Human motion classification based on a textile integrated and wearable sensor array

A system for classification of motion patterns is presented based on a non-contact magnetic induction monitoring device. This device is textile integrated, wearable, and able to measure pulse and respiratory activity. The proposed classifiers are a neural network, support vector machine, and a decision tree algorithm generated by bootstrap aggregating. Their performance is compared using a data set comprising five different types of motion patterns. In addition, the dependence of the misclassification error on the input sample length is investigated. The features used for classification were based on information derived by discrete wavelet transform and on lower and higher order statistical measures. With the presented magnetic induction device, all tested classifiers were able to classify the defined motion pattern with an accuracy of over 93%. The proposed bootstrap aggregating decision tree algorithm produces the best classification performance (accuracy of 96%). The support vector machine classifier shows the least dependence on the sample length. (paper)