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
相关论文
共 50 条
  • [41] PLASTIC ADAPTIVE RESPONSES OF GAIT THROUGH PREGNANCY - KINEMATIC ANALYSIS
    TAVES, C
    CHARTERIS, J
    JOURNAL OF BIOMECHANICS, 1979, 12 (08) : 633 - 633
  • [42] Early Diagnosis of Neurodegenerative Diseases by Handwritten Signature Analysis
    Pirlo, Giuseppe
    Diaz, Moises
    Angel Ferrer, Miguel
    Impedovo, Donato
    Occhionero, Fabrizio
    Zurlo, Urbano
    NEW TRENDS IN IMAGE ANALYSIS AND PROCESSING - ICIAP 2015 WORKSHOPS, 2015, 9281 : 290 - 297
  • [43] Influence of Head Movement on Human Gait through Biomechanical Kinematic Modelling
    Alkhatib, Rami
    Bassyouni, Zahraa
    Diab, Mohammed O.
    Sabbah, Maher
    2019 FIFTH INTERNATIONAL CONFERENCE ON ADVANCES IN BIOMEDICAL ENGINEERING (ICABME), 2019, : 48 - 51
  • [44] Training of On-line Handwriting Text Recognizers with Synthetic Text Generated Using the Kinematic Theory of Rapid Human Movements
    Martin-Albo, Daniel
    Plamondon, Rejean
    Vidal, Enrique
    2014 14TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR), 2014, : 543 - 548
  • [47] Gestures a Go Go: Authoring Synthetic Human-Like Stroke Gestures Using the Kinematic Theory of Rapid Movements
    Leiva, Luis A.
    Martin-Albo, Daniel
    Plamondon, Rejean
    ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2016, 7 (02)
  • [48] Vision-based motion capture for the gait analysis of neurodegenerative diseases: A review
    Vun, David Sing Yee
    Bowers, Robert
    Mcgarry, Anthony
    GAIT & POSTURE, 2024, 112 : 95 - 107
  • [49] Analysis of Gait Rhythm Fluctuations for Neurodegenerative Diseases by Phase Synchronization and Conditional Entropy
    Ren, Peng
    Zhao, Weihua
    Zhao, Zhiying
    Bringas-Vega, Maria L.
    Valdes-Sosa, Pedro A.
    Kendrick, Keith M.
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2016, 24 (02) : 291 - 299
  • [50] Gait Analysis and Detection of Human Pose Diseases
    Balti, Ala
    Ben Khelifa, Mohamed Moncef
    Ben Hassine, Slim
    Ouazaa, Hibet-Allah
    Abid, Saber
    Lakhoua, Mohamed Najeh
    Sayadi, Mounir
    2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22), 2022, : 1381 - 1386