The number of Americans unable to live independently in their homes due to cognitive or physical impairments is rising significantly due to the aging of the population. There is a currently a fundamental gap in the knowledge base concerning how to apply machine learning technologies to improve health monitoring and how to harness these technologies to implement interventions designed to sustain independent living. The long-term objective of this work is to improve human health and impact health care delivery by developing smart environments that aid with health monitoring and intervention. The objective of this particular application is to design, implement, and evaluate technologies for assessing everyday functional limitations and for providing automated intervention strategies for persons with early-stage dementia. To most people home is a sanctuary, yet today those who need special care, predominantly older adults, must leave home to meet clinical needs. The central hypothesis is that many older adults with cognitive impairment can lead independent lives in their own homes with the aid of automated assistance and health monitoring. The rational for the proposed work is that smart environment technologies can improve quality of life and health care for older adults who require assistance with everyday functional activities and reduce the emotional and financial burden for caregivers and society. Guided by strong preliminary data and a partnership between computer science and clinical neuropsychology researchers, our central hypothesis will be tested by pursuing the following specific aims: (1) Design software algorithms that use smart environment data to recognize complex everyday activities in real-world settings, (2) Use smart environments to automate functional health assessment and to examine the ecological validity of laboratory-based measures, (3) Design automated reminder and prompting-based interventions to aid with everyday activity completion, and (4) Analyze correlations between everyday behavioral patterns and physiological data. The proposed work is innovative because it defines methods of detecting and coping with aging, early dementia and disabilities in our most personal environments: our homes. The proposed work is significant because it provides the basis for automated, robust functional assessment of individuals with cognitive limitations and of intervention strategies designed to improve functional independence for these individuals. Rather than relying on self-reporting by the patient or by an informant who may or may not spend extended time with the patient, smart home technologies will allow us to identify functional deficits that impede a patient's ability to maintain independence in their home as they begin to occur, and to extend independent living at home by intervening in a real world setting.
The proposed studies represent the first application of smart environment and machine learning technologies to the problem of in-home functional performance assessment. The result of this research will be software algorithms embedded in everyday environments that provide more reliable and accessible tools for measuring functional limitations, and that lay the foundation for creating and assessing tools that sustain or improve the ability to accomplish everyday tasks of living. Once such technologies become available, there is the likelihood that they can provide an ecologically valid method for monitoring an individual's everyday functional status and for extending the amount of time individuals can live independently in their own homes through the use of automated and reminder-based intervention.
| Fellows, Robert P; Dahmen, Jessamyn; Cook, Diane et al. (2017) Multicomponent analysis of a digital Trail Making Test. Clin Neuropsychol 31:154-167 |
| Feuz, Kyle D; Cook, Diane J (2017) Collegial Activity Learning between Heterogeneous Sensors. Knowl Inf Syst 53:337-364 |
| Dahmen, Jessamyn; Cook, Diane J; Wang, Xiaobo et al. (2017) Smart Secure Homes: A Survey of Smart Home Technologies that Sense, Assess, and Respond to Security Threats. J Reliab Intell Environ 3:83-98 |
| Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J (2017) Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications. IEEE Trans Knowl Data Eng 29:2744-2757 |
| Williams, Jennifer A; Cook, Diane J (2017) Forecasting behavior in smart homes based on sleep and wake patterns. Technol Health Care 25:89-110 |
| Beaver, Jenna; Wilson, Kaci B; Schmitter-Edgecombe, Maureen (2017) Characterising omission errors in everyday task completion and cognitive correlates in individuals with mild cognitive impairment and dementia. Neuropsychol Rehabil :1-17 |
| Sanders, Chad; Schmitter-Edgecombe, Maureen (2017) Examining the impact of formal planning on performance in older adults using a naturalistic task paradigm. Neuropsychol Rehabil 27:759-776 |
| Minor, Bryan; Cook, Diane J (2017) Forecasting Occurrences of Activities. Pervasive Mob Comput 38:77-91 |
| Aminikhanghahi, Samaneh; Cook, Diane J (2017) A Survey of Methods for Time Series Change Point Detection. Knowl Inf Syst 51:339-367 |
| Cohen, Rajal G; Vasavada, Anita N; Wiest, Michelle M et al. (2016) Mobility and Upright Posture Are Associated with Different Aspects of Cognition in Older Adults. Front Aging Neurosci 8:257 |
Showing the most recent 10 out of 64 publications
