Classification of Lower Limb Activities Based on Discrete Wavelet Transform Using On-Body Creeping Wave Propagation

TL;DR

This study explores classifying lower limb activities by analyzing creeping wave propagation around the thigh using discrete wavelet transform and various classifiers, demonstrating effective activity recognition with safe antenna exposure levels.

Contribution

It introduces a novel method combining creeping wave propagation measurement with DWT and classifiers for accurate leg activity classification.

Findings

SVM with DWT outperforms other classifiers

SAR levels are within FCC safety standards

Effective classification of six different leg activities

Abstract

This article investigates how the creeping wave propagation around the human thigh could be used to monitor the leg movements. The propagation path around the human thigh gives information regarding leg motions that can be used for the classification of activities. The variation of the transmission coefficient is measured between two on-body polyethylene terephthalate (PET) flexible antennas for six different leg-based activities that exhibit unique time-varying signatures. A discrete wavelet transform (DWT) along with different classifiers, such as support vector machine (SVM), decision trees, naive Bayes, and K-nearest neighbors (KNN), is applied for feature extraction and classification to evaluate the efficiency for classifying different activity signals. Additional algorithms, such as dynamic time warping (DTW) and deep convolutional neural network (DCNN), have also been implemented, and in each case, SVM with DWT outperforms the others. Simulation to evaluate a specific absorption rate (SAR) is carried out as the antenna is positioned on the human thigh leaving no gap. The results show that the SAR is within the threshold as per the Federal Communications Commission (FCC) standard.