ENGS 88 Honors Thesis (AB Students)
Degree Program
B.E.
Year of Graduation
2020
Faculty Advisor
Douglas Van Citters, Ph.D., Ryan Chapman, Ph.D.
Document Type
Thesis (Senior Honors)
Publication Date
Spring 6-6-2020
Abstract
Wearable sensors were leveraged to develop two methods for computing hip joint angles and moments during walking and stair ascent that are more portable than the gold standard. The Insole-Standard (I-S) approach replaced force plates with force-measuring insoles and achieved results that match the curvature of results from similar studies. Peaks in I-S kinetic results are high due to error induced by applying the ground reaction force to the talus. The Wearable-ANN (W-A) approach combines wearables with artificial neural networks to compute the same results. Compared against the I-S, the W-A approach performs well (average rRMSE = 18%, R2 0.77).
Dartmouth Digital Commons Citation
McCabe, Megan V., "Utilizing Neural Networks and Wearables to Quantify Hip Joint Angles and Moments During Walking and Stair Ascent" (2020). ENGS 88 Honors Thesis (AB Students). 17.
https://digitalcommons.dartmouth.edu/engs88/17
Included in
Biomechanics and Biotransport Commons, Biomedical Devices and Instrumentation Commons, Data Science Commons
