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arXiv 2201.11230cs.HCcs.LG

Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020

University of California Irvine(加州大学尔湾分校);University of California, Irvine(加州大学尔湾分校)

Salar Jafarlou, Jocelyn Lai, Zahra Mousavi, Sina Labbaf, Ramesh Jain, Nikil Dutt, Jessica Borelli, Amir Rahmani

英文摘要:

Monitoring and understanding affective states are important aspects of healthy functioning and treatment of mood-based disorders. Recent advancements of ubiquitous wearable technologies have increased the reliability of such tools in detecting and accurately estimating mental states (e.g., mood, stress, etc.), offering comprehensive and continuous monitoring of individuals over time. Previous attempts to model an individual's mental state were limited to subjective approaches or the inclusion of only a few modalities (i.e., phone, watch). Thus, the goal of our study was to investigate the capacity to more accurately predict affect through a fully automatic and objective approach using multiple commercial devices. Longitudinal physiological data and daily assessments of emotions were collected from a sample of college students using smart wearables and phones for over a year. Results showed that our model was able to predict next-day affect with accuracy comparable to state of the art methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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