Human Behavior Learning in Joint Space Using Dynamic Time Warping and Neural Networks

2019 ◽  
Author(s):  
Jorge Ramirez ◽  
Wen Yu
Keyword(s):  
Neural Networks ◽  
Human Behavior ◽  
Dynamic Time Warping ◽  
Joint Space ◽  
Time Warping ◽  
Behavior Learning ◽  
Dynamic Time

scholarly journals Surgical data processing for smart intraoperative assistance systems

2017 ◽  
Vol 2 (3) ◽  
pp. 145-152 ◽  
Author(s):  
Ralf Stauder ◽  
Daniel Ostler ◽  
Thomas Vogel ◽  
Dirk Wilhelm ◽  
Sebastian Koller ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dynamic Time Warping ◽  
Data Science ◽  
Markov Models ◽  
Context Aware ◽  
Time Warping ◽  
Surgical Assistance ◽  
Surgical Data ◽  
Assistance Systems ◽  
Dynamic Time

AbstractDifferent components of the newly defined field of surgical data science have been under research at our groups for more than a decade now. In this paper, we describe our sensor-driven approaches to workflow recognition without the need for explicit models, and our current aim is to apply this knowledge to enable context-aware surgical assistance systems, such as a unified surgical display and robotic assistance systems. The methods we evaluated over time include dynamic time warping, hidden Markov models, random forests, and recently deep neural networks, specifically convolutional neural networks.

HUMAN-LIKE DYNAMIC PROGRAMMING NEURAL NETWORKS FOR DYNAMIC TIME WARPING SPEECH RECOGNITION

1995 ◽  
Vol 06 (01) ◽  
pp. 79-89 ◽  
Author(s):  
CHINCHUAN CHIU ◽  
MICHAEL A. SHANBLATT
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Speech Recognition ◽  
Dynamic Time Warping ◽  
Hardware Implementation ◽  
Time Warping ◽  
Network Method ◽  
The Neural Networks ◽  
Dynamic Time ◽  
Better Than

This paper presents a human-like dynamic programming neural network method for speech recognition using dynamic time warping. The networks are configured, much like human’s, such that the minimum states of the network’s energy function represent the near-best correlation between test and reference patterns. The dynamics and properties of the neural networks are analytically explained. Simulations for classifying speaker-dependent isolated words, consisting of 0 to 9 and A to Z, show that the method is better than conventional methods. The hardware implementation of this method is also presented.

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