Automated Cognitive Health Assessment From Smart Home-Based Behavior Data

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behavior in the home and predicting clinical scores of the residents. To accomplish this goal, we propose a clinical assessment using activity behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding clinical scores. CAAB uses statistical features that describe characteristics of a resident's daily activity performance to train machine learning algorithms that predict the clinical scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years. We obtain a statistically significant correlation ( r=0.72) between CAAB-predicted and clinician-provided cognitive scores and a statistically significant correlation (r=0.45) between CAAB-predicted and clinician-provided mobility scores. These prediction results suggest that it is feasible to predict clinical scores using smart home sensor data and learning-based data analysis.

[1]  Jacques Demongeot,et al.  A model for the measurement of patient activity in a hospital suite , 2006, IEEE Transactions on Information Technology in Biomedicine.

[2]  D. Harvey,et al.  MCI is Associated With Deficits in Everyday Functioning , 2006, Alzheimer disease and associated disorders.

[3]  Diane J. Cook,et al.  Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data , 2015 .

[4]  Jelle Jolles,et al.  Subjective sleep problems in later life as predictors of cognitive decline. Report from the Maastricht Ageing Study (MAAS) , 2002, International journal of geriatric psychiatry.

[5]  C. Fabrigoule,et al.  Restriction in complex activities of daily living in MCI , 2006, Neurology.

[6]  M. Skubic,et al.  Management of Dementia and Depression Utilizing In- Home Passive Sensor Data. , 2013, Gerontechnology : international journal on the fundamental aspects of technology to serve the ageing society.

[7]  C. Guilleminault,et al.  Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. , 2004, Sleep.

[8]  Susan M McCurry,et al.  Current treatments for sleep disturbances in individuals with dementia , 2009, Current psychiatry reports.

[9]  L Castillo,et al.  A-30Differences Among Unspecified and Specified Neurocognitive Disorders Across Spanish Speaking Hispanics Older Adults Using the Repeatable Battery for the Assessment of Neuropsychological Status-Update , 2017 .

[10]  S. Carpenter,et al.  Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data , 2012, PloS one.

[11]  Chao Chen,et al.  CASASviz: Web-based visualization of behavior patterns in smart environments , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[12]  Maureen Schmitter-Edgecombe,et al.  Assessment of functional change and cognitive correlates in the progression from healthy cognitive aging to dementia. , 2014, Neuropsychology.

[13]  H. Hotelling.  The Generalization of Student’s Ratio , 1931 .

[14]  Jyrki Lötjönen,et al.  Circadian activity rhythm in demented and non‐demented nursing‐home residents measured by telemetric actigraphy , 2005, Journal of sleep research.

[15]  Diane J. Cook,et al.  Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..

[16]  Ilkka Korhonen,et al.  Telemetric activity monitoring as an indicator of long-term changes in health and well-being of older people , 2005 .

[17]  C. Randolph,et al.  Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) , 2009 .

[18]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[19]  Maureen Schmitter-Edgecombe,et al.  Improving the ecological validity of executive functioning assessment. , 2006, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[20]  T Hope,et al.  Predictors of institutionalization for people with dementia living at home with a carer , 1998, International journal of geriatric psychiatry.

[21]  Ian Witten,et al.  Data Mining , 2000 .

[22]  Daniel Austin,et al.  In-home walking speeds and variability trajectories associated with mild cognitive impairment , 2012, Neurology.

[23]  Shuang Wang,et al.  Activity Density Map Visualization and Dissimilarity Comparison for Eldercare Monitoring , 2012, IEEE Transactions on Information Technology in Biomedicine.

[24]  Maureen Schmitter-Edgecombe,et al.  Cognitive Correlates of Functional Performance in Older Adults: Comparison of Self-Report, Direct Observation, and Performance-Based Measures , 2011, Journal of the International Neuropsychological Society.

[25]  Jennifer L Martin,et al.  Sleep Quality in Residents of Assisted Living Facilities: Effect on Quality of Life, Functional Status, and Depression , 2010, Journal of the American Geriatrics Society.

[26]  Maureen Schmitter-Edgecombe,et al.  Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[27]  Ben J. A. Kröse,et al.  Sensor monitoring in the home: Giving voice to elderly people , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[28]  Diane Podsiadlo,et al.  The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons , 1991, Journal of the American Geriatrics Society.