Smartphone-sensor-based human activities classification for forensics: a machine learning approach

Nchouwat Ndumgouo Ibrahim Moubarak, Njutapmvoui Mbah Mohamed Omar, Vepouyoum Njouokouo Youssef
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Abstract

The accurate classification of human activities in crime scenes during forensics (criminalistics) is of utmost importance in classifying suspicious and unlawful activities, easing their acceptability and interpretability by judges during legal procedures in courts or by other non-experts in the field of forensics. This paper implements machine learning (ML) algorithms: support vector machine (SVM) and decision tree (DT), to demonstrate with a high accuracy, how data emanating from smartphones’ sensors reveal and isolate relevant information about static and dynamic human activities in criminalistics. Smartphones’ data from five different sensors (accelerometer, gravity, orientation, Gyroscope and light), related to ten recurrent crime scenes activities, grouped into three classes of events (normal, felony and none-felony events) are classified by the proposed algorithms, with novelty being the classification decisions based on the entire period of the events and not instantaneous decision makings. Three independent data-subsets were made, with permutations done between them and at each time, two sets used for training and the third set used for testing. Time- and frequency-domain features were initially used separately and then combined for the model training and testing. The best average training accuracies of 100% and 97.8% were obtained for the DT and SVM, respectively, and the testing accuracies of 89.1% were obtained for both algorithms. We therefore believe that these results will serve as a solid persuasive and convincing argument to judges and non-experts of the field of forensics to accept and easily interpret computer-aided classification of suspicious activities emanating from criminalistic studies.
基于智能手机传感器的取证人类活动分类:一种机器学习方法
在取证(犯罪学)过程中,对犯罪现场中的人类活动进行准确分类,对于对可疑和非法活动进行分类、简化法庭法律程序中法官或其他非取证领域专家对这些活动的可接受性和可解释性至关重要。本文采用了机器学习(ML)算法:支持向量机(SVM)和决策树(DT),以高精度展示智能手机传感器的数据如何揭示和分离犯罪学中人类静态和动态活动的相关信息。智能手机的数据来自五个不同的传感器(加速度计、重力传感器、方向传感器、陀螺仪和光),与十个重复出现的犯罪现场活动有关,分为三类事件(正常事件、重罪事件和非重罪事件),并通过所提出的算法进行了分类,其新颖之处在于分类决策是基于整个事件期间的,而不是即时决策。我们制作了三个独立的数据子集,在它们之间进行排列组合,每次两组用于训练,第三组用于测试。时域和频域特征最初是分开使用的,然后在模型训练和测试中合并使用。DT 和 SVM 的最佳平均训练准确率分别为 100%和 97.8%,两种算法的测试准确率均为 89.1%。因此,我们相信,这些结果将对法官和法医领域的非专业人员具有坚实的说服力和信服力,使他们能够接受并轻松解读计算机辅助对犯罪学研究中产生的可疑活动进行的分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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