Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

被引:27
|
作者
Dentamaro, Vincenzo [1 ]
Impedovo, Donato [1 ]
Pirlo, Giuseppe [1 ]
机构
[1] Univ Bari, Dept Comp Sci, I-70121 Bari, Italy
来源
IEEE ACCESS | 2020年 / 8卷
关键词
Diseases; Foot; Kinematics; Solid modeling; Cameras; Legged locomotion; Sensors; Pose-estimation; computer vision; computer aided diagnosis; gait analysis; machine learning; early neurodegenerative diseases assessment; kinematic theory of rapid human movements; sigma-lognormal; FEATURES; REPRESENTATION;
D O I
10.1109/ACCESS.2020.3032202
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patients life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1%; of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation.
引用
收藏
页码:193966 / 193980
页数:15
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