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

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 fiel...

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Bibliographic Details
Published in:	Journal of Electrical Systems and Information Technology Vol. 11; no. 1; pp. 33 - 16
Main Authors:	Moubarak, Nchouwat Ndumgouo Ibrahim, Omar, Njutapmvoui Mbah Mohamed, Youssef, Vepouyoum Njouokouo
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2024 Springer Nature B.V SpringerOpen
Subjects:	Accelerometers Algorithms Artificial Intelligence Classification Computer aided testing Computer forensics Crime Crime scenes Criminal law Criminal procedure Decision tree Decision trees Electrical Engineering Electrical Machines and Networks Emission Engineering Forensic sciences Forensics Human activities Information Systems and Communication Service Machine learning Mechanical Engineering Permutations Power Electronics Sensors Smartphones Support vector machines SVM Human activities SVM Forensics Decision tree Machine learning Classification
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Summary:	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.
ISSN:	2314-7172
DOI:	10.1186/s43067-024-00157-x