Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning

Wolfe Anderson, Zachary Choffin, Nathan Jeong, Michael Callihan, Seongcheol Jeong, Edward Sazonov

Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa


movement classification, machine learning, smart shoe, footwear sensor, human movement classification


This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping left and right, pulling a cart backward, squatting, descending, ascending stairs, running, and falling (front, back, right, left). Six force sensitive resistors (FSR) sensors were positioned on critical pressure points on the insoles to capture the electrical signature of pressure change in the various movements. A total of 34 adult participants were tested with the P2S2. The pressure data were collected and processed using a Principal Component Analysis (PCA) for input to the multiple machine learning (ML) algorithms, including k-NN, neural network and Support-Vector Machine (SVM) algorithms. The ML models were trained using four-fold cross-validation. Each fold kept subject data independent from other folds. The model proved effective with an accuracy of 86%, showing a promising result in predicting human movements using the P2S2 integrated in shoes.

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