Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living

Prabitha Urwyler, Reto Stucki, Luca Rampa, René Müri, Urs P Mosimann, Tobias Nef, Prabitha Urwyler, Reto Stucki, Luca Rampa, René Müri, Urs P Mosimann, Tobias Nef

Abstract

Cognitive impairment due to dementia decreases functionality in Activities of Daily Living (ADL). Its assessment is useful to identify care needs, risks and monitor disease progression. This study investigates differences in ADL pattern-performance between dementia patients and healthy controls using unobtrusive sensors. Around 9,600 person-hours of activity data were collected from the home of ten dementia patients and ten healthy controls using a wireless-unobtrusive sensors and analysed to detect ADL. Recognised ADL were visualized using activity maps, the heterogeneity and accuracy to discriminate patients from healthy were analysed. Activity maps of dementia patients reveal unorganised behaviour patterns and heterogeneity differed significantly between the healthy and diseased. The discriminating accuracy increases with observation duration (0.95 for 20 days). Unobtrusive sensors quantify ADL-relevant behaviour, useful to uncover the effect of cognitive impairment, to quantify ADL-relevant changes in the course of dementia and to measure outcomes of anti-dementia treatments.

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1. Activity maps of a healthy…
Figure 1. Activity maps of a healthy control (left) and a dementia patient (right) visualized from data measured continuously for 20 consecutive days.
Figure 2. Poincaré Plot of a healthy…
Figure 2. Poincaré Plot of a healthy control (Age = 79 years, female, MMSE = 29) (left) and an Alzheimer patient (Age = 84 years, female, MMSE = 20) (right) from all activity of daily living (ADL) related datasets of 20 consecutive days (Δtime = 24 hours).
The blue dotted line indicates the long axis, the red line indicates the short axis. The centroid corresponds to the point where the long axis intersects the short axis.
Figure 3. Discriminating ability between healthy controls…
Figure 3. Discriminating ability between healthy controls and dementia patients in dependence of measurement duration, where days of measurement refer to 20 consecutive days.
Figure 4. Floor plan of an apartment…
Figure 4. Floor plan of an apartment showing placement of sensor boxes (red circle).
Each sensor box (inset photo) captures light, temperature, humidity, movement and acceleration. The floor plan was created using Sweet Home 3D version 5.2a. Sweet Home 3D, Copyright (c) 2005–2016 Emmanuel PUYBARET/eTeks <  info@eteks.com >. 

