Nowadays, health monitoring issues are increasing as the worldwide population is aging. In this paper, the radar modality is used to classify with radar signature automatically. The classic approach is to extract features from micro-Doppler signatures for classification. This data representation domain has its limitations for activities presenting similar accelerations like a frontal fall and picking up an object from the floor that lead to wrongly labeled activities. In this work, we propose to combine multiple radar data domains with deep learning. Features are extracted from four domains, namely, Range-Time, Range-Doppler, Doppler-Time, and Cadence Velocity Diagram. The extracted features are set as the input of a Convolutional Neural Network, yielding 91% accuracy with 10-fold cross-validation based on the University of Glasgow "Radar signatures of human activities" open dataset.

Article citation info: IntroductionAmong several techniques available to model sequence and quantify the failure probability in probabilistic risk assessment (PRA), event trees (ETs) are the most recognized methods that develop logical relationship among the events leading to the possible consequences, while fault trees (FTs) best represent the logic corresponding to pivotal events (PEs) and estimate the probabilities [16].Dependencies in event tree/ fault tree (E/FT) model are frequently encountered, and, if neglected, may result in an error estimation. Hosseini and Takahashi [4] classify dependencies into two categoriesimplicit and explicit. Explicit dependencies are due to shared basic events (SBEs) such as shared utilities or shared components which appear in more than one corresponding FTs, while the expression of implicit dependencies is a bit vague. Nývlt and Rausand [13] expanded the before-mentioned division to cover more types of dependencies such as common cause failures and cascading effect, and further classified the explicit dependencies with static and dynamic behaviour. However, in practice of aerospace PRA, such as lunar exploration which has the characteristics of the phased-mission system (PMS), ETs are typically used to portray progressions of phase mission over time, and the time interval between pivotal events (PEs) is not negligible, dependencies therefore become phase-dependency (as a subset of time-dependency in this context), and make the E/FT based reliability and risk analysis more difficult [1,13].In ET analysis, not so much work has been done with time-dependency analysis, and the papers cited above are mainly based on the hypothesis about static or time-independent behaviour [1,4,13,23]. PMS reliability attracts substantial attentions, and various techniques have been developed to deal with the phase-dependency. The analytical techniques for the PMS can be classified into two categories: combinatorial models (e.g., mini-components, sum of disjoint phase products, BDD) and state-space transition models (e.g., Markov models, Petri nets) [19,21]. The combinatorial method is based on the JiA M. A Bayesian networks approach for event tree time-dependency analysis on phased-mission system. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2015; 17 (2): 273-281, http://dx.doi.org/10.17531

As the number of older adults increases worldwide, new paradigms for indoor activity monitoring are required to keep people living at home independently longer. Radar-based human activity recognition has been identified as a sensing modality of choice because it is privacy-preserving and does not require end-users compliance or manipulation. In this paper, we explore the robustness of machine learning algorithms for human activity recognition using six different activities from the University of Glasgow dataset recorded with an FMCW radar. The raw radar data is pre-processed and represented using four different domains, namely, range-time, range-Doppler amplitude and phase diagrams, and Cadence Velocity Diagram. From those, salient features can be extracted and classified using Support Vector Machine, Stacked AutoEncoder, and Convolutional Neural Networks. The fusion of handcrafted features and features from CNN is applied to get the best scheme of classification with over 96% accuracy.

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