Detection of aberration in human behavior using shallow neural network over smartphone inertial sensors data

• Published in: Computational intelligence 2024-10, Vol.40 (5), p.n/a
• Main Authors: Sakshi, Bhatia, M. P. S., Chakraborty, Pinaki
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; CNN; Data analysis; Edge computing; feature extraction; HAR; Human activity recognition; Human motion; Inertial sensing devices; LSTM; Mobile computing; Monitoring; Motion sensors; Neural networks; Sensors; Sequences; shallow neural network model; Sliding; Smartphones; Surveillance; SVM
• ISSN: 0824-7935; 1467-8640
• DOI: 10.1111/coin.12699

The integration of different Mobile Edge Computing (MEC) applications has significantly enhanced the realm of security and surveillance, with Human Activity Recognition (HAR) standing out as a crucial application. The diverse sensors found in smartphones have made it convenient for monitoring applications to gather and analyze data, rendering them valuable for HAR purposes. Moreover, MEC can be employed to automate surveillance, allowing intelligent monitoring of restricted areas to identify and respond to unwanted or suspicious activities. This research develops a system using motion sensors in smartphones to identify unusual human activities. People's smartphones were employed to monitor both suspicious and regular activities. Information was collected for various actions categorized as either suspicious or regular. When a person performs a certain action, the smartphone records a series of sensory data, analyse important patterns from the basic data, and then determines what the person is doing by combining information from different sensors. To prepare the data, information from different sensors was aligned to a shared timeline. In this study, we used a sliding window approach on synchronized data to feed sequences into LSTM and CNN models. These models, which include initial layers of LSTM and CNN, automatically find important patterns in the order of human activities. We combined SVM with the features extracted by the shallow Neural Network to make a mixed model that predicts suspicious activities. Lastly, we compared LSTM, CNN, and our new shallow mixed neural network using a new real‐time dataset. The mixed model of CNN and SVM achieved an accuracy of 94.43%. Additionally, the sliding window method's effectiveness was confirmed with a 4.28% improvement in accuracy.