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Gait-Monitoring Wearable Technology for Transtibial Prosthetics

[+] Author Affiliations
Kolby V. Hebert, Rachel S. Keen, Derek R. King, Sally F. Shady

Wentworth Institute of Technology, Boston, MA

Paper No. IMECE2016-66226, pp. V003T04A012; 9 pages
doi:10.1115/IMECE2016-66226
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

Approximately 40,000 transtibial amputations occur each year in the United States. Current lower leg prosthetic options range from passive artificial limbs to computerized electronic models [1]. Because of insurance limitations, most patients use less sophisticated prosthetics. The average cost of lower leg prosthetics and corresponding medical care for single-leg veterans is at least $1.4 million due to increased rehabilitation times [1]. Gait training methods for transtibial amputees include extended rehabilitation processes lasting up to 9 months. These exercises provide no empirical data to analyze patient gait progress.

The device design is a wearable technology that acquires gait information that is evidentiary for physicians when deciding to continue or dismiss further rehabilitation and follow up medical appointments. The technology includes a gyroscope, accelerometer, microprocessor, and electronic components housed in a 3D printed casing that is attachable to any prosthetic, or a biological leg. Pressure sensors are embedded into a sock-like foot covering that is used in tandem with the other electronics.

Gait data collection was validated by comparing gait parameter values with literature values. A series of control tests on non-amputees was conducted in order to gather standard data and develop consistent testing practices for the prototype design. These findings are used as a reference when evaluating amputee gait data against non-amputee gait data.

As the microprocessor collects data, information is stored onto a memory card used to relay data to the developed program for data analysis. Data analysis is supported by a graphical user interface via LabView which provides valuable gait data to physicians and physical therapists. Gait data analysis is expected to result in asymmetrical patterns for below-the-knee amputees compared to non-amputees as well as abnormal pressure loads throughout the foot [1].

Copyright © 2016 by ASME

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