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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.

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.

Awardee Organization(s): Weill Cornell Medicine
Principal Investigator(s): Veerawat Phongtankuel, MD, MS
Official Project Title: Detecting Respiratory Distress in Patients with Advanced ADRD Using Radio Sensors
AITC Partner: PennAITech
Website(s): www.weill.cornell.edu

This project focuses on designing and developing conversational care technologies for older adults and their caregivers. In prior work, we surveyed informal caregivers and older adult care receivers to understand their care routines. Survey findings showed how nearly 20% of care partners used voice assistants in their homes, signaling an opportunity to extend research on older adults’ conversational technology use to include care partners. Next, we conducted a diary study and interviews with caregivers and care receivers to investigate gaps in care interactions and conversations. We found that care receivers experienced more communication frustrations than caregivers and that older adult caregivers wanted more opportunities to influence their care routines.
In this project, we will use these findings to develop in-home conversational technologies that use prompts to structure care conversations between older adults and their caregivers. We contribute a nuanced dyadic perspective to care relationships as most care research focuses solely on caregiver perspectives. We also extend conversational technology research beyond information seeking to include more social uses by developing conversational technology applications with mainstream voice technologies (e.g., Amazon Alexa) to support improved care relationships, social and emotional well-being, and quality of life.

Awardee Organization(s): Bestie Bot
Principal Investigator(s): Richard Everts
Official Project Title: RGBd+ Thermal Computer Vision Platform for Home Monitoring and Telehealth
AITC Partner: PennAITech
Website(s): www.bestiebot.com

We’re developing a new class of a home-based, AI-enabled monitoring system that uses standard computer vision and thermal sensors to detect falls, notify caretakers in case of falland perform basic health diagnostics, all with industry-exceeding privacy and accuracy.
Specifically, we will:
1. Detect falls more reliably in day and night conditions
2. Increase the capabilities of remote health checks through mobility and thermal testing
3. Reduce AI biases for the BIPOC population through our data generation tools and methods
Using unique sensor fusion along with a patented new AI system, our goal is to vastly increase the reliability, adoption, and privacy of in-home monitoring systems for use by those aging in place and their families.