scholarly journals Vitality Forms Analysis and Automatic Recognition

2021 ◽  
Author(s):  
Radoslaw Niewiadomski ◽  
Amrita Suresh ◽  
Alessandra Sciutti ◽  
Giuseppe DI Cesare
Keyword(s):  
Motion Capture ◽  
Automatic Classification ◽  
Automatic Recognition ◽  
Motion Capture Data ◽  
Spatiotemporal Characteristics ◽  
The Way

The form of an action, i.e. the way it is performed, conveys important information about the performer’s attitude. In this paper we investigate spatiotemporal characteristics of different gestures performed with specific vitality forms and we study whether it is possible to recognize these aspects of action automatically. As the first step, we created a new dataset of 7 gestures performed with a vitality form (gentle and rude) or without a vitality form (neutral, slow and fast). Thousand repetitions were collected from 2 professional actors. Next, we identified 22 features from the motion capture data. According to the results, vitality forms are not merely characterized by a velocity/acceleration modulation but by a combination of different spatiotemporal properties. We also perform automatic classification of vitality forms with F-score of 87.3%.

scholarly journals Vitality Forms Analysis and Automatic Recognition

2021 ◽  
Author(s):  
Radoslaw Niewiadomski ◽  
Amrita Suresh ◽  
Alessandra Sciutti ◽  
Giuseppe DI Cesare
Keyword(s):  
Motion Capture ◽  
Automatic Classification ◽  
Automatic Recognition ◽  
Motion Capture Data ◽  
Spatiotemporal Characteristics ◽  
The Way

The form of an action, i.e. the way it is performed, conveys important information about the performer’s attitude. In this paper we investigate spatiotemporal characteristics of different gestures performed with specific vitality forms and we study whether it is possible to recognize these aspects of action automatically. As the first step, we created a new dataset of 7 gestures performed with a vitality form (gentle and rude) or without a vitality form (neutral, slow and fast). Thousand repetitions were collected from 2 professional actors. Next, we identified 22 features from the motion capture data. According to the results, vitality forms are not merely characterized by a velocity/acceleration modulation but by a combination of different spatiotemporal properties. We also perform automatic classification of vitality forms with F-score of 87.3%.

Quaternion statistics applied to the classification of motion capture data

2021 ◽  
Vol 164 ◽  
pp. 113813
Author(s):  
Adam Świtoński ◽  
Henryk Josiński ◽  
Agnieszka Michalczuk ◽  
Konrad Wojciechowski
Keyword(s):  
Motion Capture ◽  
Motion Capture Data

Automatic classification of multi-word expressions in print dictionaries

2004 ◽  
Vol 26 (2) ◽  
pp. 187-202 ◽  
Author(s):  
Alexander Geyken ◽  
Jordan Boyd-Graber
Keyword(s):  
Automatic Classification ◽  
Automatic Recognition ◽  
Linguistic Resources

Summary This work demonstrates the assignment of multi-word expressions in print dictionaries to POS classes with minimal linguistic resources. In this application, 32,000 entries from the Wörterbuch der deutschen Idiomatik (H. Schemann 1993) were classified using an inductive description of POS sequences in conjunction with a Brill Tagger trained on manually tagged idiomatic entries. This process assigned categories to 86% of entries with 88% accuracy. This classification supplies a meaningful preprocessing step for further applications: the resulting POS-sequences for all idiomatic entries might be used for the automatic recognition of multi-word lexemes in unrestricted text.

Classification of Human Movements with Motion Capture Data in a Motor Rehabilitation Context

2021 ◽  
Author(s):  
Luis Guilherme Silva Rodrigues ◽  
Diego Dias ◽  
Marcelo de Paiva Guimaraes ◽  
Alexandre Fonseca Brandao ◽  
Leonardo Rocha ◽  
...  
Keyword(s):  
Motion Capture ◽  
Motion Capture Data ◽  
Motor Rehabilitation ◽  
Human Movements

Head movements in Finnish Sign Language on the basis of Motion Capture data

2015 ◽  
Vol 18 (1) ◽  
pp. 41-89 ◽  
Author(s):  
Anna Puupponen ◽  
Tuija Wainio ◽  
Birgitta Burger ◽  
Tommi Jantunen
Keyword(s):  
Sign Language ◽  
Motion Capture ◽  
Functional Properties ◽  
Head Movement ◽  
Head Movements ◽  
Motion Capture Data ◽  
Form And Function ◽  
Kinematic Characteristics ◽  
And Function

This paper reports a study of the forms and functions of head movements produced in the dimension of depth in Finnish Sign Language (FinSL). Specifically, the paper describes and analyzes the phonetic forms and prosodic, grammatical, communicative, and textual functions of nods, head thrusts, nodding, and head pulls occurring in FinSL data consisting of a continuous dialogue recorded with motion capture technology. The analysis yields a novel classification of the kinematic characteristics and functional properties of the four types of head movement. However, it also reveals that there is no perfect correspondence between form and function in the head movements investigated.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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