Poster Presentation: Alzheimer’s Association International Conference 2025
Title: Improving Access to Dementia Care through AI-Powered Cognitive Rehabilitation Therapy Authors: Jennifer Flexman Abstract: Forthcoming
Oral Presentation: Alzheimer’s Association International Conference 2025 – Technology And Dementia Preconference
Jennifer Flexman presented "Improving Access to Dementia Care though AI-Powered Cognitive Rehabilitation Therapy" at the Technology And Dementia Preconference during Session 5: Data Blitz.
Poster Presentation: Alzheimer’s Association International Conference 2025 – Technology and Dementia Preconference
Title: Using Light Exposure to Improve Sleep and Circadian Health for People with Dementia Authors: Erik Page, William Huang, Dave Harris, Hank Ibser
Award: Jiani Zeng, co-founder and CPO of Butlr wins 2025 McKnight’s Women of Distinction award in the Commercial Excellence category
The technology designed by Zeng has been key in improving senior living and care operations, helping staff members effectively monitor residents through motion detection, enabling them to respond quickly to acute health risks without compromising resident privacy.Forster Stubbs, McKnight Senior Living The work of Jiani Zeng, a Chinese designer, researcher, and co-founder and chief product officer of Butlr Technologies, often takes place in the background, but the results always end up at the forefront. Thanks in part to her efforts, Butlr is the first company to fuse artificial intelligence and body heat sensing technology to provide insights into how humans use indoor space for living and working while ensuring anonymity. This technology has significant applications in the senior living and care sector and helped earn Zeng a 2025 McKnight’s Women of Distinction award in the Commercial Excellence…
Patent Awarded: AI powered mobility assessment system (No. US 12,343,138 B2)
Abstract: To assess the mobility of a user, a mobility assessment system obtains a video of a user having a plurality of video frames from a camera. The mobility assessment system generates a three-dimensional (3D) skeleton model of the user based on the plurality of video frames, and determines a range of motion of the user based on a change in position of the 3D skeleton model over the plurality of video frames. Then the mobility assessment system provides an indication of the range of motion of the user for display. Also, the mobility assessment system delivers tailored exercises and suggestions to enhance user mobility and reduce the risk of falls. Source: US-12343138-B2
Publication: Reinforcement Learning on Dyads to Enhance Medication Adherence
Authors: Ziping Xu, Hinal Jajal, Sung Won Choi, Inbal Nahum-Shani, Guy Shani, Alexandra M. Psihogios, Pei-Yao Hung, Susan A. Murphy Abstract Medication adherence is critical for the recovery of adolescents and young adults (AYAs) who have undergone hematopoietic cell transplantation. However, maintaining adherence is challenging for AYAs after hospital discharge, who experience both individual (e.g. physical and emotional symptoms) and interpersonal barriers (e.g., relational difficulties with their care partner, who is often involved in medication management). To optimize the effectiveness of a three-component digital intervention targeting both members of the dyad as well as their relationship, we propose a novel Multi-Agent Reinforcement Learning (MARL) approach to personalize the delivery of interventions. By incorporating the domain knowledge, the MARL framework, where each agent is responsible for the delivery of one intervention component, allows for faster learning…
Publication: Proceedings from SOUPS ’25 – Can You Walk Me Through It? Explainable SMS Phishing Detection using LLM-based Agents
Authors: Yizhu Wang, Haoyu Zhai, Chenkai Wang, Qingying Hao, Nick A Cohen, Roopa Foulger, Jonathan A Handler, and Gang Wang. Abstract SMS phishing poses a significant threat to users, especially older adults. Existing defenses mainly focus on phishing detection, but often cannot explain why the SMS is malicious to lay users. In this paper, we use large language models (LLMs) to detect SMS phishing while generating evidence-based explanations. The key challenge is that SMS is short, lacking the necessary context for security reasoning. We develop a prototype called SmishX which gathers external contexts (e.g., domain and brand information, URL redirection, and web screenshots) to augment the chain-of-thought (CoT) reasoning of LLMs. Then, the reasoning process is converted into a short explanation message to help users with their decision-making. Evaluation using real-world SMS datasets shows SmishX can achieve…