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Assisted Mobility Gait Training System

[+] Author Affiliations
Erik Shaw, Pablo Vasquez, Ryosuke Kondo, Kevin Ung, Zachary Farrer, Evan Fagerberg, Jack Baker, Bryan Yergeau, Matthew Harrison, James McCusker, Mansour Zenouzi

Wentworth Institute of Technology, Boston, MA

Paper No. IMECE2016-65635, pp. V003T04A007; 5 pages
doi:10.1115/IMECE2016-65635
From:
  • ASME 2016 International Mechanical Engineering Congress and Exposition
  • Volume 3: Biomedical and Biotechnology Engineering
  • Phoenix, Arizona, USA, November 11–17, 2016
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5053-4
  • Copyright © 2016 by ASME

abstract

Gait training is a rehabilitation process which helps patients improve their ability to walk or stand. Current gait training methods require patients to be in hospitals or rehabilitation facilities to acquire data on their recovery progress; there is no method of monitoring patient’s walking pattern continuously. Patients can fall into bad habits when they are not with their physician. Assisted Mobility Gait Training System is a redesigned walker that wirelessly provides data to patients and healthcare professionals throughout the rehab process. With continuous monitoring of data, patients can obtain live feedback about their walking pattern when they are outside a hospital setting.

Assisted Mobility Gait Trainer combines tele-medicine and out-patient monitoring to improve the gait rehabilitation process. Portability and ease of use allows the device to be used as an outpatient monitoring tool decreasing recovery time and healthcare visitations. Data acquisition and progress monitoring are achieved through load cells and a Microsoft Kinect 2 that collects data regarding the patient’s gait. Imaging arrays within the Microsoft Kinect 2, including an RGB camera, infrared emitter, and depth sensor, monitor limb trajectories. Angle of rotation of each joint is obtained through the use of blob detection and trigonometry, specifically a variation of the dot product. Use of the camera, load cells, and wheel encoder ensures there is minimal set up time, other than turning on the system. Four load cells in each leg measure the force applied to the gait trainer, which allows physicians to identify if the patient is utilizing one leg more than the other, as well as determining if the patient becomes less reliant on the walker over time. Gait speed and distance traveled during use is measured by a wheel encoder. Data collected is sent into cloud storage where it is processed and saved. Saved data is then electronically communicated to the healthcare professional and the patient in two separate user interfaces. Healthcare professionals are able to help patients gage their rehabilitation progress more efficiently. Patients benefit by receiving feedback regarding their gait while they are not at a rehab facility, which assists against patients falling into of bad habits during the rehabilitation process.

Copyright © 2016 by ASME

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