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  4. Personal resilience can be well estimated from heart rate variability and paralinguistic features during human–robot conversations
 
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Personal resilience can be well estimated from heart rate variability and paralinguistic features during human–robot conversations

Journal
Sensors
Journal Volume
21
Journal Issue
17
Date Issued
2021
Author(s)
Hsu S.-M
Chen S.-H
SUE-HUEI CHEN  
TSUNG-REN HUANG  
DOI
10.3390/s21175844
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113824816&doi=10.3390%2fs21175844&partnerID=40&md5=1cabe5b8fa8c47c28f3cbad68583dcf7
https://scholars.lib.ntu.edu.tw/handle/123456789/606264
Abstract
Mental health is as crucial as physical health, but it is underappreciated by mainstream biomedical research and the public. Compared to the use of AI or robots in physical healthcare, the use of AI or robots in mental healthcare is much more limited in number and scope. To date, psychological resilience—the ability to cope with a crisis and quickly return to the pre-crisis state—has been identified as an important predictor of psychological well-being but has not been commonly considered by AI systems (e.g., smart wearable devices) or social robots to personalize services such as emotion coaching. To address the dearth of investigations, the present study explores the possi-bility of estimating personal resilience using physiological and speech signals measured during hu-man–robot conversations. Specifically, the physiological and speech signals of 32 research participants were recorded while the participants answered a humanoid social robot’s questions about their positive and negative memories about three periods of their lives. The results from machine learning models showed that heart rate variability and paralinguistic features were the overall best predictors of personal resilience. Such predictability of personal resilience can be leveraged by AI and social robots to improve user understanding and has great potential for various mental healthcare applications in the future. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Automatic personality recognition
Human–robot interaction
Personal resilience
Physiological signals
Speech signals
Health care
Heart
Linguistics
Speech communication
Biomedical research
Health care application
Heart rate variability
Machine learning models
Paralinguistic
Physical health
Psychological well-being
Smart wearables
Social robots
heart rate
human
interpersonal communication
robotics
social interaction
speech
Communication
Heart Rate
Humans
Robotics
Social Interaction
Speech
SDGs

[SDGs]SDG3

Type
journal article

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