DOI

https://doi.org/10.25772/8NPJ-1A48

Defense Date

2016

Document Type

Thesis

Degree Name

Master of Science

Department

Engineering

First Advisor

Weijun Xiao, Ph.D

Second Advisor

Xuejun Wen. M.D, Ph.D.

Third Advisor

Peter E. Pidcoe, PT, DPT, Ph.D.

Fourth Advisor

Michael J. Cabral, Ph.D.

Abstract

Human footwear is not yet designed to optimally relieve pressure on the heel of the foot. Proper foot pressure assessment requires personal training and measurements by specialized machinery. This research aims to investigate and hypothesize about Preferred Transition Speed (PTS) and to classify the gait phase of explicit variances in walking patterns between different subjects. An in-shoe wearable pressure system using Android application was developed to investigate walking patterns and collect data on Activities of Daily Living (ADL). In-shoe circuitry used Flexi-Force A201 sensors placed at three major areas: heel contact, 1st metatarsal, and 5th metatarsal with a PIC16F688 microcontroller and Bluetooth module. This method provides a low-cost instantaneous solution to both wear and records plantar foot simultaneously. Data acquisition used internal local memory to store pressure logs for offline data analysis. Data processing used the perpendicular slope to determine peak pressure and time of index. Statistical analysis can utilize to discover foot deformity. The empirical results in one subject showed weak linearity between normal and fast walk and a significant difference in body weight acceptance between normal and slow walk. In addition, T-test hypothesis testing between two healthy subjects, with , illustrated a significant difference in their Initial Contact pressure and no difference between their peak-to-peak time interval. Preferred Transition Speed versus VGRF was measured in 19 subjects. The experiments demonstrated that vertical GRF averagely increased 18.46% when the speed changed from 50% to 75% of PTS with STD 4.78. While VGRF increased 21.24% when the speed changed from 75% to 100% of PTS with STD 7.81. Finally, logistic regression between 12 healthy subjects demonstrated a good classification with 82.6% accuracy between partial foot bearing and their normal walk.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

5-3-2016

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