In partnership with the medical device design and development firm, Cortex Design, a final commercial design of the NEURVESTA device was finalized and 25 units have been successfully produced for ongoing clinical trials. Source: https://cortex-design.com/work/neursantys/
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…
This presentation was given at the Prevention Research Center at Penn State University on March 25, 2026 (https://ssri.psu.edu/events/digital-adaptive-interventions-common-misconceptions-and-opportunities-prc-quantdev-joint) and at the Mobile Health Training Institute (mHTi) on April 13, 2026 (https://mhti.md2k.org/program/2026-program/).
Abstract: We present our ongoing work on the development of an online reinforcement learning (RL) algorithm for dyadic digital intervention settings in which the task for the RL algorithm is to assist the target person with a difficult illness be adherent to behavioral activities. To achieve this goal the RL algorithm will not only deliver digital interventions to the target person but also deliver interventions to assist the care partner to manage caregiving burden and help the two individuals improve their relationship. That is, different RL components target different elements of the dyad. The RL algorithm is a multi-agent RL algorithm in which the 3 agents make decisions on the 3 elements of the dyad. We incorporate domain knowledge in the form of approximal causal directed acyclic graphs to speed up online learning in this sparse data setting. This work is…
This is part of the monthly MassAITC webinar series. Abstract: Cognitive rehabilitation therapy supports individuals living with mild cognitive impairment and early-stage dementia in maintaining and improving function in daily life. However, access remains limited due to constraints in the availability and scalability of trained therapists. Recent advances in artificial intelligence, combined with evolving reimbursement pathways for remote care in the United States, now make virtual delivery models increasingly viable, creating new opportunities to expand access to high-quality cognitive care. Moneta Health has developed a telephone-based cognitive rehabilitation platform that enables structured, personalized therapy sessions delivered remotely and overseen by licensed speech-language pathologists. The platform leverages AI-driven speech analysis and automated session orchestration to support consistent therapy delivery while preserving clinician oversight, enabling older adults to engage in care from their homes through a familiar…
Murphy, S.A., & Nahum-Shani, I. (2026, January) Just In Time Adaptive Interventions: Opportunities, Misconceptions, and the Promise of AI-based Algorithms. NIH Digital Health Scientific Interest Group (SIG)’s Speaker Series. NIH. Bethesda, MD. You can watch this presentation here: https://videocast.nih.gov/watch/0b1c35f9-0852-11f1-9f14-124f0a52e769