Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are however often complex, focusing on a single domain of radar, and requiring significant computational resources that prevent their deployment in embedded platforms which often have limited memory and computational resources. To address this issue, we present an adaptive magnitude thresholding approach for highlighting the region of interest in the multi-domain micro-Doppler signatures. The region of interest is beneficial to extract salient features, meanwhile it ensures the simplicity of calculations with less computational cost. The results for the proposed approach show an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods on the same dataset with an over tenfold reduction in both training time and memory footprint, and a twofold reduction in inference time compared to a series of deep learning implementations. These results can help bridge the gap toward embedded platform deployment.

Radar systems are increasingly being used for healthcare applications for human activity recognition due to their advantages for privacy compliance, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are often very complex, hence requiring significant computational resources. We propose an adaptive thresholding algorithm used as a 'mask' to highlight the region of interest from the micro-Doppler signature. The mask is then applied to spectrogram information. These masked signatures are used for handcrafted feature extraction and classification. A quadratic-SVM classifier is employed based on the features from the information acquired. The preliminary results show that an accuracy of 91.3% is achieved using sequential forward feature selection with feature fusion. Based on our initial result, a Naïve Bayes combiner is used to improve the overall performance further. With this strategy, the accuracy of classification reaches 92.5% for six activities. Additionally, we compare our findings to those of other models utilizing the same database. The results demonstrate that high accuracy can be achieved when adaptive thresholding is used with the SVM method, and computational resources may significantly decrease.

In personnel recognition based on radar, significant research exists on statistical features extracted from the micro-Doppler signatures, whereas research considering other domains and information such as phase is less developed. This paper presents the use of deep learning methods to integrate both phase and magnitude features from range profiles and spectrogram. The temporal features of both domains are separately extracted using a stack of Long Short Term Memory (LSTM) layers. Then, the extracted features are aggregated in the corresponding domains and pass through a series of dense layers with SoftMax classifier. Finally, the information from the two domains is fused with a soft fusion approach to improve the performance further. Preliminary results show that the proposed network with soft fusion can achieve 85.5% accuracy in personnel recognition with six subjects

Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people.

In human activity recognition (HAR) based on radar, significant research exists on statistical features extracted from the spectrogram (μD), whereas the research which considers other domains is less developed. This paper is aimed to investigate three domains of radar data: μD, Cadence Velocity Diagram (CVD), and range-time (RT) information, evaluating which ones are best suited to classify specific activities. In addition, information fusion is applied to enhance classification accuracy and compare it with the results of single domain approach. Based on the previous results, a hierarchical structure is proposed to improve the performance of classification further. The preliminary results show that different domains have distinctive sensitivity to specific activities. RT information is sensitive to the moving target crossing range bins, while CVD is more sensitive to body movement. The μD is more balanced, which means it can observe both moving targets and body movements. Furthermore, improvement in accuracy is approximately 6-23 % using feature-level fusion. A hierarchical classification approach is also investigated, which has accuracy in the order of approximately 92 %.