Maureen Schmitter-Edgecombe - US grants

DESCRIPTION (Adapted from the Applicant's Description): Human beings are able to become skilled at most complex tasks (e.g., driving) because they learn to automatize portions of the task. Despite the importance of automaticity in skill development. few studies have systematically investigated automatic process development in brain injured populations. Search tasks have been used in the laboratory to study the development of automatic processes. The proposed study will use a visually-based search task to examine skill acquisition and automatic process development in a severe closed-head injured (CHI) population. Because the automatization of task components is integral to developing skilled performances, a theoretical understanding of automatic process development after severe CHI should have important implications for remediation procedures. Across several sessions of training, CHI participants and matched controls will complete a semantic category visual search task in consistent mapping (CM) and varied mapping (VM) training situations. The investigators have shown that the CM training condition results in dramatic performance improvements and the development of an automatic attention response (AAR). VM training, on the other hand, results in little performance improvement and the continued reliance on attention-demanding or controlled processes. By comparing performances in the CM and VM conditions, the investigators propose to be able to evaluate the rate and acquisition of skilled visual search performance following a severe CHI. Transfer conditions will be used to test for general, task-related learning and for the development of an AAR, independent of general performance improvement. These data are expected to provide initial evidence for the ability of severe CHI participants to acquire general, task-related skills and develop an AAR in a visual search situation. Further studies can then examine, for example, transfer of automatized task components to more complex tasks, and ability to modify previously learned and automatized processes (i.e., "old habits").

DESCRIPTION (provided by applicant): Traumatic brain injury (TBI) is a disorder of public health concern that has ramifications not only for the individual, but also for their family, the public health system, and the economy. The majority of TBIs affect people in the prime of their vocational productivity. Despite decades of work in the area of cognitive rehabilitation, a recent evidence report summarizing research on the efficacy of cognitive remediation after TBI revealed predominately negative results. This is partly because there is currently no empirically supported theory about cognitive recovery from TBI to guide intervention strategies. This study will provide better scientific evidence to guide cognitive remediation by more fully characterizing the potential early learning mechanisms of TBI patients, and by prospectively evaluating the recovery trajectories of both automatic and controlled cognitive processes. TBI patients will complete experimental tasks designed to assess automatic and more controlled components of visual search, semantic priming, and memory. These task will be administered following the TBI patients emergence from post-traumatic amnesia, and then again at 2-, 6-, and 12-month intervals. Control participants will complete the tasks at similar intervals following baseline testing. Perceptually-based implicit learning abilities and memory-based skill learning abilities will also be evaluated acutely following injury. If restitution of function of automatic processes occurs early in recovery and before controlled processes, then training techniques that tap into residual automatic skills or capitalize on processes that can be made automatic through practice could prove vital for early interventions, facilitating recovery and improving ultimate cognitive outcome. Furthermore, an understanding of the recovery trajectories of automatic processes could help refine models for predicting rehabilitative gains and aid in rehabilitative planning and resource allocation as automatic processes often serve as a "data base" for more controlled processes.

This Integrative Graduate Education and Research Traineeship (IGERT) project at Washington State University supports the development of a multi-disciplinary doctoral training program focused on designing and studying health-assistive smart environments. This IGERT will provide integrated training in the complementary disciplines of computer science, electrical engineering, mechanical engineering, psychology, sociology, and health care to prepare scientists who can design smart environment techniques and use them to study aging and the development of health interventions. The faculty of the program will develop collaborative and sustaining interdisciplinary research opportunities in this area, provide new interdisciplinary classes that combine theoretical and practical learning experiences, and build leadership and cooperation skills through team problem-solving. Students will obtain real-world experience through internships and will understand user needs by living in on-campus smart environments. This IGERT program will partner with on-campus Research Experiences for Undergraduates programs, enhancing participation by students from underrepresented groups. IGERT participants will conduct research to determine whether technology can automatically monitor and analyze human health and behavior, whether it can simulate human behavior and activities, whether it can enhance human physical and cognitive abilities, and whether these technologies can be accepted by society. This program is intended to make a contribution to a generation of a workforce that is trained in multiple, complementary disciplines and that will open the door to new avenues of health and science research. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

DESCRIPTION (provided by applicant): 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.

