- Application of Sentiment Analysis and Generative Language Algorithms to Kinto, a Support Service for Family Caregivers of Persons Living with ADRD
Joseph Chung, Kinto. This project will apply sentiment analysis and large language models to text data from a variety of sources including messaging between coaches and caregivers as well as peer-to-peer support groups.
- Validating the Apple Watch for Passive Monitoring of Agitation in Patients with Dementia
James Mastrianni, The University of Chicago. Josh Kim, Adiona Health. This project seeks to validate a novel machine learning technique that analyzes motion data from an accelerometer in an Apple Watch to identify the onset of an agitation episode in a person with ADRD.
- In-home Cognitive Improvement Training using EEG-NFB
Robert Hager, Preveal Technologies, Inc. Hassan Ghasemzadeh, ASU. The goal of this proposal is to develop an Electroencephalography (EEG) neurofeedback (NFB) prototype system consisting of a headset and machine learning algorithm coupled with music, that can be used in the home to improve working memory.
- Validating a Remote Sensor for Continuous Health Monitoring of Older Adults to Support Aging in Place and AD/ADRD Care Management
Ryan Gooch, TellUs You Care. Rebecca Spencer, UMass Amherst. This study will test Tellus’s machine learning algorithms for tracking physiological and behavioral status using data from Tellus’ radar device.
- Correlations Between Light Exposure Inputs and Sleep Quality Outputs
Erik Page, Blue Iris. This project aims to identify relationships between light exposure and sleep, arguably the most important environmental input and health outcome in circadian science.
- Decreasing Risk of Falls via Computer Vision & AI Driven Functional Assessments
Dave Keeley, Electronic Caregiver, Inc. Michael Busa, UMass Amherst. This research project will enhance Electronic Caregiver’s Addison Care system with computer vision methods for evaluating functional strength, stability, and falls risk in older adults.
- Utilizing the Druid Impairment App to Assess and Enhance Senior Adult’s Driving Performance
Micheal Milburn and William DeJong, Impairment Science, Inc. Anuj Pradhan and Shannon Roberts, UMass Amherst. The Druid app uses multiple divided attention tasks to assess cognitive-motor behaviors related to driving. This project aims to validate the Druid app for use by older adults aged 64-85 years.
- Expanding a Multimodal VR Fitness Platform to Remotely Assess, Monitor, and Report Cognitive and Physical Function for Seniors
Jennifer Stamps and Kyle Rand, Rendever, Inc. This pilot study will evaluate the utility of RendeverFit™, a VR fitness platform, in terms of its acceptance by and relevance to older adults, as well as collect data to support the construction of machine learning models.
- Intelligent Cognitive Assistant for the Individuals with AD/ADRD for Handling Word-finding Difficulty
Archna Bhatia, Institute for Human & Machine Cognition, George Sperling, UCI. This project aims to develop an Intelligent Cognitive Assistant that provides real-time word retrieval support as well as personalized training to enhance word retrieval for individuals with mild cognitive impairment and Alzheimer’s Disease and Related Dementias.
- A Digital Biometric Approach to Reducing Hospital Admissions for Underserved Older Adults with COPD
Jennifer Williams, Stanford University. This project will develop and test an approach to monitoring older adults with chronic obstructive pulmonary disease in the home using an unobtrusive metric derived from acoustic features collected during normal cell phone use.
- Preventing falls before they occur: validating a wearable sensor for Orthostatic Vital Signs
Amar Basu, Wayne State University, Michael Busa, UMass Amherst. This project will evaluate TRACE, a novel wearable sensor for monitoring orthostatic vital signs continuously at home, whenever an individual stands up.
- Detection of falls and other health events using sound, activity monitoring and machine learning
Richard Watkins, Livindi. This project aims to optimize hardware and enhance software for in-home detection of distress related events for use by older adults and caregivers.
- Portable Sleep Monitoring in Older Adults with AD/ADRD and Common Chronic Conditions
Rebecca Spencer, UMass Amherst. This project will validate sleep detection capabilities of commercially available devices with physiological sensors in a diverse representative sample of older adults with common chronic conditions and a group with AD/ADRD.
- Creation of a technology-ready cohort for patients with Alzheimer’s disease and related dementias and their caregivers
Mark Eldaief, Massachusetts General Hospital. This project will establish a “technology-ready cohort” of individuals with Alzheimer’s Disease and related dementias to support the evaluation of digital assessments relevant to ADRD patients and their caregivers.
