期刊
SENSORS
卷 23, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/s23187802
关键词
human activity recognition; variational autoencoder; generative adversarial networks
This paper proposes a novel approach for real-time human activity recognition using a wearable IMU sensor. By utilizing pre-trained XGBoost and CVAE models as the classifier and generator, respectively, the system achieves an accuracy of 96.03% in activity classification.
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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