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Towards automatic induction of abnormal behavioral patterns for recognizing mild cognitive impairment

Authors:

Zaffar Haider Janjua,

Daniele Riboni,

Claudio BettiniAuthors Info & Claims

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

Pages 143 - 148

https://doi.org/10.1145/2851613.2851687

Published: 04 April 2016 Publication History

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.

References

[1]

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.

[2]

B. Berry, G. Erdogan, and D. Trigueiros. Rule induction for financial modelling and model interpretation. In Proc. of HICSS. IEEE, 1995.

Digital Library

[3]

P. Clark and T. Niblett. The cn2 induction algorithm. Mach Learn J, 3(4):261--283, 1989.

Digital Library

[4]

W. W. Cohen. Fast effective rule induction. In Proc. of ICML, pages 115--123. Morgan Kaufmann, 1995.

[5]

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.

Digital Library

[6]

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.

[7]

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.

Digital Library

[8]

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.

[9]

J. Fürnkranz. Separate-and-conquer rule learning. Artif Intell Rev, 13(1):3--54, 1999.

Digital Library

[10]

J. Fürnkranz and G. Widmer. Incremental reduced error pruning. In Proc. of ICML, pages 70--77. Morgan Kaufmann, 1994.

[11]

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.

[12]

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.

Digital Library

[13]

S. Helal and C. Chen. The gator tech smart house: Enabling technologies and lessons learned. In Proc. of ICREATE. ACM, 2009.

Digital Library

[14]

J. Hipp, U. Güntzer, and G. Nakhaeizadeh. Algorithms for association rule mining - A general survey and comparison. SIGKDD Explor, 2(1):58--64, 2000.

Digital Library

[15]

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.

[16]

R. S. Michalski. On the quasi minimal solution of the general covering problem. In Proc. of FCIP, volume A3, pages 25--128, 1969.

[17]

R. C. Petersen. Mild cognitive impairment as a diagnostic entity. J Intern Med, 256(3):183--194, 2004.

[18]

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.

[19]

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.

[20]

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.

[21]

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.

[22]

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.

[23]

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.

[24]

World Health Organization. 10 facts on dementia. http://www.who.int/features/factfiles/dementia/en/, 2016.

Cited By

Chifu VPop CDemjen DSocaci RTodea DAntal MCioara TAnghel IAntal C(2022)Identifying and Monitoring the Daily Routine of Seniors Living at HomeSensors10.3390/s2203099222:3(992)Online publication date: 27-Jan-2022
https://doi.org/10.3390/s22030992
Zolfaghari SLoddo APes BRiboni D(2022)A Combination of Visual and Temporal Trajectory Features for Cognitive Assessment in Smart Home2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00078(343-348)Online publication date: Jun-2022
https://doi.org/10.1109/MDM55031.2022.00078
Arifoglu DWang YBouchachia A(2021)Detection of Dementia-Related Abnormal Behaviour Using Recursive Auto-EncodersSensors10.3390/s2101026021:1(260)Online publication date: 2-Jan-2021
https://doi.org/10.3390/s21010260
Show More Cited By

Index Terms

Towards automatic induction of abnormal behavioral patterns for recognizing mild cognitive impairment
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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Published In

cover image ACM Conferences

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

April 2016

2360 pages

ISBN:9781450337397

DOI:10.1145/2851613

Conference Chair:
Sascha Ossowski
University Rey Juan Carlos, Spain

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 04 April 2016

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SAC 2016

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SAC 2016: Symposium on Applied Computing

April 4 - 8, 2016

Pisa, Italy

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SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Chifu VPop CDemjen DSocaci RTodea DAntal MCioara TAnghel IAntal C(2022)Identifying and Monitoring the Daily Routine of Seniors Living at HomeSensors10.3390/s2203099222:3(992)Online publication date: 27-Jan-2022
https://doi.org/10.3390/s22030992
Zolfaghari SLoddo APes BRiboni D(2022)A Combination of Visual and Temporal Trajectory Features for Cognitive Assessment in Smart Home2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00078(343-348)Online publication date: Jun-2022
https://doi.org/10.1109/MDM55031.2022.00078
Arifoglu DWang YBouchachia A(2021)Detection of Dementia-Related Abnormal Behaviour Using Recursive Auto-EncodersSensors10.3390/s2101026021:1(260)Online publication date: 2-Jan-2021
https://doi.org/10.3390/s21010260
Zekri DDelot TThilliez MLecomte SDesertot M(2020)A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning TechniquesSensors10.3390/s2024711220:24(7112)Online publication date: 11-Dec-2020
https://doi.org/10.3390/s20247112
Mentis HMadjaroff GMassey ATrendafilova Z(2020)The Illusion of Choice in Discussing Cybersecurity Safeguards Between Older Adults with Mild Cognitive Impairment and Their CaregiversProceedings of the ACM on Human-Computer Interaction10.1145/34152354:CSCW2(1-19)Online publication date: 15-Oct-2020
https://dl.acm.org/doi/10.1145/3415235
Zekri DDelot TDesertot MLecomte SThilliez M(2020)Using Learning Techniques to Observe Elderly’s Behavior Changes over Time in Smart HomeThe Impact of Digital Technologies on Public Health in Developed and Developing Countries10.1007/978-3-030-51517-1_11(129-141)Online publication date: 23-Jun-2020
https://doi.org/10.1007/978-3-030-51517-1_11
Arifoglu DBouchachia A(2019)Detection of abnormal behaviour for dementia sufferers using Convolutional Neural NetworksArtificial Intelligence in Medicine10.1016/j.artmed.2019.01.00594:C(88-95)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1016/j.artmed.2019.01.005
Bian CKhan SMihailidis A(2018)Infusing Domain Knowledge to Improve the Detection of Alzheimer’s Disease from Everyday Motion BehaviourAdvances in Artificial Intelligence10.1007/978-3-319-89656-4_15(181-193)Online publication date: 6-Apr-2018
https://doi.org/10.1007/978-3-319-89656-4_15
Sfar HRamoly NBouzeghoub AFinance B(2017)CAREDAS: Context and Activity Recognition Enabling Detection of Anomalous SituationArtificial Intelligence in Medicine10.1007/978-3-319-59758-4_3(24-36)Online publication date: 30-May-2017
https://doi.org/10.1007/978-3-319-59758-4_3

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