Year 4 Pilots

Publication: Kinematic correlates of early speech motor changes in cognitively intact APOE-ε4 carriers: a preliminary study using a color-word interference task

Authors: Mehrdad Dadgostar, Lindsay C Hanford, Jordan R Green, Brian D Richburg, Averi Taylor Cannon, Nelson V Barnett, David H Salat, Steven E Arnold, Marziye Eshghi Abstract Introduction: Alzheimer's disease (AD) is the most prevalent form of dementia and a major public health challenge. In the absence of a cure, accurate and innovative early diagnostic methods are essential for proactive life and healthcare planning. Speech metrics have shown promising potential for identifying individuals with mild cognitive impairment (MCI) and AD, prompting investigation into whether speech motor features can detect elevated risk even prior to cognitive decline. This preliminary study examined whether speech kinematic features measured during a color-word interference task could distinguish cognitively normal APOE-ε4 carriers (ε4+) from non-carriers (ε4-). Methods: Sixteen cognitively normal older adults (n = 9 ε4+, n = 7 ε4-) completed…

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Grant Funding: 1R41AG092186 – Restoring Self-Feeding with a Sensor Driven Robotic System

  • Post category:B14 - DESiN

Project Narrative: The NIH STTR Phase I project aims to improve the Obi robotic feeding system with advanced technology, allowing it to autonomously deliver food to individuals with severe upper limb disabilities. This enhancement will provide a more independent eating experience, reduce caregiver burden, and has the potential to improve the quality of life for millions of people with mobility impairments. Source: 1R41AG092186-01A1 (NIH RePORTER)

Continue ReadingGrant Funding: 1R41AG092186 – Restoring Self-Feeding with a Sensor Driven Robotic System

Grant Funding: NIA Phase I SBIR Award

Abstract: This proposal responds to an acute challenge currently underserved by technology; the need to leverage novel approaches to develop cost-effective and responsive digital, mobile, website, and artificial intelligence (AI) tools to encourage participation in Alzheimer disease and related dementias (ADRD) clinical trials. With ADRD cases expected to double by 2060, efforts to ensure adequate participation in ADRD clinical trials is paramount to ensuring successful biomedical advances and drug development. Despite ongoing efforts to improve ADRD outcomes and clinical trial participation, no digital solution exists which has been purposefully designed and co-developed in partnership with patients at higher-risk of ADRD or with informal caregivers of ADRD patients critical in addressing challenges associated with trial participation. In response, this project utilizes approaches in digital tool development, AI, qualitative interviews and small group discussions, and use of…

Continue ReadingGrant Funding: NIA Phase I SBIR Award

Oral Presentation: No One Left Behind: Building Low-Cost Wearables for Low-Income Communities

This is part of the monthly MassAITC webinar series. Abstract: Wearable devices such as Apple Watch and Fitbit wristband allow users to track their health statistics around the clock. They have become increasingly popular over the past few years. However, in the context of low-income areas of United States, these wearable devices are still pricey and thus constitute a critical bottleneck in their adoption. In this talk, I will present our past and ongoing works on repurposing electronic wastes, particularly everyday earphones into health trackers – from heart rate monitoring, heart sound recovery, all the way down to pulse wave velocity estimation in home settings. I will also discuss the potential of these technologies for filling the gap of remote health care. I believe this research creates a holistic approach toward recycling and repurposing electronic…

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Restoring Functional Eating in Late Stage Dementia

Jon Dekar, DESIN LLC, Zackory Erickson, CMU Robotics Institute. DESĪN LLC will enhance its existing Obi assistive feeding robot with AI-driven attention monitoring and redirection capabilities to support self-feeding in individuals with AD/ADRD who struggle with inattention. The project aims to demonstrate technical feasibility and clinical utility in long-term care settings, ultimately reducing caregiver burden and improving quality of life for affected individuals.

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AI-Driven Early Detection of AD Risk Using Speech Features

Marziye Eshghi, MGH Institute of Health Professions. This project will leverage AI-driven analysis of remotely collected speech data to detect early signs of Alzheimer’s disease (AD) by linking speech acoustic and kinematic features to AD molecular pathologies.

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