User recognition in participatory sensing systems using deep learning based on spectro-temporal representation of accelerometer signals

Abstract

Identification of the actual sensor data provider is important in smartphone-based participatory sensing systems. More specifically, the participatory sensing systems must be able to identify the actual users from smartphone-embedded sensors’ data in a fast, silent, and unobtrusive manner, ensuring that appropriate privacy protection and authentication are provided automatically. In this context, this paper investigates smartphone-equipped accelerometer signal data to determine a person’s distinctive gait, allowing different providers to be recognized. The problem of user identification is formulated as an image classification task by changing the accelerometer signal into a 2D spectrotemporal image representation by calculating Short-Term Fourier Transform (STFT) of the signal. A deep learning based custom CNN model is proposed and evaluated on real-world benchmark dataset (containing 60 million accelerometer data samples of 387 users) for user identification. The efficacy of the proposed deep learning based approach is verified by comparing its performance with several baseline methods including Multi-layer Perceptron, K-Nearest Neighbours, Logistic Regression, Random Forest, and Decision Tree. The results of the experiments confirm that the proposed technique yields better performance (accuracy = 0.98, precision = 0.96, recall = 0.98, F1-score = 0.96, AUC = .9995) with respect to the baselines. Furthermore, the proposed model outperforms other CNN architectures (e.g., Lenet-5, AlexNet, and ZFNet) across all performance metrics.