Department of Psychology, National Taiwan University, Taipei 10617, Taiwan.
MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei 10617, Taiwan.
Sensors (Basel). 2021 Aug 30;21(17):5844. doi: 10.3390/s21175844.
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.
心理健康和身体健康一样重要，但它在主流的生物医学研究和公众中未得到充分重视。与在身体保健中使用人工智能或机器人相比，在心理健康保健中使用人工智能或机器人的数量和范围要受到更多限制。迄今为止，心理弹性——应对危机并迅速恢复到危机前状态的能力——已被确定为心理健康的一个重要预测指标，但人工智能系统（例如，智能可穿戴设备）或社交机器人尚未普遍考虑将其用于个性化服务，例如情绪辅导。为了解决研究不足的问题，本研究探讨了使用人类-机器人对话期间测量的生理和语音信号来估计个人弹性的可能性。具体来说，在 32 名研究参与者回答类人社交机器人关于他们生活中三个时期的积极和消极记忆的问题时，记录了他们的生理和语音信号。来自机器学习模型的结果表明，心率变异性和副语言特征是个人弹性的整体最佳预测指标。人工智能和社交机器人可以利用这种个人弹性的可预测性来提高用户的理解能力，并且在未来具有各种心理健康保健应用的巨大潜力。
