Human Pose Inference Using an Elevated mmWave FMCW Radar

Abstract

Human monitoring using radar systems operating in the GHz regime has generated significant interest as a result of the increasing availability of commercial radar systems. These sensors offer all weather performance, the ability to measure range and velocity, and the protection of anonymity. However, visually inferring activities present in radar data is often challenging without prior knowledge. Here, we address this by implementing a radar-to-pose system that converts the raw radar data into human poses, such that human forms can be identified and activities monitored. In comparison to prior works, we place our radar in an elevated position, more in line with the placement of existing real world monitoring systems e.g. cameras, or emerging systems, e.g. quadcopters. We present an ensemble predictor network and apply it to a number of human poses of increasing complexity, reporting accuracies in excess of 90%, and verify the generalizable nature of our approach with unseen validation data. We perform an in depth explainability analysis, exploiting the unique mappings created by our radar placement and network structure to confirm that the network is making rational predictions based on the true location of limbs.

Keywords

• Convolutional neural network
• mmWave radar
• human pose detection
• explainable A.I
• FMCW