed
Gebser, M., Kaufmann, B., Neumann, A. and Schaub, T. 2007. Clasp: A conflict-driven answer set solver. In Ninth International Conference on Logic Programming and Nonmonotonic Reasoning, Tempe, AZ, USA. Springer-Verlag, New York, NY, USA, 260–265.CrossRefGoogle Scholar
Gebser, M., Schaub, T. and Thiele, S. 2007. Gringo : A new grounder for answer set programming. In LPNMR. 266–271.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proceedings of the Fifth International Conference on Logic Programming, Seattle, Washington, 1070–1080.Google Scholar
Guigoz, Y., Vellas, B. and Garry, P. 1994. Mini nutritional assessment: A practical assessment tool for grading the nutritional state of elderly patients. Facts and Research in Gerontology 2, 15–59.Google Scholar
Haigh, K. Z. and Yanco, H. 2002. Automation as caregiver: A survey of issues and technologies. In Proceedings of the AAAI-02 Workshop “Automation as Caregiver”, Edmonton, Alberta, Canada. AAAI 2002, 39–53.Google Scholar
Heinzelman, W., Chandrakasan, A. and Balakrishnan, H. 2000. Energy-efficient communication protocol for wireless sensor networks. In International Conference on System Sciences. IEEE Computer Society, Washington, DC, USA, 8020.Google Scholar
Katz, S., Downs, H., Cash, H. and Grotz, R. 1970. Progress in development of the index of ADL. Gerontologist 10 (1), 20–30.CrossRefGoogle ScholarPubMed
Lawton, M. 1988. Scales to measure competence in everyday activities. Psychopharmacological Bulletin 24 (4), 609–614.Google ScholarPubMed
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S. and Scarcello, F. 2006. The DLV system for knowledge representation and reasoning. ACM Transactions on Computer Logic 7 (3), 499–562.CrossRefGoogle Scholar
Liao, L., Patterson, D., Fox, D. and Kautz, H. 2004. Behavior recognition in assisted cognition. In Proceedings the AAAI-04 Workshop on Supervisory Control of Learning and Adaptive Systems, San Jose, CA, 30–35.Google Scholar
Malan, D., Fulford-Jones, T., Wesh, M. and Moulton, S. 2004. Codeblue: An ad hoc sensor network infrastructure for emergency medical care. In MobySys Workshop on Applications of Mobile Embedded Systems, Boston, MA, USA, 12–14.Google Scholar
Marek, W. and Truszczynski, M. 1989. Stable semantics for logic programs and default theories. In NACLP, 243–256.Google Scholar
Mellarkod, V. S., Gelfond, M. and Zhang, Y. 2008. Integrating answer set programming and constraint logic programming. Annals of Mathematics and Artificial Intelligence 53 (1–4), 251–287.CrossRefGoogle Scholar
Mileo, A. and Bisiani, R. 2009. Context-aware prediction and prevention to extend healthy life years: Preventing falls. In Proc. of the IJCAI Workshop on Intelligent Systems for Assisted Cognition, Pasadena, CA, USA, 56–63.Google Scholar
Ness, K. K., Gurney, J. G. and Ice, G. H. 2003. Screening, education, and associated behavioral responses to reduce risk for falls among poeple over age 65 years attending a community health fair. Physical Therapy 83 (7), 631–637.CrossRefGoogle Scholar
Niemelä, I. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25 (3–4), 241–273.CrossRefGoogle Scholar
Niemelä, I. and Simons, P. 2001. Extending the smodels system with cardinality and weight constraints. In Logic-Based Artificial Intelligence, Minker, J., Ed. Kluwer, Norwell, MA, USA, 491–521.Google Scholar
Pollack, M. E. 2005. Intelligent technology for an aging population: The use of ai to assist elders with cognitive impairment. AI Magazine 26 (2), 9–24.Google Scholar
Pollack, M. E., Brown, L. E., Colbry, D., McCarthy, C. E., Orosz, C., Peintner, B., Ramakrishnan, S. and Tsamardinos, I. 2003. Autominder: An intelligent cognitive orthotic system for people with memory impairment. Robotics and Autonomous Systems 44 (3–4), 273–282.CrossRefGoogle Scholar
Rubenstein, L. 2006. Falls in older people: Epidemiology, risk factors and strategies for prevention. Age Ageing 2, 37–41.CrossRefGoogle Scholar
Rudary, M., Singh, S. and Pollack, M. E. 2004. Adaptive cognitive orthotics: Combining reinforcement learning and constraint-based temporal reasoning. In Proceedings of ICML 2004, Banff, Alberta, Canada, July 4–8. ACM, 91–98.Google Scholar
Ryan, N. S., Pascoe, J. and Morse, D. R. 1998. Enhanced reality fieldwork: The context-aware archaeological assistant. In Computer Applications in Archaeology 1997, Gaffney, V., van Leusen, M. and Exxon, S., Eds. British Archaeological Reports, Oxford, Tempus Reparatum.Google Scholar
Savvides, A., Han, C.-C. and Strivastava, M. B. 2001. Dynamic fine-grained localization in ad-hoc networks of sensors. In MobiCom '01: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking. ACM, New York, NY, USA, 166–179.CrossRefGoogle Scholar
Schilit, B., Adams, N. and Want, R. 1994. Context-aware computing applications. In Proceedings of the Workshop on Mobile Computing Systems and Applications, Santa Cruz, CA, USA, 8–9 Dec. IEEE Computer Society, Washington, DC, USA, 85–90.Google Scholar
Simons, P., Niemelä, I. and Soininen, T. 2002. Extending and implementing the stable model semantics. Artificial Intelligence Journal 138 (1–2), 181–234.CrossRefGoogle Scholar
Stuck, A. E., Walthert, J. M., Nikolaus, T., Christophe, J. B., Hohmann, C. and Beck, J. C. 1999. Risk factors for functional status decline in community-living elderly people: A systematic literature review. Social Science and Medicine 48 (4), 445–469.CrossRefGoogle ScholarPubMed
Tinetti, M., Williams, T. and Mayewski, P. 2002. Fall risk index for elderly patients based on number of chronic disabilities. American Journal of Medicine 80, 429–434.CrossRefGoogle Scholar
Tonelli, M. R. 2001. The limits of evidence-based medicine. Respiratory Care 46 (12) (December), 1435–40.Google ScholarPubMed
Wood, A., Virone, G., Doan, T., Cao, Q., Selavo, L., Wu, Y., Fang, L., He, Z., Lin, S. and Stankovic, J. 2006. Alarm-Net: Wireless Sensor Networks for Assisted-Living and Residential Monitoring. Tech. Rep. CS-2006-11, Department of Computer Science, University of Virginia, Charlottesville, VA.Google Scholar
Yamamoto, S., Mogi, N., Umegaki, H., Suzuki, Y., Ando, F., Shimokata, H. and Iguchi, A. 2004. The clock drawing test as a valid screening method for mild cognitive impairment. Dementia and Geriatric Cognitive Disordorders 18, 172–179.CrossRefGoogle ScholarPubMed
Yesavage, J., Brink, T., Rose, T., Lum, O., Huang, V., Adey, M. and Leirer, V. 1982–1983. Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research 17 (1), 37–49.CrossRefGoogle Scholar