Chen Tianhua
School of Computing and Engineering, University of Huddersfield, Huddersfield, WYK, HD1 3DH, UK.
Brain Inform. 2024 Dec 5;11(1):29. doi: 10.1186/s40708-024-00243-w.
The mental health of students in higher education has been a growing concern, with increasing evidence pointing to heightened risks of developing mental health condition. This research aims to explore whether day-long heart rate sequences, collected continuously through Apple Watch in an open environment without restrictions on daily routines, can effectively indicate mental states, particularly stress for university students. While heart rate (HR) is commonly used to monitor physical activity or responses to isolated stimuli in a controlled setting, such as stress-inducing tests, this study addresses the gap by analyzing heart rate fluctuations throughout a day, examining their potential to gauge overall stress levels in a more comprehensive and real-world context. The data for this research was collected at a public university in the UK. Using signal processing, both original heart rate sequences and their representations, via Fourier transformation and wavelet analysis, have been modeled using advanced machine learning algorithms. Having achieving statistically significant results over the baseline, this provides a understanding of how heart rate sequences alone may be used to characterize mental states through signal processing and machine learning, with the system poised for further testing as the ongoing data collection continues.
高等教育阶段学生的心理健康问题日益受到关注，越来越多的证据表明他们出现心理健康问题的风险在增加。本研究旨在探讨通过苹果手表在开放环境中不受日常活动限制连续收集的全天心率序列，是否能够有效指示心理状态，特别是大学生的压力状况。虽然心率（HR）通常用于在诸如压力诱导测试等受控环境中监测身体活动或对孤立刺激的反应，但本研究通过分析一整天的心率波动来填补这一空白，研究其在更全面的现实环境中衡量总体压力水平的潜力。本研究的数据是在英国一所公立大学收集的。通过信号处理，原始心率序列及其通过傅里叶变换和小波分析得到的表示形式，都已使用先进的机器学习算法进行建模。在超过基线水平取得了具有统计学意义的结果后，这有助于理解仅通过信号处理和机器学习，心率序列如何能够用于表征心理状态，随着持续的数据收集，该系统准备好进行进一步测试。
