Classification of Gait Phases Based on Bilateral EMG Data Using Support Vector Machines

被引：0
|
作者：
Ziegler, Jakob [1 ]
Gattringer, Hubert [1 ]
Mueller, Andreas [1 ]
机构：
[1] Johannes Kepler Univ Linz, Inst Robot, A-4040 Linz, Austria
来源：
2018 7TH IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB2018) | 2018年
关键词：
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Robotic systems for rehabilitation of movement disorders and motion assistance are gaining increased attention. Robust classification of motion data as well as reliable recognition of the user's intended movement play a major role in order to maximize wearability and effectiveness of such systems. Biological signals like electromyography (EMG) provide a direct connection to the motion intention of the wearer. This paper addresses the classification of stance phase and swing phase during healthy human gait based on the muscle activity in both legs using the theory of Support Vector Machines (SVM). A novel EMG feature calculated from the bilateral EMG signals of muscle pairs is introduced. The presented method shows promising results with classification accuracies of up to 96%.
引用
收藏
页码：978 / 983
页数：6
相关论文
共 50 条
  • [1] Support vector machines for automated gait classification
    Begg, RK
    Palaniswami, M
    Owen, B
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2005, 52 (05) : 828 - 838
  • [2] Classification of gait phases by using SVM and ANN based on EMG signals
    Nazmi, Nurhazimah
    Yamamoto, Shin-Ichiroh
    Rohim, Muhammad Amirul Sunni
    Shair, Ezreen Farina
    2024 IEEE SYMPOSIUM ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, ISIEA 2024, 2024,
  • [3] Classification of fuzzy data based on the support vector machines
    Forghani, Yahya
    Yazdi, Hadi Sadoghi
    Effati, Sohrab
    EXPERT SYSTEMS, 2013, 30 (05) : 403 - 417
  • [4] Data classification using support vector machines with mixture kernels
    Wei, Liwei
    Wei, Chuanshen
    Wan, Xiaqing
    NANOTECHNOLOGY AND PRECISION ENGINEERING, PTS 1 AND 2, 2013, 662 : 936 - +
  • [5] Hyperspectral data classification using geostatistics and support vector machines
    Bahria, S.
    Essoussi, N.
    Limam, M.
    REMOTE SENSING LETTERS, 2011, 2 (02) : 99 - 106
  • [6] Classification of Remote Sensed Data Using Linear Kernel Based Support Vector Machines
    Rao, Tarun
    Rajasekhar, N.
    Rajinikanth, T. V.
    Sundar, K. S.
    2013 INTERNATIONAL CONFERENCE ON CONTROL COMMUNICATION AND COMPUTING (ICCC), 2013, : 22 - +
  • [7] Support vector machines for classification of hyperspectral data
    Gualtieri, JA
    Chettri, S
    IGARSS 2000: IEEE 2000 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOL I - VI, PROCEEDINGS, 2000, : 813 - 815
  • [8] Multi-class surface EMG classification using support vector machines and wavelet transform
    Liu, Han
    Huang, Yun-wei
    Liu, Ding
    2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2010, : 2963 - 2967
  • [9] Acoustic Gait Analysis Using Support Vector Machines
    Huang, Jasper
    Di Troia, Fabio
    Stamp, Mark
    ICISSP: PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY, 2018, : 545 - 552
  • [10] Aerial LiDAR data classification using Support Vector Machines (SVM)
    Lodha, Suresh K.
    Kreps, Edward J.
    Helmbold, David P.
    Fitzpatrick, Darren
    THIRD INTERNATIONAL SYMPOSIUM ON 3D DATA PROCESSING, VISUALIZATION, AND TRANSMISSION, PROCEEDINGS, 2007, : 567 - 574
12345