Hand activity classification based on perturbed nearfield radiation and augmented impedance of a wearable textile antenna

Abstract

Diagnosis of mobility disorder has become increasingly important in recent years. This paper investigates the feasibility of classifying six hand activities including two crucially Tremored and Non-Tremored motions, based on perturbed near-field radiation of the single body worn textile (BWT) antenna. The deployed antenna shows excellent biocompatibility and less vulnerability to electromagnetic absorption. Time-domain conversion of activities set using rational function approximation is investigated. Window cropping technique is exploited for data augmentation. Seven young and one elderly volunteer were used to assess the system’s performance. The measurements were performed in a chamber with an anechoic ceiling and negligible electromagnetic emanation. The Discrete Wavelet Transform (DWT) is adopted to extract the time domain features based on the fluctuation of impedance along with sliding window. Support Vector Machine (SVM) is utilized for classification. Two more algorithms namely k-Nearest Neighbor (k-NN) and Naive Bayes (NB) also been applied to test the accuracy of the classification and it is found that SVM produces best outcome in every cases under investigation. The performance of the different participants for the wrist mount and shoulder mount are tested individually. The wrist mount performs quite well, except for the elderly subject, which obtains low accuracy in both scenarios but the shoulder wins by a margin of 0.3%. Clear distinction between the results of tremored and non-tremored activities is seen. The combined augmented data for wrist and shoulder mount is observed to be 2.1% and 1.2% higher than the non-augmented data set, respectively.