Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
College of Nursing, The University of Alabama, Tuscaloosa, AL 35487, USA.
Sensors (Basel). 2022 Apr 2;22(7):2743. doi: 10.3390/s22072743.
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.
本文提出了一种足底压力传感器系统（P2S2），该系统集成在鞋子的鞋垫中，用于检测包括行走、左右弯腰、向后拉车、下蹲、下降、上楼梯、跑步和跌倒（前、后、右、左）在内的十三种常用人体运动。六个力敏电阻（FSR）传感器被放置在鞋垫上的关键压力点上，以捕捉各种运动中压力变化的电信号特征。共有 34 名成年参与者接受了 P2S2 的测试。使用主成分分析（PCA）对压力数据进行了采集和处理，为多种机器学习（ML）算法输入，包括 k-NN、神经网络和支持向量机（SVM）算法。使用四折交叉验证对 ML 模型进行了训练。每个折叠将主体数据与其他折叠保持独立。该模型的准确率达到了 86%，证明了使用集成在鞋子中的 P2S2 预测人体运动的有效性，结果非常有前景。
