Human gait identity recognition system based on gait pal and pal entropy (GPPE) and distances features fusion

2017 ◽  
Author(s):  
Marwa E. Ehaimir ◽  
Islem Jarraya ◽  
Wael Ouarda ◽  
Adel M. Alimi
Keyword(s):  
Recognition System ◽  
Human Gait ◽  
Identity Recognition ◽  
Features Fusion

Video-based Indoor Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim
Keyword(s):  
Hidden Markov ◽  
Gait Recognition ◽  
Smart Homes ◽  
The Elderly ◽  
Recognition System ◽  
Home Healthcare ◽  
Human Gait ◽  
Depth Imaging ◽  
Smart System ◽  
Spatiotemporal Features

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.

Sensor positioning for a human activity recognition system using a double layer classifier

2021 ◽  
pp. 095441192110405
Author(s):  
Mohamed H Abdelhafiz ◽  
Mohammed I Awad ◽  
Ahmed Sadek ◽  
Farid Tolbah
Keyword(s):  
Activity Recognition ◽  
Double Layer ◽  
Prediction Accuracy ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Human Gait ◽  
System A ◽  
Election Method ◽  
Single Sensor ◽  
Feature Selector

This paper describes the development of a human gait activity recognition system. A multi-sensor recognition system, which has been developed for this purpose, was reduced to a single sensor-based recognition system. A sensor election method was devised based on the maximum relevance minimum redundancy feature selector to determine the sensor’s optimum position regarding activity recognition. The election method proved that the thigh has the highest contribution to recognize walking, stairs and ramp ascending, and descending activities. A recognition algorithm (which depends mainly on features that are classified by random forest, and selected by a combined feature selector using the maximum relevance minimum redundancy and genetic algorithm) has been modified to compensate the degradation that occurs in the prediction accuracy due to the reduction in the number of sensors. The first modification was implementing a double layer classifier in order to discriminate between the interfered activities. The second modification was adding physical features to the features dictionary used. These modifications succeeded to improve the prediction accuracy to allow a single sensor recognition system to behave in the same manner as a multi-sensor activity recognition system.

scholarly journals A Novel Human Gait Recognition System

2010 ◽  
pp. 1043-1047 ◽  
Author(s):  
Seyyed Meysam Hosseini ◽  
Abbas Nasrabadi ◽  
Peyman Nouri ◽  
Hasan Farsi
Keyword(s):  
Gait Recognition ◽  
Recognition System ◽  
Human Gait

Extraction Algorithm of Lip Characteristic Parameters Based on Interpolation

2014 ◽  
Vol 536-537 ◽  
pp. 235-240
Author(s):  
Ying Jie Meng ◽  
Li Xin Bai ◽  
Wen Jun Liu ◽  
Ming Wen Liu
Keyword(s):  
High Efficiency ◽  
Parameter Extraction ◽  
Characteristic Parameters ◽  
Motion Feature ◽  
Identity Recognition ◽  
Features Fusion ◽  
Parameters Extraction ◽  
Key Points ◽  
Extraction Algorithm ◽  
Motion Features

In the research of identity recognition based on lip motion features, there are limitations for the existing algorithms of lip characteristic parameters extraction. This paper uses the strategy of lip static/dynamic geometric features fusion, designs the lip feature parameter extraction program based on interpolation, and implements the major aspects of processing algorithm of the program. The solution is based on the speaker's key six primitives spelling lip sequence image, firstly generates the lip key point coordinates in the image, then based on Lagrange interpolation obtains function curve coefficient of upper and lower lips' key points , lastly the two curve coefficients are combined to form lip motion feature information of human speaker's some specific sounds; Simulation results show that the extraction of characteristic parameters of the program not only have a high efficiency and availability, but also have the advantages of good storage.

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