- AI-Supported In-Home Brain Assessments for Older Adults and Persons with Alzheimer’s Disease
Quan Zhang, Massachusetts General Hospital. This project will create an adapted version of NINscan, a Near-Infrared Neuromonitoring Device, with the goal of enabling older adults and AD patients to collect high quality brain and physiological data at home.
- Passive monitoring of walking cadence as a novel tool for aging and cognitive health assessment
Honghuang Lin, UMass Chan Medical School. This study will leverage a large collection of physical activity and cognitive assessment data to derive novel digital phenotypes from commercially available wearable devices.
- An academic-industrial partnership for AI-based sleep staging in the elderly using an EEG headband and a smartwatch
Joyita Dutta, UMass Amherst. This project will develop AI techniques for at-home sleep staging in seniors using multimodal data from EEG headband and smartwatch devices.
Joseph Chung, Kinto. This project will apply sentiment analysis and large language models to text data from a variety of sources including messaging between coaches and caregivers as well as peer-to-peer support groups.
James Mastrianni, The University of Chicago. Josh Kim, Adiona Health. This project seeks to validate a novel machine learning technique that analyzes motion data from an accelerometer in an Apple Watch to identify the onset of an agitation episode in a person with ADRD.
Robert Hager, Preveal Technologies, Inc. Hassan Ghasemzadeh, ASU. The goal of this proposal is to develop an Electroencephalography (EEG) neurofeedback (NFB) prototype system consisting of a headset and machine learning algorithm coupled with music, that can be used in the home to improve working memory.
Ryan Gooch, TellUs You Care. Rebecca Spencer, UMass Amherst. This study will test Tellus’s machine learning algorithms for tracking physiological and behavioral status using data from Tellus’ radar device.
Erik Page, Blue Iris. This project aims to identify relationships between light exposure and sleep, arguably the most important environmental input and health outcome in circadian science.
Dave Keeley, Electronic Caregiver, Inc. Michael Busa, UMass Amherst. This research project will enhance Electronic Caregiver’s Addison Care system with computer vision methods for evaluating functional strength, stability, and falls risk in older adults.
Micheal Milburn and William DeJong, Impairment Science, Inc. Anuj Pradhan and Shannon Roberts, UMass Amherst. The Druid app uses multiple divided attention tasks to assess cognitive-motor behaviors related to driving. This project aims to validate the Druid app for use by older adults aged 64-85 years.
Jennifer Stamps and Kyle Rand, Rendever, Inc. This pilot study will evaluate the utility of RendeverFit™, a VR fitness platform, in terms of its acceptance by and relevance to older adults, as well as collect data to support the construction of machine learning models.
Archna Bhatia, Institute for Human & Machine Cognition, George Sperling, UCI. This project aims to develop an Intelligent Cognitive Assistant that provides real-time word retrieval support as well as personalized training to enhance word retrieval for individuals with mild cognitive impairment and Alzheimer’s Disease and Related Dementias.
Jennifer Williams, Stanford University. This project will develop and test an approach to monitoring older adults with chronic obstructive pulmonary disease in the home using an unobtrusive metric derived from acoustic features collected during normal cell phone use.
Amar Basu, Wayne State University, Michael Busa, UMass Amherst. This project will evaluate TRACE, a novel wearable sensor for monitoring orthostatic vital signs continuously at home, whenever an individual stands up.
Richard Watkins, Livindi. This project aims to optimize hardware and enhance software for in-home detection of distress related events for use by older adults and caregivers.
Rebecca Spencer, UMass Amherst. This project will validate sleep detection capabilities of commercially available devices with physiological sensors in a diverse representative sample of older adults with common chronic conditions and a group with AD/ADRD.
Mark Eldaief, Massachusetts General Hospital. This project will establish a “technology-ready cohort” of individuals with Alzheimer’s Disease and related dementias to support the evaluation of digital assessments relevant to ADRD patients and their caregivers.
Honghuang Lin, UMass Chan Medical School. This study will leverage a large collection of physical activity and cognitive assessment data to derive novel digital phenotypes from commercially available wearable devices.
Joyita Dutta, UMass Amherst. This project will develop AI techniques for at-home sleep staging in seniors using multimodal data from EEG headband and smartwatch devices.