An Advanced Home ElderCare Service

With the increase of welfare cost all over the developed world, there is a need to resort to new technologies that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and provides a framework for creating and managing rescue teams willing to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service

Over time RF fitting for Jitter Free 3D Vertebra Reconstruction from Video Fluoroscopy

Over the past decades, there has been an increasing interest in spine kinematics. Various approaches have been proposed on how to observe and analyse spine kinematics from a computer vision perspective. Amongst all, emphasis has been given to both the shape of the individual vertebrae as well as the overall spine curvature as a means of providing accurate and valid spinal condition diagnosis. Traditional invasive methods cannot accurately delineate the intersegmental motion of the spine vertebrae. On the contrary, capturing and measuring spinal motion via the non-invasive fluoroscopy has been a popular technique choice because of its low incurred patient radiation exposure nature. In general, image-based and other reconstruction methods target individual frames and focus on static spine instances. However, even the ones analysing sequences yield in unstable and jittery animations of the reconstructed spine. In this report, we address this issue using a novel approach to robustly reconstruct and rigidly derive a shape with no inter-frame variations. This is to produce animations that are jitter free across our sequence based on fluoroscopy video. Our main contributions are 1) retaining the shape of the solid vertebrae across the frame range, 2) helping towards a more accurate image segmentation even when there's a limited training set. We show our pipeline's success by reconstructing and comparing 3D animations of the lumbar spine from a corresponding fluoroscopic video

ANGELAH: A Framework for Assisting Elders At Home

The ever growing percentage of elderly people within modern societies poses welfare systems under relevant stress. In fact, partial and progressive loss of motor, sensorial, and/or cognitive skills renders elders unable to live autonomously, eventually leading to their hospitalization. This results in both relevant emotional and economic costs. Ubiquitous computing technologies can offer interesting opportunities for in-house safety and autonomy. However, existing systems partially address in-house safety requirements and typically focus on only elder monitoring and emergency detection. The paper presents ANGELAH, a middleware-level solution integrating both ”elder monitoring and emergency detection” solutions and networking solutions. ANGELAH has two main features: i) it enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and ii) provides a solid framework for creating and managing rescue teams composed of individuals willing to promptly assist elders in case of emergency situations. A prototype of ANGELAH, designed for a case study for helping elders with vision impairments, is developed and interesting results are obtained from both computer simulations and a real-network testbed

Dynamic eye misalignment retroversion system (DEMRS)

Strabismus is a medical term used to define the eye misalignment conditions that prevent both eyes from focusing on the same target simultaneously. This is a disability that prohibits the perception of depth. The purpose of going through treatment is to realign the bad (strabismic) eye, so that it fixates on the same target as the good (correct) eye. This paper presents a novel system that can dynamically adjust the light rays coming into each eye in order to stimulate both foveae, with the correct image, simultaneously. This system is to be used in diagnosis and treatment for children with strabismus under the age of eight and for patients of any age suffering from Convergence Insufficiency (CI) or Diplopia

Detecting indicators of cognitive impairment via Graph Convolutional Networks

While the life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Dementia is a name used to describe progressive brain syndromes affecting memory, thinking, behaviour and emotion. People suffering from dementia may lose their abilities to perform daily life activities and they become on their caregivers. Hence, detecting the indicators of cognitive decline and warning the caregivers and medical doctors for further diagnosis would be helpful. In this study, we tackle the problem of activity recognition and abnormal behaviour detection in the context of dementia by observing daily life patterns of elderly people. Since there is no real-world data available, firstly a method is presented to simulate abnormal behaviour that can be observed in daily activity patterns of dementia sufferers. Secondly, Graph Convolutional Networks (GCNs) are exploited to recognise activities based on their granular-level sensor activations. Thirdly, abnormal behaviour related to dementia is detected using activity recognition confidence probabilities. Lastly, GCNs are compared against the state-of-the-art methods. The results obtained indicate that GCNs are able to recognise activities and flag abnormal behaviour related to dementia

Scatter reduction for grid-less mammography using the convolution-based image post-processing technique

