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The growth of smart devices is making typical homes more intelligent. In this work, in collaboration with therapists, we introduce a home-based AI therapist that takes advantage of the smart home environment to screen the day-to-day functioning and infer mental wellness of an occupant. Unlike existing “chatbot” works that identify the mental status of users through conversation, our AI therapist additionally leverages smart devices and sensors throughout the home to infer mental well-being and assesses a user's daily functioning. We propose a series of 37 dimensions of daily functioning, that our system observes through conversing with the user and detecting daily activity events using sensors and smart sensors throughout the home. Our system utilizes these 37 dimensions in conjunction with novel natural language processing architectures to detect abnormalities in mental status (e.g., angry or depressed), well-being, and daily functioning and generate responses to console users when abnormalities are detected. Through a series of user studies, we demonstrate that our system can converse with a user naturally, accurately detect abnormalities in well-being, and provide appropriate responses consoling users.
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A Drone-based System for Intelligent and Autonomous Homes
Homes are becoming more intelligent due to the growth of smart sensors and devices found in typical homes. However, most of these sensors and devices function independently from one another, limiting the amount of utility and services a truly "smart" home can provide. In this demonstration, we introduce two key ideas towards more intelligent homes. First, we explore the usage of mobile drones in the home environment. Second, we propose DIA, a system that seamlessly connects to the home environment and automatically discovers and jointly utilizes smart sensors and actuators around the home to provide services that are otherwise not possible. We demonstrate three services that DIA enables.
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- PAR ID:
- 10327557
- Date Published:
- Journal Name:
- Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems
- Page Range / eLocation ID:
- 349 to 350
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3485730.3492881
