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Application of Modified PageRank Algorithm for Anomaly Detection in Movements of Older Adults

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-03-11
Abstract: 

It is a well-known statistic that the percentage of our older adult population will globally surpass the other age groups. A majority of the elderly would still prefer to keep an active life style. In support of this life style, various monitoring systems are being designed and deployed to have a seamless integration with the daily living activities of the older adults while preserving various levels of their privacy. Motion tracking is one of these health monitoring systems. When properly designed, deployed, integrated, and analyzed, they can be used to assist in determining some onsets of anomalies in the health of elderly at various levels of their Movements and Activities of Daily Living (MADL). This paper explores how the framework of the PageRank algorithm can be extended for monitoring the global movement patterns of older adults at their place of residence. Through utilization of an existing dataset, the paper shows how the movement patterns between various rooms can be represented as a directed graph with weighted edges. To demonstrate how PageRank can be utilized, a base graph representing a normal pattern can be defined as what can be used for further anomaly detection (e.g., at some instances of observation the measured movement pattern deviates from what is previously defined as a normal pattern). It is shown how the PageRank algorithm can detect simulated change in the pattern of motion when compared with the base-line normal pattern. This feature can offer a practical approach for detecting anomalies in movement patterns associated with older adults in their own place of residence and in support of aging in place paradigm.

Document type: 
Article
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Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-06-05
Abstract: 

In-vitro fertilization (IVF), as the most common fertility treatment, has never reached its maximum potentials. Systematic selection of embryos with the highest implementation potentials is a necessary step towards enhancing the effectiveness of IVF. Embryonic cell numbers and their developmental rate are believed to correlate with the embryo's implantation potentials. In this paper, we propose an automatic framework based on a deep convolutional neural network to take on the challenging task of automatic counting and centroid localization of embryonic cells (blastomeres) in microscopic human embryo images. In particular, the cell counting task is reformulated as an end-to-end regression problem that is based on a shape-aware Gaussian dot annotation to map the input image into an output density map. The proposed Cell-Net system incorporates two novel components, residual incremental Atrous pyramid and progressive up-sampling convolution. Residual incremental Atrous pyramid enables the network to extract rich global contextual information without raising the ‘grinding’ issue. Progressive up-sampling convolution gradually reconstructs a high-resolution feature map by taking into account short- and longrange dependencies. Experimental results confirm that the proposed framework is capable of predicting the cell-stage and detecting blastomeres in embryo images of 1

Document type: 
Article
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Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-05-08
Abstract: 

Energy consumption in buildings has steadily increased. Buildings consume more energy than necessary due to suboptimal design and operation. Apart from retro-fitting, not much can be done with the design of the existing building, but the operation of the building can be improved. Ignoring or failing to fix the faults can lead to problems like the higher cost in excess energy usage or premature component failure. At the same time understanding, identifying, and addressing abnormal energy consumption in buildings can lead to energy savings and detection of faulty appliances. This paper investigates two key challenges found in energy anomaly detection research: 1) the lack of labeled ground truth and 2) the lack of consistent performance accuracy metrics. In the first challenge, labeled ground truth is imperative for training and benchmarking algorithms to detect anomalies. In the second challenge, consistent performance accuracy metrics are crucial to quantifying how well algorithms perform against each other. There exists no publicly available energy consumption dataset with labeled anomaly events. Therefore, we propose two approaches that help in the automatic annotation of the ground truth data from publicly available datasets: a statistical approach for short-term data and a piecewise linear regression method for long-term data. We demonstrate these approaches using two publicly available datasets called Dataport (Pecan Street) and HUE. Using different existing accuracy metrics, we run a series of experiments on anomaly detection algorithms and discuss what metrics can be best used for consistent accuracy testing amongst researchers. In addition, while providing the source code, we also release an anomaly annotated dataset produced by this source code.

Document type: 
Article
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Deep Attention Models for Human Tracking Using RGBD

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-02-13
Abstract: 

Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects. The proposed model includes depth as an additional feature in a hierarchical modular neural framework for online object tracking. The model adapts to the confusing appearance by identifying the stable property of depth between the target and the surrounding object(s). The depth complements the existing RGB features in scenarios when RGB features fail to adapt, hence becoming unstable over a long duration of time. The parameters of the model are learned efficiently in the Deep network, which consists of three modules: (1) The spatial attention layer, which discards the majority of the background by selecting a region containing the object of interest; (2) the appearance attention layer, which extracts appearance and spatial information about the tracked object; and (3) the state estimation layer, which enables the framework to predict future object appearance and location. Three different models were trained and tested to analyze the effect of depth along with RGB information. Also, a model is proposed to utilize only depth as a standalone input for tracking purposes. The proposed models were also evaluated in real-time using KinectV2 and showed very promising results. The results of our proposed network structures and their comparison with the state-of-the-art RGB tracking model demonstrate that adding depth significantly improves the accuracy of tracking in a more challenging environment (i.e., cluttered and camouflaged environments). Furthermore, the results of depth-based models showed that depth data can provide enough information for accurate tracking, even without RGB information.