References

    1. Giebel C. M. et al.. Deterioration of basic activities of daily living and their impact on quality of life across different cognitive stages of dementia: a European study. Int.Psychogeriatr. 26, 1283–1293 (2014).
    1. Volicer L., Harper D. G., Manning B. C., Goldstein R. & Satlin A. Sundowning and circadian rhythms in Alzheimer’s disease. Am. J. Psychiatry. 158, 704–711 (2001).
    1. Katz S. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J. Am. Geriatr. Soc. 31, 721–727 (1983).
    1. Katz S., Ford A. B., Moskowitz R. W., Jackson B. A. & Jaffe M. W. Studies of Illness in the Aged. The index of ADL: A standardized measure of biological and psychosocial function. JAMA. 185, 914–919 (1963).
    1. Lawton M. P. & Brody E. M. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 9, 179–186 (1969).
    1. Lawton M. P. The functional assessment of elderly people. J. Am. Geriatr. Soc. 19, 465–481 (1971).
    1. Logsdon R. G., Gibbons L. E., McCurry S. M. & Teri L. Quality of life in Alzheimer’s disease: Patient and caregiver reports. J. Ment. Health. Aging. 5, 21–32 (1999).
    1. Sikkes S. A. et al.. Do instrumental activities of daily living predict dementia at 1- and 2-year follow-up? Findings from the Development of Screening guidelines and diagnostic Criteria for Predementia Alzheimer’s disease study. J. Am. Geriatr. Soc. 59, 2273–2281 (2011).
    1. Pincus T., Summey J. A., Soraci S. A., Wallston K. A. & Hummon N. P. Assessment of patient satisfaction in activities of daily living using a modified Stanford Health Assessment Questionnaire. Arthritis. Rheum. 26, 1346–1353 (1983).
    1. Wade D. T. & Collin C. The Barthel ADL Index: a standard measure of physical disability? Int. Disabil. Stud. 10, 64–67 (1988).
    1. Carlsson G., Haak M., Nygren C. & Iwarsson S. Self-reported versus professionally assessed functional limitations in community-dwelling very old individuals. Int. J. Rehabil. Res. 35, 299–304 (2012).
    1. Lyons B. E. et al.. Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy. Front. Aging. Neurosci. 7, 102, doi: 10.3389/fnagi.2015.00102 (2015).
    1. Schwenk M. et al.. Sensor-Derived Physical Activity Parameters Can Preidct Future Falls in People with Dementia. Gerontology. 60, 483–492 (2014).
    1. Pol M. et al.. Older People’s Perspectives Regarding the Use of Sensor Monitoring in Their Home. Gerontologist. 56, 485–493 (2016).
    1. Dawadi P. N., Cook D. J., Schmitter-Edgecombe M. & Parsey C. Automated assessment of cognitive health using smart home technologies. Technol. Health. Care. 21, 323–343 (2013).
    1. Kaye J. Home-based technologies: A new paradigm for conducting dementia prevention trials. Alzheimers Dement. 4, S60–S66 (2008).
    1. Medjahed H., Istrate D., Boudy J., Baldinger J.-L. & Dorizzi B. A pervasive multi-sensor data fusion for smart home healthcare monitoring. Paper presented at FUZZ 2011: IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, doi: 10.1109/FUZZY.2011.6007636.
    1. Kaye J. A. et al.. Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging. J. Gerontol. B Psychol. Sci. Soc. Sci. 66B Suppl 1, i180–190 (2011).
    1. Ordonez F. J., P.D. T. & Sanchis A. Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13, 5460–5477 (2013).
    1. Peetoom K. K., Lexis M. A., Joore M., Dirksen C. D. & De Witte L. P. Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil. Rehabil. Assist.Technol. 10, 271–294 (2015).
    1. Stucki R. A. et al.. A web-based non-intrusive ambient system to measure and classify activities of daily living. J. Med. Internet Res. 16, e175, doi: 10.2196/jmir.3465 (2014).
    1. Naranjo-Hernandez D., Roa L. M., Reina-Tosina J. & Estudillo-Valderrama M. A. SoM: a smart sensor for human activity monitoring and assisted healthy ageing. IEEE Trans. Biomed. Eng. 59, 3177–3184, doi: 10.1109/TBME.2012.2206384 (2012).
    1. Bang S. L., Kim M., Song S. & Park S. J. Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors. Paper presented at EMBS 2008: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada. doi: 10.1109/IEMBS.2008.4650386.
    1. van Kasteren T. L. M., Englebienne G. & Kröse B. J. A. Activity recognition using semi-Markov models on real world smart home datasets. J. Ambient Intell. Smart Environ. 2, 311–325 (2010).
    1. Berenguer M., Giordani M., Giraud-By F. & Noury N. Automatic detection of activities of daily living from detecting and classifying electrical events on the residential power line. Paper presented at HealthCom 2008:10th IEEE Intl. Conf. on e-Health Networking, Applications and Services, Singapore, doi: 10.1109/HEALTH.2008.4600104.
    1. Witten I. H., Frank E. & Hall M. A. Data Mining: Practical Machine Learning Tools and Techniques. 3 edn, (Morgan Kaufmann, 2011).
    1. Urwyler P. et al.. Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers. Biomed. Eng. Online 14, 54, doi: 10.1186/s12938-015-0050-4 (2015).
    1. Guzik P. et al.. Heart rate variability by Poincaré plot and spectral analysis in young healthy subjects and patients with type 1 diabetes. Folia Cardiol. 12, 64–67 (2005).
    1. Tulppo M. P., Makikallio T. H., Takala T. E., Seppanen T. & Huikuri H. V. Quantitative beat-to-beat analysis of heart rate dynamics during exercise. Am. J. Physiol. 271, H244–252 (1996).
    1. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874 (2006).
    1. Khattak A. M. et al.. Towards smart homes using low level sensory data. Sensors (Basel) 11, 11581–11604, doi: 10.3390/s111211581 (2011).
    1. Nef T. et al.. Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data. Sensors (Basel) 15, 11725–11740, doi: 10.3390/s150511725 (2015).
    1. Korpelainen J. T., Sotaniemi K. A., Mäkikallio A., Huikuri H. V. & Myllylä V. V. Dynamic behavior of heart rate in ischemic stroke. Stroke 30, 1008–1013 (1999).
    1. Tekin S., Fairbanks L. A., O’Connor S., Rosenberg S. & Cummings J. L. Activities of daily living in Alzheimer’s disease: neuropsychiatric, cognitive, and medical illness influences. Am. J. Geriatr. Psychiatry. 9, 81–86 (2001).
    1. Winblad B. et al.. Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J. Intern. Med. 256, 240–246, doi: 10.1111/j.1365-2796.2004.01380.x (2004).
    1. Paavilainen P. et al.. Circadian activity rhythm in demented and non‐demented nursing‐home residents measured by telemetric actigraphy. J. Sleep Res. 14, 61–68 (2005).
    1. Grossglauser M. & Saner H. Data-driven healthcare: from patterns to actions. Eur. J. Prev. Cardiol. 21, 14–17, doi: 10.1177/2047487314552755 (2014).
    1. Seelye A. M., Schmitter-Edgecombe M., Cook D. J. & Crandall A. Naturalistic assessment of everyday activities and prompting technologies in mild cognitive impairment. J. Int. Neuropsychol. Soc. 19, 442–452, doi: 10.1017/S135561771200149X (2013).
    1. Folstein M. F., Folstein S. E. & McHugh P. R. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975).
    1. Morris J. C. et al.. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology 39, 1159–1165 (1989).
    1. Morris J. C. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412–2414 (1993).
    1. WHO. The ICD-10 classification of mental and behavioural disorders: Clinical descriptions and diagnostic guidelines. (World Health Organization, Geneva, 1992).
    1. McKhann G. et al.. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944 (1984).
    1. Shulman K. I. Clock-drawing: is it the ideal cognitive screening test? Int.J.Geriatr. Psychiatry. 15, 548–561 (2000).
    1. Reitan R. M. Trail Making Test: Manual for administration and scoring. (Reitan Neuropsychology Laboratory, 1992).
    1. Podsiadlo D. & Richardson S. The timed “Up & Go”: A test of basic functional mobility for frail elderly persons. J. Am. Geriatr. Soc. 39, 142–148 (1991).
    1. Brennan M., Palaniswami M. & Kamen P. Poincare plot interpretation using a physiological model of HRV based on a network of oscillators. Am. J. Physiol. Heart Circ. Physiol. 283, H1873–1886, doi: 10.1152/ajpheart.00405.2000 (2002).
    1. Brennan M., Palaniswami M. & Kamen P. Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans. Biomed. Eng. 48, 1342–1347 (2001).

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