Enhancing FT-Transformer With a Matérn-Driven Kolmogorov-Arnold Feature Tokenizer for Tabular Data-Based In-Bed Posture Classification

In-bed posture classification plays a crucial role in health monitoring. In this paper, we explore in-bed posture classification using FT-Transformer, a model that employs 1D tabular inputs instead of the commonly used 2D pressure heatmaps. However, the Feature Tokenizer in FT-Transformer suffers fr...

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Bibliographic Details
Main Authors: Bing Zhou, Weiwei Chen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/11075767/
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