Evaluation Metrics for Continuous Human Activity Classification Using Distributed Radar Networks

Continuous Human Activity Recognition (HAR) in arbitrary directions is investigated using 5 spatially distributed pulsed Ultra-Wideband (UWB) radars. Such activities performed in arbitrary and unconstrained trajectories render a more natural occurrence of Activities of Daily Living (ADL) to be recognized. An innovative signal level fusion method was applied on the Range-Time (RT) maps, and deep learning classification via Recurrent Neural Networks (RNN) with and without bidi-rectionality was used on the computed micro-Doppler (μD) spectrogram. To assess classification performances, novel evaluation metrics accounting for the continuous nature of the sequence of activities and for imbalances in the dataset are proposed and compared with existing metrics. It is shown that conventional accuracy evaluation is too coarse, and that the proposed metrics need to be considered for a more comprehensive evaluation.