DESCRIPTION (provided by applicant): The world's population is aging and the resulting prevalence of chronic illnesses is a challenge that our society must address. Our vision is to address this challenge by designing smart environment technologies that keep older adults functioning independently in their own homes as long as possible. Smart environments have been used as the basis of monitoring activities for residents with health conditions. However, there is currently a lack of large scale, longitudinal research to identify early markers of dementia and other health status changes and to predict functional decline. The objective of this project is to perform a 5-year longitudinal study of older adults performing daily activities in thir own smart homes. By tracking residents' daily behavior over a long period of time our intelligent software can perform automated functional assessment and identify trends that are indicators of acute health changes (e.g., infection, injury) and slower progressive decline (e.g., dementia). By implementing prompt-based interventions that support functional independence and promote healthy lifestyle behaviors (e.g., social contact, exercise, regular sleep), we can improve overall health and well- being. We hypothesize that smart home technologies can be used to detect and predict functional change, to slow functional change and extend functional independence, and to improve quality of life in elderly individuals who are at risk of transitioning to MCI and t dementia. This hypothesis has been formulated on the basis of preliminary data produced by the applicants which supports the efficacy of using smart home technologies for both functional status assessment and for prompting the initiation and completion of activities in individuals with MCI and dementia. The rationale of the proposed work is that understanding the natural history of functional change between aging and dementia will lead to early prevention and proactive interventions that will slow functional change, thereby delaying nursing home placement and cost of care to society. We plan to pursue the following specific aims: (1) Characterize the daily lifestyle of smart environment residents through minimal-supervision activity recognition and activity discovery, (2) Design software algorithms that detect trends in behavioral data, and (3) Evaluate the efficacy of activity-aware automated prompting technology for extending functional independence and improving quality of life. The proposed work is innovative because it will track a large number of individuals longitudinal in their own homes and determine whether this technology can be used to promote healthy lifestyle behaviors and detect health care changes that may lead to early interventions, improved quality of life, and decreased health care utilization. The project is significant because it will introduce new technologies for activity discovery and tracking that require minimal- supervision, contribute algorithms that predict cognitive decline and signal more acute health status change, and demonstrate for the first time that activity-aware automated prompting technologies can be used to support and/or slow functional change and to increase quality of life in elderly individuals.

DESCRIPTION (provided by applicant): The world's population is aging and the resulting prevalence of chronic illnesses is a challenge that our society must address. Our vision is to address this challenge by designing a curriculum for undergraduates in MSTEM fields that provides them the needed research skills to address this looming problem. Undergraduates in nursing, psychology, sociology, computer science, engineering (MSTEM) programs as well as those in healthcare-related disciplines need a strong multi-disciplinary background to be truly prepared for research in applying technology to gerontology issues. The objective of this project is to develop training programs for undergraduate participants in the fields of gerontology, and technology-based assistive environments. This will be done through course work, summer research programs, internships in the field, and professional workshops to help other institutions develop similar programs. The ultimate goal is to bring up a generation of new graduate student researchers and innovators who understand the need of continued work in the field for addressing the aging population issues and begin their research careers prepared for gerontechnology oriented research. We hypothesize that by developing a these new programs for training both undergraduates and fellow educators in the issues surrounding the aging problem, multi-disciplinary MSTEM approaches to tackling these tough issues, and supporting undergraduates in becoming prepared for graduate programs we can provide gerontechnology-related research groups across the nation with highly qualified applicants. The rationale of the proposed work is that there are few well integrated MSTEM programs for undergraduates and a lack of training in related research programs for new undergraduates available. Our group's research centers on this field, and our research team is interested in developing generations of qualified graduate students who come prepared for this highly complex field of research. To accomplish this, we plan to pursue the following specific aims: (1) Develop an undergraduate two semester (one academic year) multi-disciplinary MSTEM Gerontechnology course series, (2) Establish a summer research and internship experience program for highly qualified undergraduates, (3) Support senior capstone projects in MSTEM fields, especially for those students who wish to continue their work from the Gerontechnology course, (4) Integrate the Gerontechnology course into the existing Minor in Aging offered at the institution, giving it its first technology related course. We will also create a certificate of accomplishment for all students who have completed the Gerontechnology course and who have completed additional field experience related to gerontology, and (5) Lead and run two workshops for others interested in learning about the latest research in the field, plus training for other educators in the field of Gerontechnology so they can bring similar programs online at their institutions. The proposed work is innovative because Gerontechnology related undergraduate programs with a true multi-disciplinary core are rare. The combination of serving both the local student body, summer students from other programs, and to also bring our faculty's experience to other institutions through workshops is a compelling and energetic approach to bolstering the quantity of highly prepared upcoming graduate researchers. The project is significant because it will introduce many undergraduates to the issues faced by our society in the coming decades, as well as prepare many of them to help develop new approaches to health care through melding technology with traditional medical approaches.

