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A mobile app to deliver music-based interventions to combat neuropsychiatric symptoms in people living with AD/ADRD
Awardee Organization(s): AutoTune Me LLC
Principal Investigator(s): Kendra Ray, PhD, MPH, MT-BC
Official Project Title: A Music-Based Mobile App to Combat Neuropsychiatric Symptoms in People Living with ADRD
AITC Partner: PennAITech
Website(s): www.autotuneme.org

The purpose of this pilot project is to develop a mobile application “TuneMind” that detects pulse and sedentary movements of homebound individuals with Alzheimer’s disease and related dementias (ADRD) and triggers auto-play of personalized songs in a wearable device. By collecting quantitative and qualitative data from the app and users, we will test the acceptability and feasibility of the app. This app will be an important tool to include in daily caregiving in a home setting by extending established benefits of music therapy for people with ADRD. The aims are as follows:
Aim 1. Develop TuneMind. TuneMind will be developed to respond to changes in the pattern of heart rate and sedentary behaviors of people with ADRD. An algorithm will be created to (1) find changes of the heart rate to trigger the application to play music; (2) adaptively improve learning when music is needed, e.g., time of day; (3) predict the music dose required to better control heart rate and movement.
Aim 2. Test the usability of the app. A total of 10 stakeholders will be asked to test TuneMind for two weeks. Based on a survey and focus groups, improvements will be made to the app.
Aim 3. Determine the performance and usability of the app for use by people with ADRD and their family caregivers at home. Twenty dyads will be recruited to test TuneMind for two weeks. We will collect physiological measures based on the app’s demonstration to auto-play music according to pulse and/or sedentary states.

AI-driven computer vision for fall prediction and detection for older adults with AD/ADRD
Awardee Organization(s): Iris Technology Inc.
Principal Investigator(s): David Stout
Official Project Title: AI-Assisted Fall Detection and Remote Monitoring for Seniors with ADRD
AITC Partner: PennAITech
Website(s): www.poweredbyiris.io

Falls are a significant contributor to health decline in older adults. Iris Technology Inc is using a proprietary AI Vision architecture to build a better fall detection and prevention solution for use in health care facilities and eventually home care environments. Our proprietary AI architecture, Deep Detection™, will revolutionize how we detect and even prevent falls by allowing us to create highly accurate models with minimal training data, and by generalizing and incorporating context to provide deeper insights. Our solutions operate entirely on the edge, ensuring patient privacy in the health care setting and giving users total control over their data. In partnership with UPenn and the National Institute of Aging, our project is focused on harnessing the unique capabilities of our AI to create a model that can more accurately detect fall events and learn when the risk of a fall is high to support preventative intervention. Our ultimate vision is to build a full suite of tools and a library of models that can facilitate better, more personalized care for seniors that both enables greater independence and fully protects their privacy without the need for expensive or unscalable monitoring. There is an ever-growing need to develop good technology that can ease the increasing burden on caregivers and help ensure that seniors are receiving the highest levels of care. Iris Technology’s mission is to empower people to solve humanity’s unsolvable problems, and we are confident that we can help to develop cutting-edge AI that will help solve real-world problems facing seniors and caregivers today.

AI-enabled conversation platform to measure mental status and manage psychotropic medication use for older adults
Awardee Organization(s): George Washington University | Crosswater Digital Media
Principal Investigator(s): Lorens Helmchen, PhD
Official Project Title: AI-Enabled Conversations to Measure Mental Status and Manage Psychotropic Medication Use
AITC Partner: PennAITech
Website(s): www.gwu.edu

Continuous monitoring of cognitive function among the elderly is vital for early detection and proper management of Alzheimer’s Disease and related dementias. Similarly, continuous monitoring of changes in mood is indispensable for the appropriate dosing of psychotropic medication. Yet, current means of monitoring cognitive function and mood among the elderly are often infrequent, inconsistent, and imprecise because they rely on the completion of standardized questionnaires that may fail to flag clinically relevant leading indicators. This project aims to deploy and validate the use of digital “conversation companions”, a remote patient-monitoring technology that can be installed on tablet computers and smartphones by untrained caregivers or the elderly themselves. The recordings and transcripts of the conversations between elderly residents and these digital companions will be used to train machine-learning algorithms that can measure the presence and severity of dementia and depression and predict fall risk. Expert clinicians, family members, and community stakeholders will ensure that the predictions are clinically informative, actionable, transparent, and culturally appropriate. As the technology can be used by patients on their own and as the voice and the visuals of the digital conversation companions can be adapted to a patient’s linguistic and cultural background, this technology can reach traditionally under-served patient populations such as racial minorities and those living in remote areas. This technology will allow caregivers to detect small and subtle changes in an individual’s cognitive function and mood in a way that is less intrusive, more frequent, more consistent, and more precise than current practice.