Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination.

ABSTRACT: Human activity recognition (HAR) performs a vital function in various fields, including healthcare, rehabilitation, elder care, and monitoring. Researchers are using mobile sensor data (i.e., accelerometer, gyroscope) by adapting various machine learning (ML) or deep learning (DL) networks. The advent of DL has enabled automatic high-level feature extraction, which has been effectively leveraged to optimize the performance of HAR systems. In addition, the application of deep-learning techniques has demonstrated success in sensor-based HAR across diverse domains. In this study, a novel methodology for HAR was introduced, which utilizes convolutional neural networks (CNNs). The proposed approach combines features from multiple convolutional stages to generate a more comprehensive feature rep

SUBMITTER: Akter M 

PROVIDER: S-EPMC10301803 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature