Stanford University

Intelligent Senior Well-Being Support


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.

Extended Summary


Detecting Falls

Automatic fall detection at night with thermal sensors and imaging.

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Sleep Monitoring

Sleep posture reconstruction and respiratory rate analysis.

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Age Prediction

Detecting faces and predicting age helps identify patients.

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Human Emotions

Automatic fall detection at night with thermal sensors and imaging.

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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.


Guido Pusiol
Activity Recognition Inc.

Alan Luo
Stanford AI Lab

Tim Hsieh
Stanford AI Lab

Jay Luxenberg
On Lok, Inc.

Grace Li
On Lok, Inc.


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)
October 2016

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​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
July 2015

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​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
June 2015

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​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)
July 2014

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​Developmental and Postural Changes in Children Visual Access to Faces

Michael C. Frank, Kaia Simmons, Daniel Yurovsky, Guido Pusiol

The Annual Meeting of the Cognitive Science Society (CogSci)
August 2013

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​Unsupervised Discovery, Modeling, and Analysis of Long Term Activities

Guido Pusiol, Francois Bremond, Monique Thonnat

International Conference on Computer Vision Systems (ICVS)
Septmber 2011

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​Trajectory Based Activity Discovery

Guido Pusiol, Francois Bremond, Monique Thonnat

IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)
August 2010

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