Document type: 
Article
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On the Design of a DEA-Based Device to Potentially Assist Lower Leg Disorders: An Analytical and FEM Investigation Accounting for Nonlinearities of the Leg and Device Deformations

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2015-11-05
Abstract: 

Background:

One of the recommended treatments for disorders associated with the lower extremity venous insufficiency is the application of external mechanical compression. Compression stockings and elastic bandages are widely used for the purpose of compression therapy and are usually designed to exert a specified value or range of compression on the leg. However, the leg deforms under external compression, which can lead to undesirable variations in the amount of compression applied by the compression bandages. In this paper, the use of an active compression bandage (ACB), whose compression can be regulated through an electrical signal, is investigated. The ACB is based on the use of dielectric elastomer actuators. This paper specifically investigates, via both analytical and non-linear numerical simulations, the potential pressure the ACB can apply when the compliancy of the human leg is taken into account. The work underpins the need to account for the compressibility of the leg when designing compression garments for lower extremity venous insufficiency. 

Methods

A mathematical model is used to simulate the volumetric change of a calf when compressed. Suitable parameters for this calf model are selected from the literature where the calf, from ankle to knee, is divided into six different regions. An analytical electromechanical model of the ACB, which considers its compliancy as a function of its pre-stretch and electricity applied, is used to predict the ACB’s behavior. Based on these calf and ACB analytical models, a simulation is performed to investigate the interaction between the ACB and the human calf with and without an electrical stimulus applied to the ACB. This simulation is validated by non-linear analysis performed using a software based on the finite element method (FEM). In all simulations, the ACB’s elastomer is stretched to a value in the range between 140 and 220 % of its initial length. 

Results

Using data from the literature, the human calf model, which is examined in this work, has different compliancy in its different regions. For example, when a 28.5 mmHg (3.8 kPa) of external compression is applied to the entire calf, the ankle shows a 3.7 % of volume change whereas the knee region undergoes a 2.7 % of volume change. The paper presents the actual pressure in the different regions of the calf for different values of the ACB’s stretch ratio when it is either electrically activated or not activated, and when compliancy of the leg is either considered or not considered. For example, results of the performed simulation show that about 10 % variation in compression in the ankle region is expected when the ACB initially applies 6 kPa and the compressibility of the calf is first considered and then not considered. Such a variation reduces to 5 % when the initial pressure applied by the ACB reduced by half. 

Conclusions

Comparison with non-linear FEM simulations show that the analytical models used in this work can closely estimate interaction between an active compression bandage and a human calf. In addition, compliancy of the leg should not be neglected when either designing a compression band or predicting the compressive force it can exert. The methodology proposed in this work can be extended to other types of elastic compression bandages and garments for biomedical applications. 

Document type: 
Article
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Evaluating the Efficacy of an Active Compression Brace on Orthostatic Cardiovascular Responses

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2017-11-22
Abstract: 

Orthostatic intolerance, one of the principle causes of syncope, can occur secondary to concomitant venous pooling and enhanced capillary filtration. We aimed to evaluate a prototype portable calf active compression brace (ACB) designed to improve orthostatic haemodynamic control. Fourteen healthy volunteers participated in a randomized, placebo controlled, cross-over, double-blind study. Testing consisted of head-upright tilting and walking on a treadmill conducted on two consecutive days with a pair of ACBs wrapped around both calves. The ACB was actuated on one test day, but not on the other (placebo). Wearability, comfort, and ambulatory use of the ACB were assessed using questionnaires. The average calf pressure exerted by the ACB was 46.3±2.2 mmHg and the actuation pressure was 20.7±1.7 mmHg. When considering the differences between ACB actuation and placebo during tilt after supine rest there were trends for a larger stroke volume (+5.20±2.34%, p = 0.05) and lower heart rate (-5.12±2.41%, p = 0.06) with ACB actuation, with no effect on systolic arterial pressure (+4.86±3.41%, p = 0.18). The decrease in stroke volume after ten minutes of tilting was positively correlated with the height:calf circumference (r = 0.464; p = 0.029; n = 22; both conditions combined). The increase in heart rate after ten minutes of tilting was negatively correlated with the height:calf circumference (r = -0.485; p = 0.022; n = 22; both conditions combined) and was positively correlated with the average calf circumference (r = 0.539; p = 0.009; n = 22; both conditions combined). Participants reported good ACB wearability and comfort during ambulatory use. These data verify that the ACB increased stroke volume during tilting in healthy controls. Active calf compression garments may be a viable option for the management of orthostatic intolerance.

