用可穿戴光谱数据区分自然光与人造光，效果远超单纯亮度阈值。
Beyond Lux thresholds: a systematic pipeline for classifying biologically relevant light contexts from wearable data
- 构建从光谱数据到光照类型分类的标准化流程，避免亮度干扰。
- 在跨被试验证中达到AUC 0.938、准确率88%的优异表现。
- 开源完整代码与数据，适合生物光照研究与健康算法开发者使用。
可穿戴光谱仪可实地量化对生物有影响的光，但可靠的上下文分类流程仍不明确。本文旨在建立并验证一种基于个体交叉验证的可复现流程及实用设计规则，用于区分自然光与人造光。分析了26名参与者每人至少7天、每10秒采样的ActLumus数据，并结合每日暴露日志。流程包括：领域选择、以10为底的对数变换、排除总强度的L2归一化（避免亮度捷径）、小时级中位数聚合、正弦/余弦小时编码，最后使用MLP分类器。结果表明，该流程在主任务上表现优异，代表性配置在保留被试划分下达到AUC 0.938（准确率88%）。而室内/室外分类仍处于可行性水平，因光谱重叠和类别不平衡导致最佳AUC约0.75，且无上下文传感器时出现多数类崩溃。阈值基线在本数据上表现不足，证实需依赖光谱-时间联合建模而非仅依赖照度阈值。结论：提供可复现、可审计的基准流程与设计规则，支持个体泛化下的光照上下文分类。所有代码、配置文件及衍生成果将公开存档（GitHub + Zenodo DOI），以促进复用与基准测试。
原文摘要 · Abstract (English)
Background: Wearable spectrometers enable field quantification of biologically relevant light, yet reproducible pipelines for contextual classification remain under-specified. Objective: To establish and validate a subject-wise evaluated, reproducible pipeline and actionable design rules for classifying natural vs. artificial light from wearable spectral data. Methods: We analysed ActLumus recordings from 26 participants, each monitored for at least 7 days at 10-second sampling, paired with daily exposure diaries. The pipeline fixes the sequence: domain selection, log-base-10 transform, L2 normalisation excluding total intensity (to avoid brightness shortcuts), hour-level medoid aggregation, sine/cosine hour encoding, and MLP classifier, evaluated under participant-wise cross-validation. Results: The proposed sequence consistently achieved high performance on the primary task, with representative configurations reaching AUC = 0.938 (accuracy 88%) for natural vs. artificial classification on the held-out subject split. In contrast, indoor vs. outdoor classification remained at feasibility level due to spectral overlap and class imbalance (best AUC approximately 0.75; majority-class collapse without contextual sensors). Threshold baselines were insufficient on our data, supporting the need for spectral-temporal modelling beyond illuminance cut-offs. Conclusions: We provide a reproducible, auditable baseline pipeline and design rules for contextual light classification under subject-wise generalisation. All code, configuration files, and derived artefacts will be openly archived (GitHub + Zenodo DOI) to support reuse and benchmarking.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。