Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

Abstract

This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.

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“…This research [19] proposes an innovative approach to tackle the human activity recognition (HAR) challenge, employing hybrid learning models. Achieving a classification accuracy of 96.03%, the proposed system utilizes a pretrained XGbBoost model as the classifier, and integrates the Convolutional Variational Autoencoder (CVAE) model as the generator.…”