Senior Care | Stanford Partnership in AI-Assisted Care (PAC)

Overview

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

Activities

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.