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 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.
Highlights
The present study confirms the possibility of estimating personal resilience from speech and physiological signals
Our binary classification of personal resilience successfully achieved F1-scores as high as 0.86 in the cases of predicting social resources by the heart rate variability (HRV) features during the recall of negative memories and predicting structured style by the paralinguistic features during the recall of positive and negative memories
Our results suggest that the HRV and paralinguistic features are the best predictors of resilience
Summary
People often take good care of their physical health while ignoring their mental health. Individuals with better protective factors are less likely to suffer from mental health problems, especially when facing traumatic or stressful life events One of these protective factors is trait resilience [3], which is a positive personality characteristic indicative of one’s adaptability in the face of adversity. In our resilience-predictive models, the physiology-based predictors included galvanic skin response (GSR), electrocardiograms (ECG), and heart rate variability (HRV); the speech-based predictors encompassed audio and linguistic features. While these physiological signals have been found to correlate with psychological resilience [21], speech markers of resilience, if any, are not yet identified. The present study aimed to explore the use of speech signals for predicting personal resilience and compare the predictive powers of these two data sources
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