A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network

Details

• Personal Author:
  Fey NP ; Gabert L ; Lenzi T ; Liu H ; Mendez J ; Murray R
• Description:
  Lower-limb powered prostheses can provide users with volitional control of ambulation. To accomplish this goal, they require a sensing modality that reliably interprets user intention to move. Surface electromyography (EMG) has been previously proposed to measure muscle excitation and provide volitional control to upper- and lower-limb powered prosthesis users. Unfortunately, EMG suffers from a low signal to noise ratio and crosstalk between neighboring muscles, often limiting the performance of EMG-based controllers. Ultrasound has been shown to have better resolution and specificity than surface EMG. However, this technology has yet to be integrated into lower-limb prostheses. Here we show that A-mode ultrasound sensing can reliably predict the prosthesis walking kinematics of individuals with a transfemoral amputation. Ultrasound features from the residual limb of 9 transfemoral amputee subjects were recorded with A-mode ultrasound during walking with their passive prosthesis. The ultrasound features were mapped to joint kinematics through a regression neural network. Testing of the trained model against untrained kinematics show accurate predictions of knee position, knee velocity, ankle position, and ankle velocity, with a normalized RMSE of 9.0 +/- 3.1%, 7.3 +/- 1.6%, 8.3 +/- 2.3%, and 10.0 +/- 2.5% respectively. This ultrasound-based prediction suggests that A-mode ultrasound is a viable sensing technology for recognizing user intent. This study is the first necessary step towards implementation of volitional prosthesis controller based on A-mode ultrasound for individuals with transfemoral amputation. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Occupational Health Safety
• Keywords:
  Author Keywords: A-Mode Ultrasound Biomechanical Engineering Biomechanics Electromyography Intent Recognition Joint Kinematics Prediction Lower-limb Prosthesis Lower Extremities Prosthetic Devices Robotics Transfemoral Amputation Ultrasound

  More +
• ISSN:
  1534-4320
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 8 ; Utah
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  31
• NIOSHTIC Number:
  nn:20067731
• Citation:
  IEEE Trans Neural Syst Rehabil Eng 2023 Feb; 31:1511-1520
• Contact Point Address:
  Joel Mendez, Utah Robotics Center, Department of Mechanical Engineering, The University of Utah, Salt Lake City, UT 84112 USA
• Email:
  joel.mendez@utah.edu
• Federal Fiscal Year:
  2023
• Performing Organization:
  University of Utah
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  IEEE Transactions on Neural Systems and Rehabilitation Engineering
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:19c089dc4321df6ddd4828eafd7aff816100166c17f4b315b381e96f5efec836ab871f004aa6433b102606b098614eeb1cdccdd3bd26f1a86f836e0345bd5401
• Download URL:
  https://stacks.cdc.gov/view/cdc/229993/cdc_229993_DS1.pdf
• File Type:
  [PDF - 15.11 MB ]