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Gait pattern recognition and control...

The University of Texas at San Antonio.

Gait pattern recognition and control using a neural network model and PSO method.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Gait pattern recognition and control using a neural network model and PSO method.
Author:	Trevino, Roseann.
Description:	64 p.
Notes:	Source: Masters Abstracts International, Volume: 47-06, page: 3721.
Notes:	Adviser: Chunjiang Qian.
Contained By:	Masters Abstracts International47-06.
Subject:	Engineering, Biomedical.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1467634
ISBN:	9781109298055

Gait pattern recognition and control using a neural network model and PSO method.
Trevino, Roseann.

Gait pattern recognition and control using a neural network model and PSO method. - 64 p.

Source: Masters Abstracts International, Volume: 47-06, page: 3721.

Thesis (M.S.)--The University of Texas at San Antonio, 2009.

In this thesis, we investigate the development of body models using artificial neural networks (ANN) with the gait data measured by the VICON motion capturing system. The models will then be used to develop controllers that maintain the body's center-of-mass (COM) during gait. More specifically, we first use leg and arm motion data to build the body model using an artificial neural network (ANN), which simulate a human's balance dynamics. Second, we develop an inverse control using the gait data that represents the person with an injured leg and feed into the model that generated the COM to analyze the COM of the person with an injured leg. Third, the Particle Swarm Optimization Method (PSO) is used to design a controller which finds the optimal motion for the affected or injured right leg in order to maintain body balance. The PSO was used to optimize the center-of-mass in this research based on the right leg position values which help maintain full body balance. Lastly, we show that the balance model we established can be used for gait pattern recognition and identification, which can help distinguish gait among individuals, thus forming a type of identification.

ISBN: 9781109298055Subjects--Topical Terms:

227004
Engineering, Biomedical.

Gait pattern recognition and control using a neural network model and PSO method.
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035 $a (UMI)AAI1467634
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100 1 $a Trevino, Roseann. $3 492976
245 1 0 $a Gait pattern recognition and control using a neural network model and PSO method.
300 $a 64 p.
500 $a Source: Masters Abstracts International, Volume: 47-06, page: 3721.
500 $a Adviser: Chunjiang Qian.
502 $a Thesis (M.S.)--The University of Texas at San Antonio, 2009.
520 $a In this thesis, we investigate the development of body models using artificial neural networks (ANN) with the gait data measured by the VICON motion capturing system. The models will then be used to develop controllers that maintain the body's center-of-mass (COM) during gait. More specifically, we first use leg and arm motion data to build the body model using an artificial neural network (ANN), which simulate a human's balance dynamics. Second, we develop an inverse control using the gait data that represents the person with an injured leg and feed into the model that generated the COM to analyze the COM of the person with an injured leg. Third, the Particle Swarm Optimization Method (PSO) is used to design a controller which finds the optimal motion for the affected or injured right leg in order to maintain body balance. The PSO was used to optimize the center-of-mass in this research based on the right leg position values which help maintain full body balance. Lastly, we show that the balance model we established can be used for gait pattern recognition and identification, which can help distinguish gait among individuals, thus forming a type of identification.
520 $a The significance of this research is geared toward physical rehabilitation; establishing a database of gait pattern for populations with medical ailments such as diabetes which will help enable precise diagnosis to help correct body motion. This research also helps in the development of a walking-aid device for those subject to lower extremity injuries.
590 $a School code: 1283.
650 4 $a Engineering, Biomedical. $3 227004
650 4 $a Engineering, Electronics and Electrical. $3 226981
690 $a 0541
690 $a 0544
710 2 $a The University of Texas at San Antonio. $b Electrical & Computer Engineering. $3 492977
773 0 $t Masters Abstracts International $g 47-06.
790 1 0 $a Qian, Chunjiang, $e advisor
790 1 0 $a Chen, Philip $e committee member
790 1 0 $a Frye, Michael $e committee member
790 $a 1283
791 $a M.S.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1467634