Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Animal lameness detection with radar sensing

Lameness is a significant problem for performance horses and farmed animals, with severe impact on animal welfare and treatment costs. Lameness is commonly diagnosed through subjective scoring methods performed by trained veterinary clinicians, but automatic methods using suitable sensors would improve efficiency and reliability. In this paper, we propose the use of radar micro-Doppler signatures for contactless and automatic identification of lameness, and present preliminary results for dairy cows, sheep, and horses. These proof-of-concept results are promising, with classification accuracy above 85% for dairy cows, around 92% for horses, and close to 99% for sheep

An evaluation framework for stereo-based driver assistance

This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km

Low frequency radio observations of bi-directional electron beams in the solar corona

The radio signature of a shock travelling through the solar corona is known as a type II solar radio burst. In rare cases these bursts can exhibit a fine structure known as `herringbones', which are a direct indicator of particle acceleration occurring at the shock front. However, few studies have been performed on herringbones and the details of the underlying particle acceleration processes are unknown. Here, we use an image processing technique known as the Hough transform to statistically analyse the herringbone fine structure in a radio burst at $\sim$20-90 MHz observed from the Rosse Solar-Terrestrial Observatory on 2011 September 22. We identify 188 individual bursts which are signatures of bi-directional electron beams continuously accelerated to speeds of 0.16$_{-0.10}^{+0.11} c$. This occurs at a shock acceleration site initially at a constant altitude of $\sim$0.6 R$_{\odot}$ in the corona, followed by a shift to $\sim$0.5 R$_{\odot}$. The anti-sunward beams travel a distance of 170$_{-97}^{+174}$ Mm (and possibly further) away from the acceleration site, while those travelling toward the sun come to a stop sooner, reaching a smaller distance of 112$_{-76}^{+84}$ Mm. We show that the stopping distance for the sunward beams may depend on the total number density and the velocity of the beam. Our study concludes that a detailed statistical analysis of herringbone fine structure can provide information on the physical properties of the corona which lead to these relatively rare radio bursts

Identifying First-person Camera Wearers in Third-person Videos

We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene. To do this, we need to establish person-level correspondences across first- and third-person videos, which is challenging because the camera wearer is not visible from his/her own egocentric video, preventing the use of direct feature matching. In this paper, we propose a new semi-Siamese Convolutional Neural Network architecture to address this novel challenge. We formulate the problem as learning a joint embedding space for first- and third-person videos that considers both spatial- and motion-domain cues. A new triplet loss function is designed to minimize the distance between correct first- and third-person matches while maximizing the distance between incorrect ones. This end-to-end approach performs significantly better than several baselines, in part by learning the first- and third-person features optimized for matching jointly with the distance measure itself

A Geometric Approach to Pairwise Bayesian Alignment of Functional Data Using Importance Sampling

We present a Bayesian model for pairwise nonlinear registration of functional data. We use the Riemannian geometry of the space of warping functions to define appropriate prior distributions and sample from the posterior using importance sampling. A simple square-root transformation is used to simplify the geometry of the space of warping functions, which allows for computation of sample statistics, such as the mean and median, and a fast implementation of a $k$-means clustering algorithm. These tools allow for efficient posterior inference, where multiple modes of the posterior distribution corresponding to multiple plausible alignments of the given functions are found. We also show pointwise $95\%$ credible intervals to assess the uncertainty of the alignment in different clusters. We validate this model using simulations and present multiple examples on real data from different application domains including biometrics and medicine

An Approach to the Health Monitoring of the Fuel System of a Turbofan

This paper focuses on the monitoring of the fuel system of a turbofan which is the core organ of an aircraft engine control system. The paper provides a method for real time on-board monitoring and on-ground diagnosis of one of its subsystems: the hydromechanical actuation loop. First, a system analysis is performed to highlight the main degradation modes and potential failures. Then, an approach for a real-time extraction of salient features named indicators is addressed. On-ground diagnosis is performed through a learning algorithm and a classification method. Parameterization of the on-ground part needs a reference healthy state of the indicators and the signatures of the degradations. The healthy distribution of the indicators is measured on field data whereas a physical model of the system is utilized to simulate degradations, quantify indicators sensibility and construct the signatures. Eventually, algorithms are deployed and statistical validation is performed by the computation of key performance indicators (KPI)

Methodology for the Diagnosis of Hydromechanical Actuation Loops in Aircraft Engines

This document provides a method for on-board monitoring and on-ground diagnosis of a hydromechanical actuation loop such as those found in aircraft engines. First, a complete system analysis is performed to understand its behaviour and determine the main degradation modes. Then, system health indicators are defined and a method for their real time on-board extraction is addressed. Diagnosis is performed on-ground through classification of degradation signatures. To parameterize on-ground treatment, both a reference healthy state of indicators and degradations signatures are needed. The healthy distribution of indicators is obtained from data and a physics-based model is used to simulate degradations, quantify indicators sensibility and construct the signatures database. At last, algorithms are deployed and a statistical validation of the performances is conducted