We are designing an integrated solution for the remote monitoring, assessment and support of seniors living independently at home. We aim at improving the speed and reliability of health risk detection and support timely, personalized intervention.
We are investigating the use of multiple sensors for the detection and recording of daily activities, lifestyle patterns, emotions, and vital signs, as well as the development of intelligent mechanisms for translating multi-sensor inputs into accurate situational assessment and rapid response. Our goal is to allow seniors to extend their capacity to live at home, improve their quality of life and avoid unnecessary and costly relocation into institutional care.
We are monitoring 17 activities of clinical relevance: eating, sleeping, falls, slowed movements, unstable transfers, front door loitering, day and night reversals, fluid intake, chair and bed immobility, urinary frequency, restlessness, fever, alcohol consumption, pill consumption, high salt diets, substance abuse, and food consumption.
The Stanford Thailand Research Consortium
STRC supports Stanford faculty research pertinent to areas of social, economic, and technological development vital to Thailand’s future success.
Consortium-funded research projects align to central themes of raising human values, increasing economic prosperity, ensuring environmental protection, and advancing social well-being throughout Thai society. Established in 2018, the Consortium creates avenues for faculty across Stanford schools and disciplines to engage in research with real-world applications.
ARTIFICIAL INTELLIGENCE-BASED TECHNOLOGIES FOR IMPROVING ELDERLY CARE
AI for Elderly Care uses human-centered design and AI-based technologies to improve caregiving of the elderly, a growing population in Thailand. An example of using AI to support elder care is a computer vision system that can detect mobility in elders who may be at home alone while their caregivers are at work. The project, led by the Clinical Excellence Research Center’s Professor Arnold Milstein (medicine), Kevin Schulman (medicine) and Professor Amit Kaushal (bioengineering), is a collaboration between Stanford Medicine, Stanford’s Artificial Intelligence Laboratory, and the d.school.
We have a pilot at Onlok home-care facilities. We will install non intrusive sensors to detect the target activities automatically of volunteers and design algorithms to automatically analyze long term low level sensorial information.
Ehsan Adeli, PhD.
Scientist, Stanford AI Lab, Stanford Vision and Learning, Computer Science Department
Clinical Assistant Professor, Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine
Doctoral Student, Computer Science
Alan is a Ph.D. student in the Stanford Vision and Learning Lab, advised by Prof. Fei-Fei Li. His main research interests include weakly-supervised learning, transfer learning, and deep learning.
Vittavat (Tor) Termglinchan
Dr. Vittavat (Tor) Termglinchan is an Instructor at the Clinical Excellence Research Center at Stanford University. He is very passionate about elderly care and health innovations to better the care of older adults and to reduce caregiver burden. He will utilize his expertise to conduct translational research on using computer vision technology to continuously detect seniors’ behaviors and provide the corresponding descriptive analytics.
Swati DiDonato, MD. MBA
Dr. DiDonato is a Clinical Assistant Professor in the Stanford School of Medicine. She is the co-lead of Stanford's Value Based Care Academy, serves as Stanford's MD/MBA career advisor, and has worked with a variety of companies focused on bringing the latest technology to healthcare.
Research Operations Manager
Tracy is a 15+ year adminstrative veteran for the Stanford School of Medicine. She started at the Lane Medical Library and is currently with the Clinical Excellence Research Center.
Classification of Developmental Disorders Using Eye-Movements
Guido Pusiol, Andre Esteva, Scott S. Hall, Michael Frank, Arnold Milstein, Li Fei-Fei
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Unsupervised Discovery of Human Activities from Long-Videos
Salma Elloumi, Serhan Cosar, Guido Pusiol, Francois Bremond, Monique Thonnat
The Institution of Engineering and Technology, IET Computer Vision
Quantifying Naturalistic Social Gaze in Fragile X Syndrome Using a Novel Eye-Tracking Paradigm
Scott S. Hall, Michael C. Frank, Guido Pusiol, Faraz Farzin, Amy A. Lightbody, Allan L. Reiss
American Journal of Medical Genetics, Part 1: Neuropsychiatric Genetics
Discovering the Signatures of Joint Attention in Child-Caregiver Interaction
Guido Pusiol, Laura Soriano, Li Fei-Fei, Michael C. Frank
The Annual Meeting of the Cognitive Science Society (CogSci)