Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition

Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition

The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals, the Transformer model does not effectively utilize...

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Bibliographic Details
Main Authors: Xin Guo, Young Kim, Xueli Ning, Se Dong Min
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Sensors
Subjects:
human activity recognition
inertial measurement units (IMUs)
transformer model
relative position embedding
convolutional neural networks (CNNs)
time series signal
Online Access:https://www.mdpi.com/1424-8220/25/2/301
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