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

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

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%.

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.

Correlational Methods for Analysis of Dance Movements

2011 ◽  
Vol 29 (supplement) ◽  
pp. 283-304 ◽  
Author(s):  
Timothy R. Brick ◽  
Steven M. Boker
Keyword(s):  
Motion Capture ◽  
Mirror Symmetry ◽  
Cross Correlation ◽  
The Other ◽  
Free Form ◽  
Motion Capture Data ◽  
Complex Interactions ◽  
Dance Movements ◽  
Time Lagged ◽  
Insight Into

Among the qualities that distinguish dance from other types of human behavior and interaction are the creation and breaking of synchrony and symmetry. The combination of symmetry and synchrony can provide complex interactions. For example, two dancers might make very different movements, slowing each time the other sped up: a mirror symmetry of velocity. Examining patterns of synchrony and symmetry can provide insight into both the artistic nature of the dance, and the nature of the perceptions and responses of the dancers. However, such complex symmetries are often difficult to quantify. This paper presents three methods – Generalized Local Linear Approximation, Time-lagged Autocorrelation, and Windowed Cross-correlation – for the exploration of symmetry and synchrony in motion-capture data as is it applied to dance and illustrate these with examples from a study of free-form dance. Combined, these techniques provide powerful tools for the examination of the structure of symmetry and synchrony in dance.

Perceptually motivated LSPIHT for motion capture data compression

2015 ◽  
Vol 51 ◽  
pp. 1-7 ◽  
Author(s):  
Irene Cheng ◽  
Amirhossein Firouzmanesh ◽  
Anup Basu
Keyword(s):  
Data Compression ◽  
Motion Capture ◽  
Motion Capture Data
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