Integration Analysis of Heterogeneous Data on Mind Externalization of Elderly People at Home

Chen, Sinan; Ozono, Hayato; Nakamura, Masahide

doi:10.1007/978-3-031-05654-3_13

“…Such as a function for reviewing dialogue logs as a personal diary, watching YouTube videos, information retrieval, and calendar event registration have been incorporated into agents, enabling control of various functions through spoken dialogue between an older adult and an agent. Also considered is a personalized health question function that uses a wearable health device to capture changes in an older adult's features and link the measurement results to a spoken dialogue agent, and an approach [3] that estimates the cause of health changes from dialogue logs.…”

Section: Introductionmentioning

confidence: 99%

事前学習済みモデルと音声対話エージェントを用いた顔識別システムの開発

CHEN

¹

,

Nakamura

²

2022

The Proceedings of Design &Amp; Systems Conference

0

View full text Add to dashboard Cite

Our research group is currently studying and developing listening services using spoken dialogue agents and IoT technologies to assist the "mind" of the elderly at home. However, the user identification function, an essential part of the service, has not yet been realized. It is difficult to determine the identity of the person who interacts with the spoken dialogue agent. Although with the rapid development of the artificial intelligence field, various smart devices and services using deep learning have appeared in the face recognition technology, problems exist, including costs and computational resources to build and apply a recognition model. The purpose of this paper is to develop a facial identification system using the pre-trained model and spoken dialogue agent. Our key ideas include automatic training data generation by spoken dialogue between the user and the agent and the acquisition and comparison of facial features using a pre-trained model. In this way, our face identification system can be easier introduced and expected with only a general-purpose computer and a Web camera, without needing a conventional Internet connection and manual labeling of training data.

show abstract

A Study for Estimating Caregiving Contexts Based on Extracting Nonverbal Information from Elderly People at Home

Chen

¹

,

Nakamura

²

,

Yasuda

³

2023

Lecture Notes in Computer Science

0

View full text Add to dashboard Cite

Developing a Facial Identification System Using Pre-Trained Model and Spoken Dialogue Agent

Chen

¹

,

Nakamura

²

2022

2022 International Balkan Conference on Communications and Networking (BalkanCom)

Self Cite

3

0

View full text Add to dashboard Cite

Our research group is currently studying and developing listening services using spoken dialogue agents and IoT technologies to assist the "mind" of the elderly at home. However, the user identification function, an essential part of the service, has not yet been realized. It is difficult to determine the identity of the person who interacts with the spoken dialogue agent. Although with the rapid development of the artificial intelligence field, various smart devices and services using deep learning have appeared in the face recognition technology, problems exist, including costs and computational resources to build and apply a recognition model. The purpose of this paper is to develop a facial identification system using the pre-trained model and spoken dialogue agent. Our key ideas include automatic training data generation by spoken dialogue between the user and the agent and the acquisition and comparison of facial features using a pre-trained model. In this way, our face identification system can be easier introduced and expected with only a general-purpose computer and a Web camera, without needing a conventional Internet connection and manual labeling of training data.

show abstract

Integration Analysis of Heterogeneous Data on Mind Externalization of Elderly People at Home

Cited by 4 publications

References 18 publications

事前学習済みモデルと音声対話エージェントを用いた顔識別システムの開発

事前学習済みモデルと音声対話エージェントを用いた顔識別システムの開発

A Study for Estimating Caregiving Contexts Based on Extracting Nonverbal Information from Elderly People at Home

Developing a Facial Identification System Using Pre-Trained Model and Spoken Dialogue Agent

Contact Info

Product

Resources

About