DESCRIPTION (provided by applicant): 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.

This project aims to design algorithmic techniques to perform activity discovery, recognition, and prediction from sensor data. These techniques will form the foundation for the science of Activity- Prediction Cyber-Physical Systems, including potential improvement in the responsiveness and adaptiveness of the systems. The outcome of this work is also anticipated to have important implications in the specific application areas of health care and sustainability, two priority areas of societal importance. The first application will allow for health interventions to be provided that adapt to an individual's daily routine and operate in that person's everyday environment. The second application will offer concrete tools for building automation that improve sustainability without disrupting an individual's current or upcoming activities. The project investigators will leverage existing training programs to involve students from underrepresented groups in this research. Bi-annual tours and a museum exhibit will reach K-12 teachers, students and visitors, and ongoing commercialization efforts will ensure that the designed technologies are made available for the public to use.

Deploying activity-predictive cyber-physical systems "in the wild" requires a number of robust computational components for activity learning, knowledge transfer, and human-in- the-loop computing that are introduced as part of this project. These components then create cyber physical systems that funnel information from a sensed environment (the physical setting as well as humans in the environment), to activity models in the cloud, to mobile device interfaces, to the smart grid, and then back to the environment. The proposed research centers on defining the science of activity-predictive cyber-physical systems, organized around the following thrusts: (1) the design of scalable and generalizable algorithms for activity discovery, recognition, and prediction; (2) the design of transfer learning methods to increase the the ability to generalize activity-predictive cyber-physical systems; (3) the design of human-in-the-loop computing methods to increase the sensitivity of activity-predictive cyber-physical systems; (4) the introduction of evaluation metrics for activity-predictive cyber-physical systems; and (5) transition of activity-predictive cyber-physical systems to practical applications including health monitoring/intervention and smart/sustainable cities.

PROJECT SUMMARY/ABSTRACT The world's population is aging and the increasing number of older adults with chronic health conditions is a challenge our society must address. While the idea of smart environments is now a reality, there remain gaps in our knowledge about how to scale smart homes technologies for use in complex settings and to use machine learning and activity learning technologies to design automated health assessment and intervention strategies. 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 primary objective of this application is to design a ?clinician in the loop? smart home to empower individuals in managing their chronic health conditions by automating health monitoring, assessment, and evaluation of intervention impact. Building on our prior work, the approach will be to generate analytics describing an individual's behavior routine using smart homes, smart phones, and activity learning (Aim 1). Our trained clinicians will use the analytics to perform health assessment and detection of health events (Aim 2). In addition, we will introduce brain health interventions to support sustainable improvement of brain health (Aim 3). Finally, we train machine learning algorithms from the clinical observations to automate assessment of health and intervention impact (Aim 4). The use of these technologies is expected to improve and extend the functional health and wellbeing of older adults, lead to more proactive and preventative health care, and reduce the caregiver burden of health monitoring and assistance. By understanding situational factors that impact prompt adherence, adherence situations can be increased. The approach is innovative because it will explore and validate new machine learning techniques for activity learning and health assessment based on clinical ground truth. These contributions are significant because they can extend the health self-management of our aging society through proactive health care and real-time intervention, and reduce the emotional and financial burden for caregivers and society. Given nursing home care costs, the impact of family-based care, and the importance that people place on staying at home, technologies that increase functional independence and thus support aging in place while improving quality of life for both individuals and their caregivers are of significant value to both individuals and society.

