Plantar Pressure-Based Gait Recognition with and Without Carried Object by Convolutional Neural Network-Autoencoder Architecture

Convolutional neural networks (CNNs) have been widely and successfully demonstrated for closed set recognition in gait identification, but they still lack robustness in open set recognition for unknown classes. To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-...

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Veröffentlicht in:	Biomimetics (Basel, Switzerland) Jg. 10; H. 2; S. 79
Hauptverfasser:	Wu, Chin-Cheng, Tsai, Cheng-Wei, Wu, Fei-En, Chiang, Chi-Hsuan, Chiou, Jin-Chern
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.02.2025 MDPI
Schlagworte:	Accuracy convolutional neural network-autoencoder architecture Data collection Deep learning Experiments Feet Gait gait recognition Machine learning Neural networks Object recognition (Computers) Pattern recognition plantar pressure Pressure Pressure distribution Sensors Support vector machines Walking Taiwan gait recognition plantar pressure convolutional neural network-autoencoder architecture
ISSN:	2313-7673, 2313-7673
Online-Zugang:	Volltext
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