Quantification and classification of locomotion patterns by spatio-temporal morphable models

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

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Classification of spatio-temporal data represented as multiset for tactical profiling in badminton

2017 2nd International Conference on Image, Vision and Computing (ICIVC) ◽

10.1109/icivc.2017.7984701 ◽

2017 ◽

Author(s):

Kokum Weeratunga ◽

Anuja Dharmaratne ◽

Khoo Boon How

Keyword(s):

Temporal Data ◽

Spatio Temporal

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Supervised linear classification of Gaussian spatio-temporal data

Lietuvos matematikos rinkinys ◽

10.15388/lmr.2021.25214 ◽

2021 ◽

Vol 62 ◽

pp. 9-15

Author(s):

Marta Karaliutė ◽

Kęstutis Dučinskas

Keyword(s):

Time Moment ◽

Gaussian Random Field ◽

Covariance Structure ◽

Simulated Data ◽

Spatial Location ◽

Error Rates ◽

Prior Probabilities ◽

Structure Factors ◽

Spatio Temporal

In this article we focus on the problem of supervised classifying of the spatio-temporal Gaussian random field observation into one of two classes, specified by different mean parameters. The main distinctive feature of the proposed approach is allowing the class label to depend on spatial location as well as on time moment. It is assumed that the spatio-temporal covariance structure factors into a purely spatial component and a purely temporal component following AR(p) model. In numerical illustrations with simulated data, the influence of the values of spatial and temporal covariance parameters to the derived error rates for several prior probabilities models are studied.

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A Logistic Regression Based Framework for Spatio-Temporal Feature Representation and Classification of Single-Trial EEG

Communications in Computer and Information Science - Cognitive Systems and Signal Processing ◽

10.1007/978-981-16-2336-3_36 ◽

2021 ◽

pp. 387-394

Author(s):

Feifei Qi ◽

Wei Wu ◽

Ke Liu ◽

Tianyou Yu ◽

Yang Cao

Keyword(s):

Logistic Regression ◽

Feature Representation ◽

Single Trial ◽

Spatio Temporal ◽

Temporal Feature

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Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

Brain Sciences ◽

10.3390/brainsci9120348 ◽

2019 ◽

Vol 9 (12) ◽

pp. 348 ◽

Cited By ~ 7

Author(s):

Ji-Hoon Jeong ◽

Baek-Woon Yu ◽

Dae-Hyeok Lee ◽

Seong-Whan Lee

Keyword(s):

Short Term Memory ◽

Mental States ◽

Critical Issue ◽

High Accuracy ◽

Classification Performance ◽

Eeg Signals ◽

Aviation Accidents ◽

Spatio Temporal ◽

Lstm Network

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.

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Landscape Changes and Fragmentation Analysis in a Guinea Savannah Ecosystem: Case study of Talensi and Nabdam Districts of the Upper East region, Ghana

Journal of Geography and Geology ◽

10.5539/jgg.v8n1p41 ◽

2016 ◽

Vol 8 (1) ◽

pp. 41 ◽

Cited By ~ 1

Author(s):

Steve Ampofo ◽

Isaac Sackey ◽

Boateng Ampadu

Keyword(s):

Human Interaction ◽

Northern Ghana ◽

Landcover Change ◽

East Region ◽

Upper East Region ◽

Spatio Temporal ◽

Categorical Maps ◽

Landsat Satellite

Landcover change is an observed natural change dynamics at both the local and regional levels. However, its scales are exacerbated by human interaction with its natural environment. The study examines these spatio-temporal changes in landcover and the level to which the change is accompanied by fragmentation of the identifiable cover types in the Talensi and Nabdam districts in Northern Ghana. The research uses digital classification of Landsat satellite imagery for 1999 and 2007 to produce the cover types which results in good accuracy levels of 66.39% and 63.03% respectively. Fragmentation analysis of the landscape was computed using FRAGSTATS® software for categorical maps obtained from the classified landcover maps for the two years. All cover types increased marginally. However, Bare areas decreased by as much as 17.17% and that of water decreased from 3% to 1%. The changing landscape involving conversions within and among various cover types is accompanied by fragmentation in all classes but more pronounced in the Bare class. The Bare class type which has more patches corresponds to the class with increased cover size and rather strangely decreases in the mean path size.

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ON THE REPRESENTATION, LEARNING AND TRANSFER OF SPATIO-TEMPORAL MOVEMENT CHARACTERISTICS

International Journal of Humanoid Robotics ◽

10.1142/s0219843604000320 ◽

2004 ◽

Vol 01 (04) ◽

pp. 613-636 ◽

Cited By ~ 33

Author(s):

WINFRIED ILG ◽

GÖKHAN H. BAKIR ◽

JOHANNES MEZGER ◽

MARTIN A. GIESE

Keyword(s):

Representation Learning ◽

Movement Sequences ◽

Movement Trajectories ◽

Movement Characteristics ◽

Human Movements ◽

Complex Sequences ◽

Movement Representation ◽

Spatio Temporal ◽

Morphable Models ◽

Complex Movement

In this paper we present a learning-based approach for the modeling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMs) we derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modeling and synthesis of complex sequences of human movements that contain movement elements with a variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics.

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A taxonomy of optical dissipative structures in whispering-gallery mode resonators with Kerr nonlinearity

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2017.0381 ◽

2018 ◽

Vol 376 (2124) ◽

pp. 20170381 ◽

Cited By ~ 6

Author(s):

Irina V. Balakireva ◽

Yanne K. Chembo

Keyword(s):

Laser Field ◽

Continuous Wave ◽

Kerr Nonlinearity ◽

Whispering Gallery Mode ◽

Dissipative Structures ◽

Basins Of Attraction ◽

Theme Issue ◽

Whispering Gallery ◽

Spatio Temporal

In this paper, the research related to the formation of optical dissipative structures in Kerr-nonlinear whispering-gallery mode resonators pumped with continuous-wave lasers is reviewed. Pattern formation in these systems can be analysed using the paradigmatic Lugiato–Lefever model, which is a partial differential equation ruling the dynamics of the intra-cavity laser field. Various dissipative structures such as Turing rolls, solitons, breathers and spatio-temporal chaos can emerge in the resonator depending on the laser power and frequency. The bifurcation analysis enables a classification of these patterns, and also permits identification of their basins of attraction. This article is part of the theme issue ‘Dissipative structures in matter out of equilibrium: from chemistry, photonics and biology (part 1)’.

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