Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Based on Discriminative Manifold Learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Manifold learning visualization of network traffic data
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
A vision-based analysis system for gait recognition in patients with Parkinson's disease
Expert Systems with Applications: An International Journal
Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Mobile Networks and Applications
Human recognition at a distance in video by integrating face profile and gait
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Gait Analysis Using a Shoe-Integrated Wireless Sensor System
IEEE Transactions on Information Technology in Biomedicine
A reliable and flexible data gathering protocol for battery limited wireless sensor networks
International Journal of Communication Networks and Distributed Systems
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Gait analysis of human plays a significant role in maintaining the well-being of our mobility and healthcare, and it can be used for various e-healthcare systems for fast medical prognosis and diagnosis. In this paper, we have developed a novel body sensor network-based recognition system to identify the specific gait pattern of Parkinson's disease (PD). Firstly, a BSN with 16-nodes is used to acquire the gait information from the PD patients. Then, an algorithm is developed based on local linear embedding (LLE) to extract and recognise the gait features. Experiments demonstrate the effectiveness of proposed scheme. The results show that the proposed scheme has a recognition rate of about 95.57% for gait patterns of PD, which is higher than the conventional PCA feature extraction method. The proposed system can identify PD patients from normal people and by their gait map with high reliability and appears a promising aid in the diagnosis of the Parkinson's disease.