The population is aging - the estimated number of individuals over the age of 85 will triple by 2050. Even in our current population an estimated 9% of adults age 65+ and 50% of adults age 85+ need assistance with everyday activities, and the annual cost for the United States is roughly $2 trillion. Given the economic and quality of life costs, there is a critical need to better use smart technologies so that individuals can live independently in their own homes, helping both individuals and society as a whole. This proposed work will create a novel multi-agent robot system, called Robotic Activity Support (RAS), that provides in-home activity support for older adults and others that need assistance to independently perform common activities of daily living. The system will rely on cooperation between a smart home and a mobile robot to learn activity routines for an individual. RAS will use this information to provide activity interventions that help smart home residents initiate and successfully complete important daily activities and improve functional independence.

Rather than explore co-robot systems with multiple identical platforms, the system will represent a collaboration between a mobile robot, a smart home agent with multiple heterogeneous sensors and multiple humans with distinct roles. For this collaboration, the project team will employ a custom robot that will partner with the team's CASAS smart home architecture. RAS will incorporate caregiver-in-the-loop active learning to improve its models. the team will use an iterative user-centered development process to enhance the mobile robotic platform design. The RAS system will use active learning from both the resident and caregiver to learn common activities and how it can support such activities. For example, the robot may prompt a resident to eat breakfast if she does not initiate the task at the normal time, remind the resident where the cereal is, and notify a nearby caregiver if help is needed that is beyond the robot's capabilities. The team will evaluate RAS on historic smart home data (from their more than 100 deployments), in their on-campus smart home, and in a home with a healthy older adult caregiver, as well as an older adult exhibiting cognitive limitations.

PROJECT SUMMARY/ABSTRACT The world's population is aging and the increasing number of older adults with Alzheimer's disease and related dementias (ADRDs) is a challenge our society must address. While the idea of smart environments is now a reality, there remain gaps in our knowledge about how to scale smart homes technologies for use in complex settings and to use machine learning and activity learning technologies to design automated health assessment and intervention strategies. The primary objective of the parent study is to design a ?clinician in the loop? smart home to empower individuals in managing their chronic health conditions by automating health monitoring, assessment, and evaluation of intervention impact. This supplement extends our design to monitor, assess, and intervene for individuals with ADRDs and their caregivers. Building on our prior work, the approach will be to generate analytics describing an individual's behavior routine using smart homes, smartwatches, and activity learning (Aim 1). Our trained clinicians will use the analytics to train algorithms for health assessment (Aim 2). In addition, we will introduce brain health interventions to extend brain health and objectively capture intervention both adherence and caregiver support (Aim 3). Clinician guidance is used to train machine learning algorithms to automatically recognize health events (Aim 4). Given the unique challenges that will arise when we include individuals with ADRDs, we also introduce novel methods to track multiple residents in smart environments (Aim 5) and compare behaviors between individuals with ADRDs and the original sample of older adults with chronic health conditions (Aim 6). These contributions are significant because they can extend the health self-management of our aging society through proactive health care and real-time intervention, and reduce the emotional and financial burden for caregivers and society. Given nursing home care costs, the impact of family-based care, and the importance that people place on staying at home, technologies that increase functional independence and thus support aging in place while improving quality of life for both individuals and their caregivers are of significant value to both individuals and society.

