A computational theory for movement pattern recognition based on optimal movement pattern generation

1995 ◽  
Vol 73 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Yasuhiro Wada ◽  
Yasuharu Koike ◽  
Eric Vatikiotis-Bateson ◽  
Mitsuo Kawato
Keyword(s):  
Pattern Recognition ◽  
Movement Pattern ◽  
Pattern Generation ◽  
Computational Theory ◽  
Optimal Movement

A computational theory for movement pattern recognition based on optimal movement pattern generation

1995 ◽  
Vol 73 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Yasuhiro Wada ◽  
Yasuharu Koike ◽  
Eric Vatikiotis-Bateson ◽  
Mitsuo Kawato
Keyword(s):  
Pattern Recognition ◽  
Movement Pattern ◽  
Pattern Generation ◽  
Computational Theory ◽  
Optimal Movement

Neural Network for On-bed Movement Pattern Recognition

2018 ◽  
Author(s):  
Chawakorn Sri-ngernyuang ◽  
Prakarnkiat Youngkong ◽  
Duangruedee Lasuka ◽  
Kitti Thamrongaphichartkul ◽  
Watcharapong Pingmuang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Movement Pattern

A THEORY AND A MODEL FOR PATTERN GENERATION IN PLANTS

1993 ◽  
Vol 01 (02) ◽  
pp. 159-186 ◽  
Author(s):  
ROGER V. JEAN
Keyword(s):  
Mathematical Model ◽  
Pattern Recognition ◽  
Optimal Design ◽  
Pattern Generation ◽  
Growth Functions ◽  
Systemic Theory ◽  
Functional Aspects ◽  
Special Cases ◽  
L Systems ◽  
By Products

This article introduces a systemic theory of phyllotaxis (study of primordial patterns on plants) and updates a mathematical model which is central in the theory. The theory deals with the descriptive and the functional aspects of phyllotaxis, and studies the origins of patterns as well. The article concentrates on the formal aspects of the model and on its explanatory values. The model possesses biological foundations which will not be recalled here. It supposes a principle of optimal design and the representation of phyllotactic patterns with control hierarchies. These hierarchies can be generated with irreducible matrices and L-systems. In the hierarchies, parameters can be identified representing important characteristics of growth that is complexity, stability and rhythm. A formula linking those parameters allows us to calculate the numerical cost of each one of the phyllotactic patterns and to order the costs. The various types of patterns come out, including whorled patterns which are seen as special cases of spiral patterns. The model proposes predictions which can be compared to observations. It predicts the existence of improbable patterns which have been later identified and it possesses explanatory values which have been interestingly put to contribution in difficult problems of pattern recognition in botany. It also possesses mathematical by-products in the theory of growth functions of L-systems, thus related to Perron-Frobenius spectral theory.

Finger Movement Pattern Recognition from Surface EMG Signals Using Machine Learning Algorithms

ICTMI 2017 ◽  
2019 ◽  
pp. 75-89 ◽  
Author(s):  
Shravan Krishnan ◽  
Ravi Akash ◽  
Dilip Kumar ◽  
Rishab Jain ◽  
Karthik Murali Madhavan Rathai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Algorithms ◽  
Movement Pattern ◽  
Surface Emg ◽  
Machine Learning Algorithms ◽  
Finger Movement
Sign in / Sign up

Export Citation Format

Share Document