A survey on fall detection: Principles and approaches

Pattern recognition

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Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.

WiFall: Device-Free Fall Detection by Wireless Networks

Traditional and recent approaches in background modeling for foreground detection: An overview

This research addresses a new iterative geometrical noise cancellation method for closed-form camera pose estimation based on collinearity theory. We first explain how to estimate camera position and it’s orientation by employing extra nonsingular point of the edge line of the landmark’s corner through a closed-form geometrical pose estimation algorithm. Then, we propose a new iterative noise cancellation algorithm to reduce the estimation error of camera transaction matrix, which has the most portion in camera pose estimation errors. To validate our proposed method, we test it as a computer simulation. The results show that this method is efficient, accurate and robustness. But the drawback is that the camera rotation matrix, which used in noise cancellation algorithm, may not always be inverseable.

/pdf/an-iterative-geometrical-noise-cancellation-approach-to-21y9yqoejf.pdf

An Iterative Geometrical Noise Cancellation Approach to Closed-form Camera Pose Estimation

As the physical size of single pixels in digital cameras grows smaller, the captured images are increasingly affected by defocused blurring and consequently valuable details are lost. Different aperture patterns have already been proposed to mitigate this problem based on presumed conditions, which maybe violated in practise. Sensor characteristics and current photometric scene properties have been largely ignored in the design of aperture patterns in the literature. In this study, a number of perceptually optimised coded apertures are introduced for defocused deblurring. These apertures are specifically designed considering illumination conditions, sensor specifications and human visual system characteristics. The designed patterns are compared with circular apertures of equal throughput and pinhole aperture. Experiments show signal-to-noise ratio (SNR) gains of up to 0.35 and 2 dB over circular and pinhole apertures, respectively. To study the trade-off between diffraction and deblurring gains, the proposed binary masks are enhanced by smoothing and morphological operations, which can yield non-binary and rounded binary patterns. The results of the authors’ study show that rounded binary patterns improve diffraction behaviour while maintaining the desired SNR level.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2015.0831

Analysis and design of coded apertures for defocus deblurring based on imaging system properties and optical features

Various applications with different requirments are rapidly developed in the smart grid. The need to provide Quality of Service (QoS) for such a communication network is inevitable. However, recently a protocol called RPL (Routing Protocol for Low Power and Lossy Network) has been standardized and is known as the main solution for last mile communication network of smart grid. In this paper, by studying the existing methods and identifying the shortcomings, we propose a customized version of RPL which we call OMC-RPL (Optimized Multi Class-RPL). Two principal advantages of the proposed method are: a holistic objective function including distinctive metrics related to QoS; and supporting the data classification which is an important requirement in this context. The main contribution of this paper is to make different objective functions proportional to the number of classes by using weighting parameters. The best values of these coefficients are determined by an optimization algorithm. OMC-RPL is evaluated from different aspects. Simulation results show that the new idea significantly decreases the end-to-end delay and increases lifetime of the nodes that have limited source of energy. It seems that OMC-RPL could be a good substitution for the available methods.

/pdf/designing-optimized-scheduling-qos-aware-rpl-for-sensor-2426ho63wh.pdf

Designing Optimized Scheduling QoS-Aware RPL for Sensor-Based Smart Grid Communication Network

In this paper, we present a new visual attention system which is able to detect objects in the images with non-uniform resolution. Since, one of the goals of the visual attention systems is simulating of human perception, and in human visual system the foveated images processed, therefore, visual attention systems should be able to identify the object to these images. We test the system by two types of the images: real world and artificial images. Real world images include the cloth images with some defect, and the system should detect these defects. In artificial images, one element is different from the others only in one feature and the system should detect it.

Object Detection in Foveated Images

In face detection or recognition applications, a major trend would be the eye detection. Its wide use as part of intensive applications, has made it a challenging task to work on which mainly depends on color characteristics as a useful way to detect eyes. We use special color space, YCbCr as its components yield valuable information about eyes in the image. We have constructed two maps according to its components and have merged them to obtain a final map. Candidates have been generated on this final map. An extra phase on candidates has been applied to determine suitable eye pair. The extra phase consists of flexible thresholding and geometrical tests. Flexible thresholding makes generating candidates more precise, while geometrical tests allow suitable candidates to be selected as eyes. Simulation results on CVL and Iranian databases have shown that this extra phase has improved the correct detection rate by about 12% and reached 98% success rate on the average.

Hamid Reza Pourreza

Papers

An Iterative Geometrical Noise Cancellation Approach to Closed-form Camera Pose Estimation

Analysis and design of coded apertures for defocus deblurring based on imaging system properties and optical features

Designing Optimized Scheduling QoS-Aware RPL for Sensor-Based Smart Grid Communication Network

Object Detection in Foveated Images

A Novel Algorithm on Facial Color Images for Detection Eyes