The prevalence of Alzheimer's Disease and other disorders (ADRDs) is now a public health crisis. In the absence of effective medical treatment, there is a critical need for behavioral interventions to prevent or delay symptom onset. Multidomain interventions simultaneously targeting multiple modifiable risks for ADRD have shown promise, but additional innovative approaches that could be highly accessible by capitalizing on user- friendly digital applications to support and strengthen behavior modification are needed. Training in the use of compensatory aids (e.g., calendars and note taking systems) can improve daily independence. These same compensatory tools can be employed to facilitate the adoption of lifestyle changes that support brain health (e.g., exercise, cognitive engagement, stress management) through management of goal-setting, behavioral monitoring, tracking and feedback. The current project will test a 6-month intervention that provides training in both compensatory aids and lifestyle modification. A comprehensive suite of digital tools encapsulated in the Digital Memory Notebook (DMN), an easy to use, interactive application, will be used to facilitate behavioral change and enhance participant motivation. Further, the DMN allows collection of real-time data to track intervention adherence. The DMN has been successfully applied to improving compensation among individuals with mild cognitive impairment. The proposed work capitalizes on a critical window for building resilience by targeting individuals at risk for ADRD due to a subjective cognitive concern (SCC) but who remain cognitively normal. We will conduct a randomized controlled trial (RCT) among ethnoracially diverse older adults with SCC to compare our digital app supported compensation training and lifestyle modification intervention to an education only control group that will not use the DMN or be provided with guidance on how to implement the educational material into their daily lives. Specific aims of the project include: 1) evaluate intervention efficacy on primary outcomes (global cognition and everyday function); secondary outcomes focus on well-being, cognitive domains (memory and executive function), activities of daily living (IADLs), physical function, compensation, and health behaviors; 2) evaluate characteristics of treatment responders; 3) evaluate adherence and identify the effective components of the target intervention using a mixed-method approach; and 4) design machine learning algorithms that use patterns of change in real-time DMN data metrics to identify incipient declines in treatment adherence and changes in health status. The intervention under study is novel because it applies training in compensation to support lifestyle modifications and everyday functioning using a digital app that also monitors adherence to each component of the intervention in real-time. The project is expected to expand understanding of factors that may impact adherence to and outcomes of a preventative intervention leading to optimization of a scalable intervention to reduce dementia risk applicable to diverse populations.

PROJECT SUMMARY / ABSTRACT Advances in machine learning and low-cost, wearable sensors offer a practical method for understanding, assessing, and intervening for Alzheimer's Disease and Related Dementias (ADRDs) in everyday spaces. We propose to create a Behaviorome research program that will create ground-breaking methods for building health-predictive models from wearable sensor data by mapping patterns of behavior using machine learning and pervasive computing technologies. This program will create innovative multidisciplinary ideas to address NIH ADRD Milestone 11.c, Embed wearable technologies/pervasive computing in existing and new clinical research. Our research program builds on a history of interdisciplinary research contributions in areas including human behavior modeling from longitudinal sensor data and design of novel assessment and intervention mechanisms. We propose to design and validate methods for mapping a human behaviorome ?in the wild?, automatically assessing cognitive and functional health from behavior markers, scaling technologies through machine learning, linking health and behavior with their influences, and closing the loop with automated interventions. Similarly, our mentoring program builds on experience training students and early- career investigators to become leaders in the field of gerontechnology. We will recruit and train graduate students and early-stage researchers, including those from underrepresented groups, to grow an institutional multidisciplinary Behaviorome research program and to establish new research programs that contribute to the targeted Milestone. We will scale the impact of mentoring by establishing a webinar series and creating youtube videos that highlight and explain breakthroughs in the design and application of Behaviorome research. Results of this program will include scripts and templates to construct a behaviorome with resource- limited wearable devices, scale data and models to large diverse populations, integrate data with multiple information sources (e.g., genetics), automate health assessment and intervention, and create understandable explanations of data and models. These will contribute to existing clinical studies such as the clinician-in-the- loop smart home, digital memory notebook, and pervasive computing measures of functional performance. Furthermore, they will lead to new clinical studies that formalize connections between health and its influences, exploration of the impact of ethnicity and the built environment on health, and the design of ADRD interventions for medication adherence, task prompting, and negative interaction de-escalation. The proposed contributions are significant because they will provide insights on detecting and assessing ADRDs within a person's everyday environment using wearable sensing and pervasive computing methods that have not been investigated in prior work. Additionally, the mentoring steps will pave the way for a new generation of researchers to offer improved methods of addressing the need to understand, assess, and intervene for ADRDs in everyday settings, thereby improving quality of life and reducing health care costs.

