https://scholars.lib.ntu.edu.tw/handle/123456789/606264
標題: | Personal resilience can be well estimated from heart rate variability and paralinguistic features during human–robot conversations | 作者: | Hsu S.-M Chen S.-H SUE-HUEI CHEN TSUNG-REN HUANG |
關鍵字: | 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 | 公開日期: | 2021 | 卷: | 21 | 期: | 17 | 來源出版物: | Sensors | 摘要: | 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. |
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 |
ISSN: | 14248220 | DOI: | 10.3390/s21175844 |
顯示於: | 心理學系 |
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