Variable Duration Motion Texture for Human Motion Modeling

2006 ◽  
pp. 603-612
Author(s):  
Tianyu Huang ◽  
Fengxia Li ◽  
Shouyi Zhan ◽  
Jianyuan Min
Keyword(s):  
Human Motion ◽  
Motion Modeling ◽  
Variable Duration

Statistics for Digital Human Motion Modeling in Ergonomics

Technometrics ◽  
2007 ◽  
Vol 49 (3) ◽  
pp. 277-290 ◽  
Author(s):  
Julian Faraway ◽  
Matthew P Reed
Keyword(s):  
Human Motion ◽  
Motion Modeling ◽  
Digital Human

Learning Manifolds

2012 ◽  
pp. 374-402
Author(s):  
Diana Mateus ◽  
Christian Wachinger ◽  
Selen Atasoy ◽  
Loren Schwarz ◽  
Nassir Navab
Keyword(s):  
Manifold Learning ◽  
Domain Knowledge ◽  
Dimensional Space ◽  
Human Motion ◽  
Motion Modeling ◽  
Learning Methods ◽  
Data Representations ◽  
Non Linear ◽  
Data Points ◽  
Low Dimensional

Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-points, manifold learning algorithms first approximate the low dimensional manifold where data lives with a graph; then, they find a non-linear map to embed this graph into a low dimensional space. Since the explicit pairwise relations and the neighborhood system can be designed according to the application, manifold learning methods are very flexible and allow easy incorporation of domain knowledge. The authors describe different assumptions and design elements that are crucial to building successful low dimensional data representations with manifold learning for a variety of applications. In particular, they discuss examples for visualization, clustering, classification, registration, and human-motion modeling.

Artificial Intelligence in Psychomotor Learning: Modeling Human Motion from Inertial Sensor Data

2019 ◽  
Vol 28 (04) ◽  
pp. 1940006 ◽  
Author(s):  
Olga C. Santos
Keyword(s):  
Artificial Intelligence ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Musical Instrument ◽  
Human Motion ◽  
Learning Performance ◽  
Sensor Data ◽  
Psychomotor Skills ◽  
Motion Modeling ◽  
Psychomotor Learning

Recent trends in educational technology focus on designing systems that can support students while learning complex psychomotor skills, such as those required when practicing sports and martial arts, dancing or playing a musical instrument. In this context, artificial intelligence can be key to personalize the development of these psychomotor skills by enabling the provision of effective feedback when the instructor is not present, or scaling up to a larger pool of students the feedback that an instructor would typically provide one-on-one. This paper presents the modeling of human motion gathered with inertial sensors aimed to offer a personalized support to students when learning complex psychomotor skills. In particular, when comparing learner data with those of an expert during the psychomotor learning process, artificial intelligence algorithms can allow to: (i) recognize specific motion learning units and (ii) assess learning performance in a motion unit. However, it seems that this field is still emerging, since when reviewed systematically, search results hardly included the motion modeling with artificial intelligence techniques of complex human activities measured with inertial sensors.

Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis

2016 ◽  
Vol 46 (3) ◽  
pp. 325-339 ◽  
Author(s):  
Jonathan Feng-Shun Lin ◽  
Michelle Karg ◽  
Dana Kulic
Keyword(s):  
Human Motion ◽  
Motion Modeling
Sign in / Sign up

Export Citation Format

Share Document