Ankle Fatigue Classification Using Support Vector Machines

Details

• Personal Author:
  Lockhart TE ; Soangra R ; Zhang J
• Description:
  Fall accidents are a significant problem for the elderly, in terms of both human suffering and economic losses. Localized muscle fatigue is a potential risk factor for slip-induced falls as muscle fatigue adversely affects proprioception, movement coordination and muscle reaction times leading to postural instability and gait changes. Specifically, fatigue in ankle is associated with decline in postural stability, motor performance and fall accidents in human subjects. Automated recognition of ankle fatigue condition may be advantageous in early detection of fall and injury risks. In this study, we explore the classification potential of support vector machines (SVM) in recognizing gait patterns associated with ankle fatigue utilizing an inertial measurement unit (IMU) as the wearable technology has the potential to investigate continuous kinematic changes evoked by fatigue. The SVM is considered a powerful technique for general data classification and has been widely used to classify human motion patterns with good results. The advantage of SVM algorithm is that it can generate a classification result with limited data sets by minimizing both structural and empirical risks. Although numerous studies have been devoted to improving the SVM algorithms, little work has been performed to assess the robustness of SVM algorithms associated with movement variations and fatigue states. In the current study, we aim to monitor kinematics of walking in unconstrained environments using an IMU situated around the trunk Center-of-Mass (COM) during ankle fatigue and no-fatigue walking conditions. We hypothesize that ankle fatigue will influence walking behavior and this subtle changes in gait can be classified by supervised machine learning techniques such as support vector machines. [Description provided by NIOSH]
• Subjects:
  Accidental Falls Accidents Aging Biomechanical Phenomena Musculoskeletal System Occupational Health Safety
• Keywords:
  Age Groups Biomechanics Falls Muscle Function Posture Wearable Technologies
• Publisher:
  American Society of Biomechanics
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Abstract or Summary
• Place as Subject:
  OSHA Region 3 ; Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• NIOSHTIC Number:
  nn:20055475
• Citation:
  Proceedings of the 37th Annual Meeting of the American Society of Biomechanics, September 4-7, 2013, Omaha, Nebraska. Newark, DE: American Society of Biomechanics, 2013 Sep; :365
• Email:
  zhangj@vt.edu
• Federal Fiscal Year:
  2013
• NORA Priority Area:
  Services ; Construction
• Performing Organization:
  Virginia Polytechnic Institute and State University
• Peer Reviewed:
  False
• Start Date:
  20090901
• Source Full Name:
  Proceedings of the 37th Annual Meeting of the American Society of Biomechanics, September 4-7, 2013, Omaha, Nebraska
• End Date:
  20140831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:36e553b5c2e70abaf5c4370a826bfc47b1ef20da5d8ac2389e4ab7995a712234f6da07bd787c89bce6b359fef53b8d0ecba98126243fdc35980a266bcef83346
• Download URL:
  https://stacks.cdc.gov/view/cdc/213313/cdc_213313_DS1.pdf
• File Type:
  [PDF - 574.63 KB ]