This work focuses on a radar-based fall detection technique. In order to address the fall detection problem, a new method to analyze the radar returns is proposed based on time-frequency analysis and deep neural network. The time-frequency analysis is first performed by applying the short-time Fourier transform and resulting spectrogram is projected onto a binary image representation. Unlike most of the existing works, the proposed method is realized by a high-level feature learning from radar return signals using deep neural network. This method adopts a deep convolutional neural network to automate feature extraction and accurately classifies fall and non-fall activities. The results show that the proposed method is capable of distinguishing fall from non-fall activities with low false alarm rate. In the proposed method, hierarchical features extracted from radar returns are the key points to understanding fundamental composition of human activities and determining whether or not a fall has occurred.

Automatic fall detection using radar aids in better assisted living and smarter health care. In this brief, a novel time series-based method for detecting fall incidents in human daily activities is proposed. A time series in the slow-time is obtained by summing all the range bins corresponding to fast-time of the ultra wideband radar return signals. This time series is used as input to the proposed deep convolutional neural network for automatic feature extraction. In contrast to other existing methods, the proposed fall detection method relies on multi-level feature learning directly from the radar time series signals. In particular, the proposed method utilizes a deep convolutional neural network for automating feature extraction as well as global maximum pooling technique for enhancing model discriminability. The performance of the proposed method is compared with that of the state-of-the-art such as recurrent neural network, multi-layer perceptron and dynamic time warping techniques. The results demonstrate that the proposed fall detection method outperforms the other methods in terms of higher accuracy, precision, sensitivity and specificity values.

Objective : Wearable devices with embedded photoplethysmography (PPG) sensors enable continuous monitoring of cardiovascular activity, allowing for the detection cardiovascular problems, such as arrhythmias. However, the quality of wrist-based PPG is highly variable, and is subject to artifacts from motion and other interferences. The goal of this paper is to evaluate the signal quality obtained from wrist-based PPG when used in an ambulatory setting. Approach : Ambulatory data were collected over a 24 h period for 10 elderly, and 16 non-elderly participants. Visual assessment is used as the gold standard for PPG signal quality, with inter-rater agreement evaluated using Fleiss’ Kappa. With this gold standard, 5 classifiers were evaluated using a modified 13-fold cross-validation approach. Main results : A Random Forest quality classification algorithm showed the best performance, with an accuracy of 74.5%, and was then used to evaluate 24 h long ambulatory wrist-based PPG measurements. Significance : In general, data quality was high at night, and low during the day. Our results suggest wrist-based PPG may be best for continuous cardiovascular monitoring applications during the night, but less useful during the day unless methods can be identified to improve low quality signal segments.

Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.

Monitoring critical infrastructure is of great importance for resilience against current and emerging hazards. In Canada, at the national level, critical infrastructure is strategically classified into the following 10 sectors: energy and utilities, finance, food, transportation, government, information and communication technology, health, water, safety, and manufacturing. Many of these critical infrastructures may be effectively monitored using emerging technologies such as light detection and ranging (LiDAR). LiDAR technology enables active remote sensing from airborne and terrestrial systems. Accordingly, LiDAR can be used in a wide range of applications related to the monitoring of critical infrastructure to promote resilience. This survey provides a comprehensive, and structured literature overview of the LiDAR technology currently available for commercial and research use, and also provides a detailed review of the relevant applications of LiDAR for critical infrastructure monitoring. Several LiDAR-based applications in areas of critical infrastructure such as terrestrial and air transportation, gas, oil, and water pipelines, energy generation and distribution, and security are discussed. A summary of the LiDAR data sets that are currently available for use is also presented.

Typical radar detectors exploit only a small proportion of the valuable information contained in radar reflections, i.e. magnitude and Doppler. A neural network-based approach for augmenting traditional radar detector structures using machine learning (ML) is proposed in this paper. Specifically, the network is designed to augment target detection in the field of maritime wide area surveillance for non-coherent data. A combination network consisting of a convolutional neural network (CNN) to extract spatial features and a long short-term memory (LSTM) for extracting temporal patterns in the spatial features is proposed. The network augments the detector structure by blanking out regions of the frame which are classified as not containing a target, thus reducing false alarms. The network is tested on data containing four marine targets collected by a ground-based radar. The data set was chosen because it contains strong sea clutter returns. When ML is used, the receiver operating characteristic (ROC) curves are shifted to lower probability of false alarm (PFA). A Kalman filter tracker was applied to the ML-augmented and baseline detections, and it was shown that ML-augmented detections produced similar tracks at lower PFA. The feature discovering capability of the network is analyzed through a series of tests, and the argument is made that the CNN-LSTM network presented in this work demonstrates the ability to improve the detection performance by exploiting spatial and temporal information in the data.