用大模型将手机传感数据转为行为叙事，助力数字表型分析
AWARE Narrator and the Utilization of Large Language Models to Extract Behavioral Insights from Smartphone Sensing Data
- 将手机传感器数据转化为连续英文叙事文本
- 基于叙事分析大学生一周行为模式与心理状态
- 适合数字健康、行为计算领域研究者参考
智能手机搭载多种传感器，已成为个人感知的重要工具。在数字健康领域，手机可追踪与健康相关的行为和情境，推动数字表型发展——即通过数字交互数据推断行为并评估心理健康。传统方法将原始传感器数据处理为信息特征，用于统计或机器学习分析。本文提出一种新方法，系统性地将手机采集的数据转化为结构化、时间有序的叙事。AWARE Narrator 将量化传感数据转换为英语描述，生成个体活动的完整叙事。我们将其应用于大学生一周的手机数据，验证了该框架在总结个体行为、利用大语言模型分析心理状态方面的潜力。
原文摘要 · Abstract (English)
Smartphones, equipped with an array of sensors, have become valuable tools for personal sensing. Particularly in digital health, smartphones facilitate the tracking of health-related behaviors and contexts, contributing significantly to digital phenotyping, a process where data from digital interactions is analyzed to infer behaviors and assess mental health. Traditional methods process raw sensor data into information features for statistical and machine learning analyses. In this paper, we introduce a novel approach that systematically converts smartphone-collected data into structured, chronological narratives. The AWARE Narrator translates quantitative smartphone sensing data into English language descriptions, forming comprehensive narratives of an individual's activities. We apply the framework to the data collected from university students over a week, demonstrating the potential of utilizing the narratives to summarize individual behavior, and analyzing psychological states by leveraging large language models.
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