The world's population is aging and the increasing number of elderly who cannot maintain functional independence in their own homes is a challenge our society must address. While the idea of smart environments is now a reality, gaps in our knowledge base concerning how to scale and validate activity recognition and health assessment technologies currently limit clinical translation of smart environments for real-time health monitoring and intervention. The long-term objective of this project 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 renewal application is to design, evaluate and validate software algorithms that recognize daily activities, provide automated health assessment and support real-time interventions. To most people home is a sanctuary, yet today those who need special care, predominantly older adults, must leave home to meet clinical needs. We hypothesize that many older adults who require support completing everyday activities 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. Building on our pioneering prior work and a partnership between computer science and clinical neuropsychology researchers, our central hypothesis will be tested by pursuing the following specific aims: (1) expand and validate software algorithms that recognize daily activities and provide automated functional assessment to encompass a greater number of behaviors and more diverse older adult population; (2) develop and validate software algorithms that provide automated health assessment by partnering actigraphy and ecological momentary assessment with in-home smart home data; (3) develop technologies to provide data-driven context-aware automated prompts; and (4) investigate methods for visualizing and integrating clinically-relevant smart home health data into personal and electronic health records. The proposed work is innovative because it partners real-time methodologies and defines methods of detecting and coping with aging and disabilities in our most personal environments: our homes. This work is significant because it provides the basis for technologies that will keep older adults with functional impairment in their homes and monitor frail older individuals from afar. The outcome of this work will result in automated health assessments that make use of smart technology, recommendations for improving the ecological validity of office-based clinical assessments, automated real-time intervention methods that can help support preventative health care measures, and clinically-relevant, user-friendly interfaces for integration of smart home data into health records.
The potential health care and social benefits of the proposed work are dramatic as this work will improve the clinical utility, usability and translation of smar home technologies for health assessment and intervention. This work will demonstrate that intelligent technologies can be used in real-world settings to gather validated activity and health related information that can be used by the medical community to improve the efficacy of traditional assessment and support ecological momentary intervention techniques. This research is relevant to public health because these technologies can extend the functional independence of our aging society through proactive health care and real-time intervention, reduce caregiver burden and improve quality of life.
| 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 |
| Fellows, Robert P; Dahmen, Jessamyn; Cook, Diane et al. (2017) Multicomponent analysis of a digital Trail Making Test. Clin Neuropsychol 31:154-167 |
| Thomas, Brian L; Crandall, Aaron S; Cook, Diane J (2016) A Genetic Algorithm Approach to Motion Sensor Placement in Smart Environments. J Reliab Intell Environ 2:3-16 |
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