Mammography examinations are highly affected by scattered radiation, as it degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids are currently used in planar mammography examinations as the standard physical scattering reduction technique. This method has been found to be inefficient, as it increases the dose delivered to the patient, does not remove all the scattered radiation and increases the price of the equipment. Alternative scattering reduction methods, based on post-processing algorithms, are being investigated to substitute anti-scatter grids. Methods such as the convolution-based scatter estimation have lately become attractive as they are quicker and more flexible than pure Monte Carlo (MC) simulations. In this study we make use of this specific method, which is based on the premise that the scatter in the system is spatially diffuse, thus it can be approximated by a two-dimensional low-pass convolution filter of the primary image. This algorithm uses the narrow pencil beam method to obtain the scatter kernel used to convolve an image, acquired without anti-scatter grid. The results obtained show an image quality comparable, in the worst case, to the grid image, in terms of uniformity and contrast to noise ratio. Further improvement is expected when using clinically-representative phantoms

A semi-empirical model for scatter field reduction in digital mammography

X-ray mammography is the gold standard technique in breast cancer screening programmes. One of the main challenges that mammography is still facing is scattered radiation, which degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids, the main standard physical scattering reduction technique, have some unresolved challenges as they increase the dose delivered to the patient, do not remove all the scattered radiation and increase the cost of the equipment. Alternative scattering reduction methods based on post-processing algorithms, have lately been under investigation. This study is concerned with the use of image post-processing to reduce the scatter contribution in the image, by convolving the primary plus scatter image with kernels obtained from simplified Monte Carlo (MC) simulations. The proposed semi-empirical approach uses up to five thickness-dependant symmetric kernels to accurately estimate the scatter contribution of different areas of the image. Single breast thickness-dependant kernels can over-estimate the scatter signal up to 60%, while kernels adapting to local variations have to be modified for each specific case adding high computational costs. The proposed method reduces the uncertainty to a 4%-10% range for a 35-70 mm breast thickness range, making it a very efficient, case-independent scatter modelling technique. To test the robustness of the method, the scattered corrected image has been successfully compared against full MC simulations for a range of breast thicknesses. In addition, clinical images of the 010A CIRS phantom were acquired with a mammography system with and without the presence of the anti-scatter grid. The grid-less images were post-processed and their quality was compared against the grid images, by evaluating the contrast-to-noise ratio and variance ratio using several test objects, which simulate calcifications and tumour masses. The results obtained show that the method reduces the scatter to similar levels than the anti-scatter grids

A semi-empirical model for scatter field reduction in digital mammography.

X-ray mammography is the gold standard technique in breast cancer screening programmes. One of the main challenges that mammography is still facing is scattered radiation, which degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids, the main standard physical scattering reduction technique, have some unresolved challenges as they increase the dose delivered to the patient, do not remove all the scattered radiation and increase the cost of the equipment. Alternative scattering reduction methods based on post-processing algorithms, have lately been under investigation. This study is concerned with the use of image post-processing to reduce the scatter contribution in the image, by convolving the primary plus scatter image with kernels obtained from simplified Monte Carlo (MC) simulations. The proposed semi-empirical approach uses up to five thickness-dependant symmetric kernels to accurately estimate the scatter contribution of different areas of the image. Single breast thickness-dependant kernels can over-estimate the scatter signal up to 60%, while kernels adapting to local variations have to be modified for each specific case adding high computational costs. The proposed method reduces the uncertainty to a 4%-10% range for a 35-70 mm breast thickness range, making it a very efficient, case-independent scatter modelling technique. To test the robustness of the method, the scattered corrected image has been successfully compared against full MC simulations for a range of breast thicknesses. In addition, clinical images of the 010A CIRS phantom were acquired with a mammography system with and without the presence of the anti-scatter grid. The grid-less images were post-processed and their quality was compared against the grid images, by evaluating the contrast-to-noise ratio and variance ratio using several test objects, which simulate calcifications and tumour masses. The results obtained show that the method reduces the scatter to similar levels than the anti-scatter grids

Tracking Poorly Modelled Motion using Particle Filters with Iterated Likelihood Weighting

Human motion in cluttered scenes is often tracked using particle filtering. However, poorly modelled inter-frame motion is not uncommon, resulting in poor priors for the filtering step. Alternatives to the Condensation algorithm in the form of an Auxiliary Particle Filter (APF) and Iterated Likelihood Weighting (ILW) are described. Experimental results comparing these filters' accuracy and consistency are presented for a scenario in which a person is tracked in an overhead view using an ellipse model with a likelihood based on colour and gradient cues. ILW is not intended to give unbiased estimates of a posterior but rather to reduce approximation error. It is shown to outperform both Condensation and the APF on sequences from this scenario