Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

TLDR

Human Activity Recognition (HAR) is increasingly studied for applications such as biometric identification, health monitoring, and surveillance, driven by widespread wearable sensors and IoT, with deep learning as the dominant approach, though challenges remain for using human behaviors as biometric traits. The study proposes a novel multi‑class wearable user‑identification framework that leverages deep‑learning recognition of human behavior. The framework uses tri‑axial gyroscope and accelerometer data from wearable devices processed by deep‑learning models (CNN and LSTM) to recognize human behavior for user identification. Experiments show the framework achieves 91.77 % accuracy with a CNN and 92.43 % with an LSTM, demonstrating acceptable performance for biometric user identification.

Abstract

Currently, a significant amount of interest is focused on research in the field of Human Activity Recognition (HAR) as a result of the wide variety of its practical uses in real-world applications, such as biometric user identification, health monitoring of the elderly, and surveillance by authorities. The widespread use of wearable sensor devices and the Internet of Things (IoT) has led the topic of HAR to become a significant subject in areas of mobile and ubiquitous computing. In recent years, the most widely-used inference and problem-solving approach in the HAR system has been deep learning. Nevertheless, major challenges exist with regard to the application of HAR for problems in biometric user identification in which various human behaviors can be regarded as types of biometric qualities and used for identifying people. In this research study, a novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented. In order to obtain advanced information regarding users during the performance of various activities, sensory data from tri-axial gyroscopes and tri-axial accelerometers of the wearable devices are applied. Additionally, a set of experiments were shown to validate this work, and the proposed framework’s effectiveness was demonstrated. The results for the two basic models, namely, the Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) deep learning, showed that the highest accuracy for all users was 91.77% and 92.43%, respectively. With regard to the biometric user identification, these are both acceptable levels.