Sensors, Signal and Image Processing in Biomedicine and Assisted Living

In this context digital signal and image processing methods play an important role in feature or quality enhancement, and compression of the acquired, transmitted, or received biomedical signals and images. Novel contributions of that work include the development of a robust hierarchical multiple-ta...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 20; H. 18; S. 5071
1. Verfasser: Iakovidis, Dimitris K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 07.09.2020
MDPI
Schlagworte:
Algorithms
Biosensing Techniques
Brain cancer
Classification
Endoscopy
Heart rate
Image Processing, Computer-Assisted
Machine learning
Medical imaging
Methods
Monitoring, Physiologic
n/a
Neural networks
Sensors
Signal Processing, Computer-Assisted
Sleep
Strain gauges
Wavelet transforms
Wheelchairs
ISSN:1424-8220, 1424-8220
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Zusammenfassung:In this context digital signal and image processing methods play an important role in feature or quality enhancement, and compression of the acquired, transmitted, or received biomedical signals and images. Novel contributions of that work include the development of a robust hierarchical multiple-target pose tracking method in uncontrolled environments in the presence of multiple human actors; the introduction of an explicit body movement representation, called pose evolution, that can be used to complement appearance and motion cues for action recognition; and, a target-specific action classification architecture applied on video recordings of patients with PD. The experimental evaluation of the proposed method indicates that it can provide accurate target-specific classification of activities in the presence of other human actors, robust to changing environments. [...]EEG can provide information not only about motor activities that actually happen, but also about motor activities that are imagined by a human subject. The proposed system goes beyond the state-of-the-art by following a novel uncertainty-aware approach to obstacle detection, incorporating salient regions generated using a Generative Adversarial Network (GAN) trained to estimate saliency maps based on human eye-fixations.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20185071