Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Enhanced Fisher Linear Discriminant Models for Face Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
The classification of human tremor signals using artificial neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A comparison of multiple classification methods for diagnosis of Parkinson disease
Expert Systems with Applications: An International Journal
International Journal of Communication Networks and Distributed Systems
A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
Expert Systems with Applications: An International Journal
Eigenspace-based fall detection and activity recognition from motion templates and machine learning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Monitoring mobility disorders at home using 3D visual sensors and mobile sensors
Proceedings of the 4th Conference on Wireless Health
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Recognition of specific Parkinsonian gait patterns is helpful in the diagnosis of Parkinson's disease (PD). However, there are few computer-aided methods to identify the specific gait patterns of PD. We propose a vision-based diagnostic system to aid in recognition of the gait patterns of Parkinson's disease. The proposed system utilizes an algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA). This scheme not only addresses the high data dimensionality problem during image processing but also distinguishes different gait categories simultaneously. The feasibility of the proposed system for the recognition of PD gait was tested by using gait videos of PD and normal subjects. The efficiency of feature extraction using PCA and LDA coefficients are also compared. Experimental results showed that LDA had a recognition rate for Parkinsonian gait of 95.49%, which is higher than the conventional PCA feature extraction method. The proposed system is a promising aid in identifying the gait of Parkinson's disease patients and can discriminate the gait patterns of PD patients and normal people with a very high classification rate.