PROJECT SUMMARY / ABSTRACT The world's population is aging and the increasing number of older adults with Alzheimer's disease and related dementias (ADRDs) is a challenge our society must address. While the future of healthcare availability and quality of services seems uncertain, at the same time advances in pervasive computing and intelligent embedded systems provides innovative strategies to meet these needs. One particular need which technology can help address is assessment and assistance with a person's functional performance. The long-term goal of this work is to develop technologies that will improve the independent functioning and quality of life of individuals with functional limitations (particularly individuals with ADRDs) and reduce their reliance on caregivers. The primary objective of this application is to develop a multi-modal sensor-based approach to automate functional health assessment and assistance with everyday activities. Building on our prior collaborative work, our approach will be to collect and fuse multi-modal functional performance data from ambient sensors, mobile sensors, free text, and assessment apps (Aim 1). This fused ?human behaviorome? will provide a basis, together with observation-based ground truth, for automated functional assessment and validation of each component technology, including the use of compensatory strategies, through in-person observation and through video recording of typical daily activities and strategies (Aim 2). Finally, using iterative, user-centered assessment of prompt-based assistance, we will evaluate the ability of activity segmentation and forecasting techniques to provide automated support for activity initiation and accurate completion of everyday activities (Aim 3). The proposed contributions are significant because they will provide insights on functional health revealed within a person's everyday environment that have not been investigated in prior work. The results can also help to extend functional independence through real-time assistance, while the outcomes can assist family planning, provision of care, and design of real-world and lab-based measures of functional performance. This work is important because of the increasing number of older individuals experiencing cognitive and functional limitations due to chronic health conditions. Furthermore, they address the need for individuals to remain functionally independent as long as possible in their own homes, thereby improving quality of life and reducing health care costs.

PROJECT SUMMARY/ABSTRACT The world's population is aging. The resulting prevalence and ability to provide quality care for older individuals with Alzheimer's disease and related dementias (ADRDs) and other chronic illnesses is a challenge our society must address. Our vision is to address this challenge by providing a diverse body of undergraduate students with the scientific, clinical, and research experience needed to understand health-assistive technology and design technological solutions that aid with the challenges of aging and improve human health. Undergraduates in neuroscience, psychology, sociology, computer science, and engineering (MSTEM) programs as well as those in healthcare-related disciplines need a strong multi-disciplinary background to be truly prepared for research in applying technology to gerontology issues. The objective of this renewal application is to continue to enhance and lead a research training program for undergraduate students in the fields of gerontology and technology-based assistive environments. This will be done through course work, summer research programs, online materials and professional symposia to help other institutions develop similar programs. The ultimate goal is to bring up a diverse generation of new graduate student researchers and innovators who understand the need of continued work in the field for addressing the aging population issues and begin their research careers prepared for gerontechnology oriented research. To accomplish our goal, we will refine and offer a gerontechnology class that is geared toward multidisciplinary undergraduate students (Aim 1). We will also refine and offer a gerontechnology-focused summer undergraduate research experience (GSUR) program that will provide a team-based research opportunity for highly-qualified students (Aim 2). To broaden the impact of the training program, we will offer mentoring support for senior capstone projects and independent and clinical training projects related to gerontechnology (Aim 3). Finally, we will broaden the impact of our program by disseminating training materials through online classes, Youtube videos, and podcasts, and presenting methods and results of the training program at high-visibility gerontology and technology meetings (Aim 4). In all of these efforts we will recruit and involve a diverse student body, including women in STEM, minorities, persons with disabilities and individuals from disadvantaged backgrounds. The proposed program is innovative because Gerontechnology-related undergraduate programs with a true multi- disciplinary core are rare. The combination of serving both the local student body, summer students from other programs, and individuals from outside the university through online materials, open seminars, and workshops will bolster the quality, quantity, and diversity of highly prepared upcoming graduate researchers. The project is significant because it will introduce many undergraduates to the issues faced by our society in the coming decades, as well as prepare many of them to help develop new approaches to health care through melding technology with traditional medical approaches.