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Radar-based human activity recognition (HAR) finds various applications like assisted living and driver behavior monitoring. As radar data are heavily environment-dependent, it is becoming increasingly important to develop a transfer learning mechanism that enables a radar-based HAR system with desirable cross-environment adaptation feasibility. This paper concerns the issue of how radar-based HAR system can adapt to a new environment without source data. To this end, we devote to using the source hypothesis transfer learning architecture to build such an environment adaptation mechanism towards cross-environment radar-based HAR. In doing this, it is a challenging task to develop a reliable self-supervised labeling strategy for generating pseudo labels associated with the unlabeled target data, which is crucial to facilitate the learning of a target-specific feature extractor being responsible for environment adaptation. This paper presents the neighbor-aggregating-based labeling method and incorporates it with the existing clustering-based labeling method to perform the self-supervised labeling task. The logic behind our approach is that the above two labeling methods are complementary to each other in terms of making use of both local and global structures of adaptation data to supervise the labeling task. The coordination of both labeling methods is motivated to be implemented in the weighted combination form, which contributes to improving the reliability of generated labels. Experimental results on a public HAR dataset based on the frequency modulated continuous wave (FMCW) radar demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This article concerns the issue of how to combine the multidomainradar information, including range–Doppler, time–Doppler, and time–range, for human activity recognition. Specifically, to fully make use of radar information, instead of using a single-domain spectrum as inputs, a novel hybrid neural network model is developed for exploring multidomain fusion of radar information. In doing this, three kinds of 2-D domain spectra are used in a fashion of supplementing each other with a hybrid framework that combines three models: 1-D convolution neural network, recurrent neural network, and 2-D convolution network. It is advantageous to use such a hybrid model to capture much rich features through multidomain feature fusion, so as to improve the accuracy of human activity recognition effectively. Experimental results validate the proposed method. [ABSTRACT FROM AUTHOR]

• The activity modeling method based on the emergent paradigm and the directed-weighted network can fully extract feature structure. • The additional context-aware information of each whole activity settles the issue of activity ambiguity. • The heterogeneous mechanism of pheromone keeps the balance between the new and old context-aware information. With the population aging, elderly health monitoring is triggering more studies on daily activity recognition as the fundamental of ambient assisted living. It is remarkable that activity recognition remains difficulties including how to adequately extract feature structure and settle the issue of activity confusion. To address these challenges, we propose a novel activity modeling method under the emergent paradigm with marker-based stigmergy and the directed-weighted network with additional context-aware information. In the modeling process, stigmergy is first introduced to aggregate the context information at the low level for generating activity pheromone trails, and then the constructed stigmergic trails are represented in form of directed-weighted network with distinguishability of individual pheromone source corresponding to location. The potential advantage is that the robust trails with distinguishable individual initial positions are feasible to supplement user's daily habits and thus both inter-class and intra-class distances can be kept at acceptable levels. Experiments on Aruba demonstrates that the proposed emergent modeling method can effectively deal with the problems of feature extraction and activity ambiguity and achieve good classification performance. [ABSTRACT FROM AUTHOR]

Falls are a major health risk for the elderly, decreasing their ability to live independently. This article proposes a novel feature-specific floor pressure imaging system that uses a smart floor embedded with fiber sensors. The potential applications include indoor human-activity analysis for intelligent surveillance. In particular, the authors focus on the application of fall detection in a bathroom scenario. Their fall detection method consists of two steps: floor pressure imaging for target posture classification and the fall event decision based on the classified target postures. Fall detection experiments validated the proposed method. This article is part of a special issue on domestic pervasive computing. [ABSTRACT FROM AUTHOR]

With the acceleration of aging process of population structure, the single resident lifestyle is increasing on account of the high cost of care services and the privacy invasion concern. It is essential to monitor the activities of solitary elderly to find the emergency and lifestyle deviation, as independent life cannot be maintained due to physical or mental problems. The unobtrusive systems are the most preferred choice for the real-life long-term monitoring, while the camera and wearable devices based systems are not suitable due to the privacy and uncomfortableness, respectively. We propose a novel sensor-based activity recognition model based on the two-layer multi-granularity framework and the emergent paradigm with marker-based stigmergy. The stigmergy based marking subsystem builds features by aggregating the context-aware information and generating the two-dimensional activity pheromone trail. The two-layer framework consists of coarse-grained and fine-grained classification subsystems. The coarse-grained subsystem identifies whether the input completed activity segmented by the traditional method is easily-confused, and utilizes our generalized segmentation method to increase the inter-cluster distance. The fine-grained subsystem employs machine learning or deep learning classifiers to realize the activity recognition task. The proposed model is a data-driven model based on the information self-organization. It does not need sophisticated domain knowledge, and can fully mine the hidden feature structure containing semantically related information and spatio-temporal characteristics. The experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]