The Speck from Blue Iris Labs is a wearable light exposure sensor designed to help users understand daily light exposure patterns and how those patterns may relate to sleep and circadian health. Source: blueirislabs.com
This is part of the monthly MassAITC webinar series. Abstract: Hype around the potential of generative AI tools to transform healthcare is at an all time high, but their design and utility is often not user or patient-centered. This is particularly true for generalized large language models that have limited UI/UX features, may hallucinate or give incorrect medical/healthcare advice, or may lack conversational clarity and specificity. In fact, these tools are rarely designed in partnership with the end-user or patients they intend to serve. As part of a2 Collective Pilot award in partnership with MassAITC, S-3 Research and California State Fullerton have been developing “TRIALCHAT”, a multiagentic AI tool with the goal of navigating older adults and their caregivers to resources related to Alzheimer’s Disease and clinical research participation opportunities. Lessons learned from a technology…
https://www.youtube.com/watch?v=nxRdD0OHu1c Abstract: Digital phenotyping is transforming aging research by enabling high-frequency, real-world measurement of cognition, behavior, symptoms, and context through smartphones, wearables, and passive sensing technologies. This talk will review how digital health tools can complement traditional clinic-based assessments by capturing intraindividual variability, diurnal patterns, environmental influences, and subtle changes in cognitive and functional performance that may signal risk for neurodegenerative disease. Using examples from studies of healthy aging, MCI, Alzheimer’s disease risk, dementia caregiving, and related clinical populations, the talk will highlight best practices for designing digital phenotyping protocols, balancing participant burden with data richness, maximizing adherence, integrating active cognitive assessments with passive data streams and biomarkers, and applying analytic approaches that distinguish within-person change from between-person differences. The session will emphasize opportunities for digital phenotyping to improve early detection, clinical trial endpoints, remote…
https://www.youtube.com/watch?v=L70oKKvsgrA Abstract: Hype around the potential of generative AI tools to transform healthcare is at an all time high, but their design and utility is often not user or patient-centered. This is particularly true for generalized large language models that have limited UI/UX features, may hallucinate or give incorrect medical/healthcare advice, or may lack conversational clarity and specificity. In fact, these tools are rarely designed in partnership with the end-user or patients they intend to serve. As part of a2 Collective Pilot award in partnership with MassAITC, S-3 Research and California State Fullerton have been developing “TRIALCHAT", a multiagentic AI tool with the goal of navigating older adults and their caregivers to resources related to Alzheimer’s Disease and clinical research participation opportunities. Lessons learned from a technology design and development process that involved rapid prototyping,…
DiMe is the leading global nonprofit advancing digital medicine, and the Library is one of their most valuable contributions to the field — a free, publicly available catalog of digital endpoints used in clinical research across therapeutic areas. For sponsors and researchers designing studies that incorporate wearable sensors, it's an essential reference. At VivoSense, our work is grounded in the belief that shared resources make the whole field stronger. No single organization can build the evidence base alone. Supporting the Library is one way we can contribute to the infrastructure the field needs, and partnering with DiMe, the recognized expert in this space, makes that contribution meaningful. Source: https://www.linkedin.com/posts/digitalhealth-digitalmeasures-healthcareinnovation-share-7441863834992160768-YIys/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAANw6GUBUuqlp9jndjMTsgXQj7lf9CkVSnE
Abstract: A system includes a sensing assembly configured to be disposed on a mastoid of a user. The sensing assembly includes a housing, a first sensor disposed in the housing and configured to measure a first Phybrata signal from the user, and a second sensor disposed in the housing and configured to measure a second Phybrata signal from the user, the second Phybrata signal different from the first Phybrata signal. A controller is in communication with the sensing assembly. The controller is configured to : receive a first Phybrata data from the sensing assembly, the first Phybrata data including information obtained from the first Phybrata signal and the second Phybrata signal. The controller is configured to determine a Phybrata parameter associated with the user based on the first Phybrata data, and determine a Phybrata signature…
Authors: Xian Du, Meysam Safarzadeh, Maoqin Zhu, Shishir Prasad, Sudeshna Das, Joohyun Chung Abstract Pain monitoring and assessment traditionally rely on subjective methods such as self-reports and caregiver evaluations, which can be costly and often inaccurate due to their inherent subjectivity and reliance on the individual's communication skills. Many objective methods have been introduced to address these issues, primarily utilizing single or multiple wearable sensor modalities. However, these approaches face challenges in home care settings, particularly concerning continuous wearability and discomfort, especially among elderly users. An alternative solution is using patient monitoring tools such as various imaging modalities to detect pain-related facial expressions. In this paper, we developed a new transformer model to extract pain-related features from facial expressions captured through three imaging modalities—RGB, thermal, and depth across sequential images. This method can leverage the…