Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations
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 resil...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/17/5844 |
| _version_ | 1797520854291054592 |
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| author | Shin-Min Hsu Sue-Huei Chen Tsung-Ren Huang |
| author_facet | Shin-Min Hsu Sue-Huei Chen Tsung-Ren Huang |
| author_sort | Shin-Min Hsu |
| collection | DOAJ |
| description | 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 possibility of estimating personal resilience using physiological and speech signals measured during human–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. |
| first_indexed | 2024-03-10T08:04:27Z |
| format | Article |
| id | doaj.art-ec8bca1c88234236ae07dc205409ab07 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:04:27Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ec8bca1c88234236ae07dc205409ab072023-11-22T11:13:19ZengMDPI AGSensors1424-82202021-08-012117584410.3390/s21175844Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot ConversationsShin-Min Hsu0Sue-Huei Chen1Tsung-Ren Huang2Department of Psychology, National Taiwan University, Taipei 10617, TaiwanDepartment of Psychology, National Taiwan University, Taipei 10617, TaiwanDepartment of Psychology, National Taiwan University, Taipei 10617, TaiwanMental 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 possibility of estimating personal resilience using physiological and speech signals measured during human–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.https://www.mdpi.com/1424-8220/21/17/5844automatic personality recognitionhuman–robot interactionpersonal resiliencephysiological signalsspeech signals |
| spellingShingle | Shin-Min Hsu Sue-Huei Chen Tsung-Ren Huang Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations Sensors automatic personality recognition human–robot interaction personal resilience physiological signals speech signals |
| title | Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations |
| title_full | Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations |
| title_fullStr | Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations |
| title_full_unstemmed | Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations |
| title_short | Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations |
| title_sort | personal resilience can be well estimated from heart rate variability and paralinguistic features during human robot conversations |
| topic | automatic personality recognition human–robot interaction personal resilience physiological signals speech signals |
| url | https://www.mdpi.com/1424-8220/21/17/5844 |
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