用手机自动捕捉微笑，客观衡量日常幸福感。
Smartphone monitoring of smiling as a behavioral proxy of well-being in everyday life
- 通过手机视频分析用户自然微笑强度，实现无感监测。
- 微笑频率与全国幸福指数高度相关（r=0.92），且符合日常行为规律。
- 适合心理学、健康科技与大规模行为研究者参考。
主观幸福感是个体与社会健康的核心，但传统测量依赖易受回忆偏差影响的自我报告，参与者负担重，难以真实反映日常生活中的幸福感。我们假设，在自然手机交互中捕捉到的自发微笑可作为积极情绪的可扩展、客观行为指标。为此，我们分析了233名同意参与的用户在一周内被动记录的405,448段视频片段，利用深度学习模型量化微笑强度。结果显示，微笑强度呈现显著的日间和日周期模式。一周内的日均微笑强度与国家幸福调查数据高度相关（r=0.92），日间节律与日重构法研究结果一致（r=0.80）。日均微笑强度越高，越与更高身体活动量（β = 0.043，95% CI [0.001, 0.085]）和更强光照暴露相关（β = 0.038，[0.013, 0.063]），而与手机使用时间无显著关联。这些发现表明，被动式智能手机感知可成为研究情绪行为动态的强大生态有效方法，为在群体层面理解幸福感打开新途径。
原文摘要 · Abstract (English)
Subjective well-being is a cornerstone of individual and societal health, yet its scientific measurement has traditionally relied on self-report methods prone to recall bias and high participant burden. This has left a gap in our understanding of well-being as it is expressed in everyday life. We hypothesized that candid smiles captured during natural smartphone interactions could serve as a scalable, objective behavioral correlate of positive affect. To test this, we analyzed 405,448 video clips passively recorded from 233 consented participants over one week. Using a deep learning model to quantify smile intensity, we identified distinct diurnal and daily patterns. Daily patterns of smile intensity across the week showed strong correlation with national survey data on happiness (r=0.92), and diurnal rhythms documented close correspondence with established results from the day reconstruction method (r=0.80). Higher daily mean smile intensity was significantly associated with more physical activity (Beta coefficient = 0.043, 95% CI [0.001, 0.085]) and greater light exposure (Beta coefficient = 0.038, [0.013, 0.063]), whereas no significant effects were found for smartphone use. These findings suggest that passive smartphone sensing could serve as a powerful, ecologically valid methodology for studying the dynamics of affective behavior and open the door to understanding this behavior at a population scale.
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