Classification Strategies for Radar-Based Continuous Human Activity Recognition With Multiple Inputs and Multilabel Output

Abstract

Fall detection systems can play an important role in assuring safe independent living for vulnerable people. These sensors not only have to detect falls but also have to recognize uncritical, normal activities of daily living in order to differentiate them from falls. Radar sensors are very attractive for human activity recognition thanks to their contactless capabilities and lack of plain videos recorded. In this article, a novel approach to recognize single activities in a continuous stream of radar data is proposed, whereby the stream is divided into windows of fixed length and, then, multilabel classification is used to recognize all activities taking place in these time segments. While the initial feasibility of this approach was presented in an earlier contribution presented at the 2023 IEEE SENSORS conference, in this extended work, additional in-depth studies on critical parameters are performed. Specifically, multiple combinations of different radar data domains/representations (e.g., range-time maps, range-Doppler maps, and spectrograms) and different radar nodes in a network of five cooperating sensors are considered as inputs to two considered multilabel classification networks. In addition, a parametric study on the probability thresholds of the networks to assign labels to specific classes is also performed.