用可穿戴设备和手机数据，提前七天预测孤独感，准确率达82%。
Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings
- 结合智能戒指、手表和手机，监测生理与行为信号。
- 个性化模型预测七天后孤独水平，准确率和F1值均为0.82。
- 通过解释性分析揭示关键预测因子，适合心理健康干预研究。
孤独对身心健康有深远影响。尽管先前研究已利用手机传感检测心理问题，但极少使用先进可穿戴设备来预测孤独感及其生理表现。本研究旨在探索利用智能戒指、手表等可穿戴设备，监测孤独的早期生理指标，并结合智能手机捕捉初始行为迹象，以实现孤独感预测。研究基于对大学生群体的长期监测，构建了包含生理与行为信息的综合数据集，采用个性化机器学习模型，成功实现了七天前孤独水平的预测，准确率为0.82，F1分数为0.82。同时，通过Shapley值分析提升了模型可解释性。该研究提供的丰富数据与预测方法，有望助力高风险人群的早期识别与干预。
原文摘要 · Abstract (English)
The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and estimate the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk.
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