Zaffar Haider Janjua
Claudio Bettini
ABSTRACT
Global demographics show a steady growth in the population of cognitively impaired patients. Consequently, the aging societies are looking to adopt smart technologies in healthcare services to early detect the onset of cognitive decline. These technologies include advanced methods that enable continuous in-house monitoring of the elderly's activities through unobtrusive sensing for recognizing abnormal behaviors that may indicate cognitive deficits. In an earlier work, we proposed a technique to detect the early symptoms of cognitive impairment by continuously monitoring the daily behavior of an elderly at home to recognize fine-grained abnormal behaviors. Recognition was based on rule-based descriptions of anomalies manually defined by domain experts. However, those rules strongly depend on the specific home environment, on the used sensors, and on the particular habits of the elderly; hence, their definition is time-expensive, and rules are not seamlessly portable to different environments. In order to address this issue, in this paper we propose a method to automatically learn the rule-based definitions of behavioral anomalies. In particular, we use a rule induction algorithm to infer those rules based on a dataset of activities and anomalies. We evaluated our method using a dataset of activities and abnormal behaviors carried out in an instrumented smart home. Our method achieves high precision and recall values, around 0.97 and 0.85, respectively, which are comparable to those obtained using manually-defined rules.
- C. Apte, E. Grossman, E. Pednault, B. Rosen, F. Tipu, and B. White. Insurance risk modeling using data mining technology. In Proc. of PADD, pages 39--47, 1998.Google Scholar
- B. Berry, G. Erdogan, and D. Trigueiros. Rule induction for financial modelling and model interpretation. In Proc. of HICSS. IEEE, 1995. Google ScholarDigital Library
- P. Clark and T. Niblett. The cn2 induction algorithm. Mach Learn J, 3(4):261--283, 1989. Google ScholarDigital Library
- W. W. Cohen. Fast effective rule induction. In Proc. of ICML, pages 115--123. Morgan Kaufmann, 1995.Google ScholarCross Ref
- D. J. Cook, A. S. Crandall, B. L. Thomas, and N. C. Krishnan. Casas: A smart home in a box. Computer, 46(7):62--69, 2013. Google ScholarDigital Library
- N. R. Daud and D. W. Corne. Human readable rule induction in medical data mining. In Proc. of ECC, LNEE, pages 787--798. Springer, 2009.Google ScholarCross Ref
- P. Dawadi, D. Cook, M. Schmitter-Edgecombe, and C. Parsey. Automated assessment of cognitive health using smart home technologies. Technol Health Care, 21(4):323--343, 2013. Google ScholarDigital Library
- P. Dawadi, D. J. Cook, and M. Schmitter-Edgecombe. Automated cognitive health assessment using smart home monitoring of complex tasks. IEEE Trans Syst Man Cybern, 43(6):1302--1313, 2013.Google ScholarCross Ref
- J. Fürnkranz. Separate-and-conquer rule learning. Artif Intell Rev, 13(1):3--54, 1999. Google ScholarDigital Library
- J. Fürnkranz and G. Widmer. Incremental reduced error pruning. In Proc. of ICML, pages 70--77. Morgan Kaufmann, 1994.Google ScholarCross Ref
- T. Giovannetti, B. M. Bettcher, L. Brennan, D. J. Libon, M. Burke, K. Duey, C. Nieves, and D. Wambach. Characterization of everyday functioning in mild cognitive impairment: A direct assessment approach. Dement Geriatr Cogn Disord, 25:359--365, 2008.Google ScholarCross Ref
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explor, (11(1):10--18, 2009. Google ScholarDigital Library
- S. Helal and C. Chen. The gator tech smart house: Enabling technologies and lessons learned. In Proc. of ICREATE. ACM, 2009. Google ScholarDigital Library
- J. Hipp, U. Güntzer, and G. Nakhaeizadeh. Algorithms for association rule mining - A general survey and comparison. SIGKDD Explor, 2(1):58--64, 2000. Google ScholarDigital Library
- B. E. Lyons, D. Austin, A. Seelye, J. Petersen, J. Yeargers, T. Riley, N. Sharma, N. Mattek, K. Wild, H. Dodge, and J. A. Kaye. Pervasive computing technologies to continuously assess alzheimer's disease progression and intervention efficacy. Front Aging Neurosci, 7:102, 2015.Google Scholar
- R. S. Michalski. On the quasi minimal solution of the general covering problem. In Proc. of FCIP, volume A3, pages 25--128, 1969.Google Scholar
- R. C. Petersen. Mild cognitive impairment as a diagnostic entity. J Intern Med, 256(3):183--194, 2004.Google ScholarCross Ref
- B. Reisberg, S. H. Ferris, M. J. de Leon, and T. Crook. The global deterioration scale for assessment of primary degenerative dementia. Am J Psychiatry, 139(9):1136--1139, 1982.Google ScholarCross Ref
- D. Riboni, C. Bettini, G. Civitarese, Z. H. Janjua, and V. Bulgari. From lab to life: Fine-grained behavior monitoring in the elderly's home. In Proc. of PerCom Workshops. IEEE Comp. Soc., 2015.Google ScholarCross Ref
- D. Riboni, C. Bettini, G. Civitarese, Z. H. Janjua, and R. Helaoui. Fine-grained recognition of abnormal behaviors for early detection of mild cognitive impairment. In Proc. of PerCom conference. IEEE Comp. Soc., 2015.Google ScholarCross Ref
- M. Schmitter-Edgecombe, C. Parsey, and R. Lamb. Development and psychometric properties of the instrumental activities of daily living: Compensation scale. Arch Clin Neuropsych, 29(8):776--792, 2014.Google ScholarCross Ref
- T. Suzuki and S. Murase. Influence of outdoor activity and indoor activity on cognition decline: Use of an infrared sensor to measure activity. Telemed E-Health, (16(6):686--690, 2010.Google Scholar
- K. V. Wild, N. Mattek, D. Austin, and J. A. Kaye. Are you sure?: Lapses in self-reported activities among healthy older adults reporting online. J Appl Gerontol, 2015.Google Scholar
- World Health Organization. 10 facts on dementia. http://www.who.int/features/factfiles/dementia/en/, 2016.Google Scholar
Index Terms
- Towards automatic induction of abnormal behavioral patterns for recognizing mild cognitive impairment
Recommendations
Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersFalling is a serious threat to the elderly people. One severe fall can cause hazardous problems like bone fracture or may lead to some permanent disability or even death. Thus, it has become the need of the hour to continuously monitor the activities of ...
Opportunistic sensing and detection of mild cognitive impairment
PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive EnvironmentsDementia is a growing healthcare problem, and it has become necessary to find a way to reduce the prevalence of this disease. Mild cognitive impairment is a risk factor for dementia, and being able to detect the onset of mild cognitive impairment gives ...
Assessing Ambient Assisted Living Support for Patients with Mild Cognitive Impairment: Results supported by a questionnaire applied to several healthcare stakeholders
ICSET 2021: 2021 5th International Conference on E-Society, E-Education and E-TechnologyAccording to the World Health Organization, about 50 million people worldwide suffer from dementia. Ten million new cases added every year [1]. Mild Cognitive Impairment (MCI) affects more than 15% of the population aged 65. MCI patients are mostly home-...
Comments