Project Team
Students
Aryan Sarin
Mechanical Engineering
Penn State Berks
Faculty Mentors
Joseph Mahoney
Penn State Berks
Kinesiology and Mechanical Engineering
Allison Altman-Singles
Penn State Berks
Kinesiology and Mechanical Engineering
Truong Xuan Tran
Penn State Harrisburg
Computer Science
Project
Project Video
Project Abstract
Running is a prominent activity enjoyed by many with a high impact loading on the feet, which could lead to running-related injuries. To avoid injuries, certain runners change their technique, and changing foot strike is a way to benefit a runner’s performance in the long run. Foot strike is the first form of contact that the foot has when striking the ground. There are three types of foot strike patterns (FSPs): Forefoot, Rearfoot, and Midfoot. These types of patterns show the part of the foot that is having first contact, such as the forefoot being the toes of the foot. Any applications and wearable devices help runners provide inputs such as their heart rate, distance, and pace. Providing precise FSPs could prevent running-related injuries. Groups are beginning to produce studies that use Machine Learning to help detect FSPs. This study develops an algorithm using data from a tibia-mounted uni-axial accelerometer from 58 runners. Using TensorFlow, a Neural Network (NN) and long short-term memory (LSTM) model is created, and a Keras tuner is used to create an optimal model based on the dataset. In every training trial, two test subjects are randomly removed for evaluation with a 101 times max voting loop that consists of randomly splitting training and validation for the model. This unique outlook achieved an average of 86% testing accuracy in the NN model and 66% in the LSTM model. The structure could result in an optimal model that could have a potentially great future application that may help many to avoid injuries.
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