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A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification. [PDF]
Sensors (Basel), 2021 Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an ...
Albuquerque P +3 more
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Stock Market Forecasting Based on Spatiotemporal Deep Learning [PDF]
Entropy, 2023 This study introduces the Spacetimeformer model, a novel approach for predicting stock prices, leveraging the Transformer architecture with a time–space mechanism to capture both spatial and temporal interactions among stocks. Traditional Long–Short Term
Yung-Chen Li +3 more
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ST-DeepGait: A Spatiotemporal Deep Learning Model for Human Gait Recognition. [PDF]
Sensors (Basel), 2022 Human gait analysis presents an opportunity to study complex spatiotemporal data transpiring as co-movement patterns of multiple moving objects (i.e., human joints). Such patterns are acknowledged as movement signatures specific to an individual, offering the possibility to identify each individual based on unique gait patterns.
Konz L, Hill A, Banaei-Kashani F.
europepmc +4 more sources
Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models [PDF]
Applied Sciences, 2021 The output of a deep-learning model delivers different predictions depending on the input of the deep learning model. In particular, the input characteristics might affect the output of a deep learning model.
Hyesook Son +5 more
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images [PDF]
SensorsRemote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, the number and variety of remote sensing satellites have increased, which provide abundant data for precise ...
Zilong Lian +5 more
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BIDL: a brain-inspired deep learning framework for spatiotemporal processing
Frontiers in Neuroscience, 2023 Brain-inspired deep spiking neural network (DSNN) which emulates the function of the biological brain provides an effective approach for event-stream spatiotemporal perception (STP), especially for dynamic vision sensor (DVS) signals. However, there is a
Zhenzhi Wu +9 more
doaj +3 more sources
Spatiotemporal Deep Learning for Power System Applications: A Survey
IEEE AccessUnderstanding spatiotemporal correlations in power systems is crucial for maintaining grid stability, reliability, and efficiency. By discerning connections between spatial and temporal dimensions, operators can anticipate and address issues such as ...
Mohsen Saffari, Mahdi Khodayar
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Spatiotemporal Model Based on Deep Learning for ENSO Forecasts [PDF]
Atmosphere, 2021 El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters. Here, we regard ENSO prediction as an unsupervised spatiotemporal prediction problem, and design a deep learning model called Dense ...
Huantong Geng, Tianlei Wang
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Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model
Remote Sensing, 2021 The integration analysis of multi-type geospatial information poses challenges to existing spatiotemporal data organization models and analysis models based on deep learning.
Daoye Zhu +5 more
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A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy
IEEE Transactions on Medical Imaging, 2021 Copyright 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ...
Leo Milecki +8 more
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