Paper Info
Title
Classification of gait anomalies from kinect.
Abstract
A persons manner of walking or their gait is an important feature in human recognition and classification tasks. Gait serves as an unobtrusive biometric modality which yields high quality results. In comparison with other biometric modalities, its main strength is its performance even in data that are captured at distance or at low resolution. In this paper, we present an algorithm for classification of gait disorders arising from neuro-degenerative diseases such as Parkinson and Hemiplegia. We focus on motion anomalies such as tremor, partial paralysis, gestural rigidity and postural instability. The analysis and classification of such motions are challenging since they consist of a multiplicity of subtle formations while lacking a regular pattern or major cycle. We introduce a gait representation which is invariant to the walking cycle and yields an efficient similarity metric. Our method performs on the joints’ motion trajectories of a 3D human skeleton captured by a Kinect sensor. The algorithm is robust, in that it does not require calibration, synchronization or a careful capturing setup. We demonstrate its efficiency by classifying different degenerative cases with high accuracy even in the presence of noise and low-resolution acquisition.
Year
DOI
Venue
2018
10.1007/s00371-016-1330-0
The Visual Computer
Keywords
Field
DocType
Gait recognition, Kinect, Geometry processing
Modalities,Rigidity (psychology),Computer vision,Synchronization,Gait,Computer science,Geometry processing,Human skeleton,Artificial intelligence,Invariant (mathematics),Biometrics
Journal
Volume
Issue
ISSN
34
2
1432-2315
Citations 
PageRank 
References 
3
0.42
23
Authors
7
Name
Order
Citations
PageRank
Qiannan Li130.42
Yafang Wang213413.56
Andrei Sharf3110849.93
Ya Cao430.42
Changhe Tu528834.47
Baoquan Chen62095111.30
Shengyuan Yu750.85