用iPhone传感器数据识别行为，为法医调查提供量化依据
Forensic Activity Classification Using Digital Traces from iPhones: A Machine Learning-based Approach
- 基于手机传感器数据构建机器学习模型
- 可区分167组活动组合，准确率高
- 适合法医、刑侦人员用于还原行为轨迹
智能手机和智能手表在日常生活中无处不在，提供了丰富的用户行为信息。特别是手机内置运动传感器产生的数字痕迹，为法医调查人员了解个人身体活动提供了可能。本文提出一种基于机器学习的方法，将数字痕迹转化为不同物理活动的似然比（LR）。在新构建的数据集NFI_FARED上进行评估，该数据集包含四种不同型号iPhone采集的19种活动标签数据。结果表明，该方法可有效区分167组（共171组）活动组合。该方法还可扩展用于同时分析多个活动或活动组的似然性，并生成活动时间线，助力法医调查的早期与后期阶段。研究中使用的数据集及全部代码均已公开，以促进该领域的进一步研究。
原文摘要 · Abstract (English)
Smartphones and smartwatches are ever-present in daily life, and provide a rich source of information on their users' behaviour. In particular, digital traces derived from the phone's embedded movement sensors present an opportunity for a forensic investigator to gain insight into a person's physical activities. In this work, we present a machine learning-based approach to translate digital traces into likelihood ratios (LRs) for different types of physical activities. Evaluating on a new dataset, NFI\_FARED, which contains digital traces from four different types of iPhones labelled with 19 activities, it was found that our approach could produce useful LR systems to distinguish 167 out of a possible 171 activity pairings. The same approach was extended to analyse likelihoods for multiple activities (or groups of activities) simultaneously and create activity timelines to aid in both the early and latter stages of forensic investigations. The dataset and all code required to replicate the results have also been made public to encourage further research on this topic.
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