In this study, the problem of multipath in radar sensor networks for human activity recognition (HAR) has been examined. Traditionally considered as a source of additional clutter, the multipath is being investigated for its potential to be exploited through the creation of virtual radar nodes. These virtual nodes are conceptualized to observe targets from aspect angles that differ from those of physically existing radars. To realize this idea, an innovative processing pipeline is proposed that extracts information from multipath signals to improve HAR. The pipeline isolates and tracks the line-of-sight (LOS) and multipath components of a moving human target performing continuous sequences of activities observed by a network of three radar sensors. Furthermore, the method has been verified with experimental data consisting of six activities and 14 volunteers by comparing classification metrics with the use of a single radar as well as only the LOS components of the three radars in the network. A 12-layer convolutional neural network (CNN) classifier has been designed to operate on range-Doppler (RD) images derived from the LOS and multipath components, extracted by the proposed method. A substantial performance improvement using the leave-one-person-out (L1Po) test set is demonstrated in the order of +11% by exploiting a multiradar network with its LOS and multipath components.

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Due to numerous benefits, radar is considered as an important sensor for human activity classification. The problem of classifying continuous sequences of activities of unconstrained duration has been studied in this work. To tackle this challenge, a radar data processing method utilizing point transformer networks has been proposed. The method has been experimentally verified on a dataset of human activities, and experiments have been performed to determine its optimal implementation. Promising preliminary results on a 9-class dataset show test accuracy and macro F-1 scores in the range of 83% and 73% respectively.@en

Driven by its contactless sensing capabilities and the lack of optical images being recorded, radar technology has been recently investigated in the context of human healthcare. This includes a broad range of applications, such as human activity classification, fall detection, gait and mobility analysis, and monitoring of vital signs such as respiration and heartbeat. In this paper, a review of notable achievements in these areas and open research challenges is provided, showing the potential of radar sensing for human healthcare and assisted living.

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Radar-based human motion and activity recognition is currently a topic of great research interest, as the aging population increases and older individuals prefer an independent lifestyle. This technology has a wide range of applications, such as fall detection in assisted living, gesture recognition for human-machine interfaces, and many more. Numerous studies exist on various approaches for radar-based activity capture and classification. However, most of these employ rather artificial data, often obtained in laboratory environments, and typically collected under particular conditions. Specifically, most research so far has aimed at distinguishing a predefined set of single activities with a defined start, stop and duration. This paper aims at drawing the attention to a so far less researched issue, one that will be of vital importance for future real-world application of radar-based human activity recognition: continuous activity recognition, i.e. recognizing specific activities in a stream of several sequential activities with unknown duration and arbitrary transitions between different classes of activities. A review on the current state of the art in this relatively new topic is given, followed by a discussion on future research directions.@en

Recognition of continuous human activities is investigated in unconstrained movement directions using multiple spatially distributed radar nodes, where activities can occur at unfavourable aspect angles or occluded perspectives when using a single node. Furthermore, such networks are favourable not only for the aforementioned aim, but also for larger controlled surveillance areas that may require more than just one sensor. Specifically, a distributed network can show significant differences in signature between the nodes when targets are located at long distances and different aspect angles. Radar data can be represented in various domains, where a widely known domain for Human Activity Recognition (HAR) is the microDoppler spectrogram. However, other domains might be more suitable for better classification performance or are superior for low-cost hardware with limited computational resources, such as the Range-Time or the Range-Doppler domain. An open question is how to take advantage of the diversity of information extractable from the aforesaid data domains, as well as from different distributed radar nodes that simultaneously observe a surveillance area. For this, data fusion techniques can be used at both the level of data representations for each radar node, and across the different nodes in the network. The introduced methods of decision fusion, where typically one classifier operates on each node, or feature fusion, where the data is concatenated before using one single classifier, will be exploited, investigating their performance for continuous sequence classification, a more naturalistic and realistic way of classifying human movements, also accounting for inherent imbalances in the dataset. @en

Cognitive radar frameworks rely on the ability to quantify and reason on future uncertainty, which allows for the selection of an optimal decision policy. These methods require that the uncertainty estimates provided by the underlying statistical model are well-calibrated, i.e. consistent with true uncertainty. In this work, the utilization of probability calibration techniques for target classification is explored. It is shown from simulations and experimental data that the proposed techniques can be used to correct errors in uncertainty estimates caused by incorrect modeling assumptions, such as the independence of sensors and the independence of classification covariates. This correction improves classification performance and the reliability of cognitive systems so that resources are utilized in accordance with user-defined cost functions.@en