Abstract

Three dimensional (3D) ground reaction force (GRF) are an essential component for gait analysis. Current methods for measuring 3D GRF involve using a stationary force plate embedded in the ground, which captures the forces as subjects walk across the platform. This approach has several limitations, a few being: it can only capture a few steps at a time, it is expensive to purchase and maintain, it can't reflect forces caused by natural uneven surfaces, etc. Previous research has attempted to develop wearable force sensors to overcome these problems; however, these endeavors have resulted in devices that are expensive, bulky, and fail to accurately measure forces when compared to static force plates. This thesis presents the implementation and validation of novel nanocomposite piezoresponsive foam (NCPF) sensors for measuring 3D GRF. Four NCPF sensors were embedded in a shoe sole at four locations: heel, arch, ball, and toe. The signals from each sensor were used in a functional data analysis (FDA) to develop a statistical model for estimating 3D GRF. The process of calibrating the sensors to model GRF was validated through a study where 9 subjects (4 females, 5 males) walked on a force-sensing treadmill for two minutes. Two approaches were used to model the GRF response. The first approach was based on functional decomposition of the data. Using a tenfold cross validation process a statistical model was developed for each subject with the ability to predict walking 3D GRF with less than 7% error. The second approach used machine learning to model 3D GRF. Using the same walking data for the statistical models, an artificial neural network (ANN) was used to create subject-specific models that could predict walking 3D GRF with less than 11% error. The predictive capabilities of ANN were tested using a pilot study where a single subject performed a calibration procedure by running at seven different speeds for thirty seconds each on the force-sensing treadmill. This calibration data was used to train a model, which was then used to estimate vertical GRF (VGRF) for three additional running trials at randomly selected speeds from within the calibration range. The ANN model was able to predict VGRF for three running speeds after calibration with less than 4% error. The use of NCPF sensors to estimate 3D GRF was shown to be a viable alternative to static force plates. It is recommended, in future work, that 3D GRF and subsequent sensor data be collected from a large sample of subjects to create a baseline of 3D GRF characteristics for a population that will enable a robust cross-subject model capable of performing real-time ground reaction force analysis across the general population, which will greatly benefit our understanding of human gait.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2017-05-01

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd9263

Keywords

nanocomposite, ground reaction force (GRF), artificial neural network (ANN)

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