Enlighten Wearable Physiological Monitoring Systems: On-Body RF Characteristics Based Human Motion Classification Using a Support Vector Machine

TLDR

Real‑time health monitoring via body‑area networks can enhance firefighter safety and also serve as a motion detection system. The study aims to classify firefighter motions using features extracted from on‑body radio‑frequency channels. Classification performance was evaluated from three perspectives: overall results, motion selection, and sensor placement. The authors identified relevant RF features, trained an SVM that distinguishes seven firefighter motions with an average accuracy of 88.69 %, and demonstrated that a reduced feature set still yields acceptable performance.

Abstract

The real-time health monitoring system is a promising body area network application to enhance the safety of firefighters when they are working in harsh and dangerous environments. Other than monitoring the physiological status of the firefighters, on-body monitoring networks can be also regarded as a candidate solution of motion detection and classification. In this paper, we consider motion classification with features obtained from the on-body radio frequency (RF) channel. Various relevant RF features have been identified and a support vector machine (SVM) has been implemented to facilitate human motion classification. In particular, we distinguish the most frequently appearing human motions of firefighters including standing, walking, running, lying, crawling, climbing, and running upstairs with an average true classification rate of 88.69 percent. Classification performance has been analyzed from three different perspectives including typical classification results, effects of candidate human motions, and effects of on-body sensor locations. We prove that even a subset of available RF features provides an acceptable classification rate, which may result in less computational cost and easier implementation by using our proposed scheme.