Quantitative Evaluation of Hemiplegic Gait Using Principal Component Analysis.
نویسندگان
چکیده
منابع مشابه
Human Gait Recognition using Silhouette Vector and Principal Component Analysis
Lot of research in the field of human recognition is being carried out. Gait recognition is a relatively new approach which is gaining momentum in biometrics. We have demonstrated a simple approach as a solution to this problem. We have taken a feature which was proposed earlier i. e. the Silhouette Vector. This is the distance of boundary points from the centroid of the silhouette as it rotate...
متن کاملFuzzy Principal Component Analysis based Gait Recognition
Gait recognition is a relatively new biometric identification technology for human identification. Gait recognition algorithm based on fuzzy principal component analysis (FPCA) for gait energy image(GEI) is proposed. Firstly, the original gait sequence is preprocessed and gait energy image is obtained. Secondly, the eigenvalues and eigenvectors are extracted by fuzzy principal component analysi...
متن کاملMotion estimation for gait rehabilitation of hemiplegic patients using principal components analysis
In gait rehabilitation of hemiplegic patients by means of a robotic orthosis, a major challenge resides in cooperative control. The patient should not simply be moved, but rather be assisted in his motions. Ideally, the controller should thus detect the patient's intention and actuate his paretic limbs coordinately. Recently, good results have been achieved with impedance control, which gives t...
متن کاملdemonstrating buried channels using principal component analysis
spectral decomposition of time series has a significant role in seismic data processing and interpretation. since the earth acts as a low-pass filter, it changes frequency content of passing seismic waves. conventional representing methods of signals in time domain and frequency domain cannot show time and frequency information simultaneously. time-frequency transforms upgraded spectral decompo...
متن کاملMultiresolution using principal component analysis
This paper proposes Principal Component Analysis (PCA) to find adaptive bases for multiresolution. An input image is decomposed into components (compressed images) which are uncorrelated and have maximum l2 energy. With only minor modification, a single layer linear network using the Generalized Hebbian Algorithm (GHA) is used for multiresolution PCA. The decomposition has been successfully app...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Japanese Journal of Rehabilitation Medicine
سال: 1998
ISSN: 1880-778X,0034-351X
DOI: 10.2490/jjrm1963.35.477