Document type: 
Article
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Staring Spotlight TerraSAR-X SAR Interferometry for Identification and Monitoring of Small-Scale Landslide Deformation

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-05-28
Abstract: 

We discuss enhanced processing methods for high resolution Synthetic Aperture Radar (SAR) interferometry (InSAR) to monitor small landslides with difficult spatial characteristics, such as very steep and rugged terrain, strong spatially heterogeneous surface motion, and coherence-compromising factors, including vegetation and seasonal snow cover. The enhanced methods mitigate phase bias induced by atmospheric effects, as well as topographic phase errors in coherent regions of layover, and due to inaccurate blending of high resolution discontinuous with lower resolution background Digital Surface Models (DSM). We demonstrate the proposed methods using TerraSAR-X (TSX) Staring Spotlight InSAR data for three test sites reflecting diverse challenging landslide-prone mountain terrains in British Columbia, Canada. Comparisons with corresponding standard processing methods show significant improvements with resulting displacement residuals that reveal additional movement hotspots and unprecedented spatial detail for active landslides/rockfalls at the investigated sites.

Document type: 
Article
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Multimodal Characterization of the Semantic N400 Response within a Rapid Evaluation Brain Vital Sign Framework

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-06-04
Abstract: 

Background: For nearly four decades, the N400 has been an important brainwave marker of semantic processing. It can be recorded non-invasively from the scalp using electrical and/or magnetic sensors, but largely within the restricted domain of research laboratories specialized to run specifc N400 experiments. However, there is increasing evidence of signifcant clinical utility for the N400 in neurological evaluation, particularly at the individual level. To enable clinical applications, we recently reported a rapid evaluation framework known as “brain vital signs” that successfully incorporated the N400 response as one of the core components for cognitive function evaluation. The current study characterized the rapidly evoked N400 response to demonstrate that it shares consistent features with traditional N400 responses acquired in research laboratory settings—thereby enabling its translation into brain vital signs applications.

Methods: Data were collected from 17 healthy individuals using magnetoencephalography (MEG) and electroencephalography (EEG), with analysis of sensor-level efects as well as evaluation of brain sources. Individual-level N400 responses were classifed using machine learning to determine the percentage of participants in whom the response was successfully detected.

Results: The N400 response was observed in both M/EEG modalities showing signifcant diferences to incongruent versus congruent condition in the expected time range (p<0.05). Also as expected, N400-related brain activity was observed in the temporal and inferior frontal cortical regions, with typical left-hemispheric asymmetry. Classifcation robustly confrmed the N400 efect at the individual level with high accuracy (89%), sensitivity (0.88) and specifcity (0.90).

Conclusion: The brain vital sign N400 characteristics were highly consistent with features of the previously reported N400 responses acquired using traditional laboratory-based experiments. These results provide important evidence supporting clinical translation of the rapidly acquired N400 response as a potential tool for assessments of higher cognitive functions.

Document type: 
Article
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Developing Brain Vital Signs: Initial Framework for Monitoring Brain Function Changes over Time

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2016-05-12
Abstract: 

Clinical assessment of brain function relies heavily on indirect behavior-based tests. Unfortunately, behavior-based assessments are subjective and therefore susceptible to several confounding factors. Event-related brain potentials (ERPs), derived from electroencephalography (EEG), are often used to provide objective, physiological measures of brain function. Historically, ERPs have been characterized extensively within research settings, with limited but growing clinical applications. Over the past 20 years, we have developed clinical ERP applications for the evaluation of functional status following serious injury and/or disease. This work has identified an important gap: the need for a clinically accessible framework to evaluate ERP measures. Crucially, this enables baseline measures before brain dysfunction occurs, and might enable the routine collection of brain function metrics in the future much like blood pressure measures today. Here, we propose such a framework for extracting specific ERPs as potential “brain vital signs.” This framework enabled the translation/transformation of complex ERP data into accessible metrics of brain function for wider clinical utilization. To formalize the framework, three essential ERPs were selected as initial indicators: (1) the auditory N100 (Auditory sensation); (2) the auditory oddball P300 (Basic attention); and (3) the auditory speech processing N400 (Cognitive processing). First step validation was conducted on healthy younger and older adults (age range: 22–82 years). Results confirmed specific ERPs at the individual level (86.81–98.96%), verified predictable age-related differences (P300 latency delays in older adults, p < 0.05), and demonstrated successful linear transformation into the proposed brain vital sign (BVS) framework (basic attention latency sub-component of BVS framework reflects delays in older adults, p < 0.05). The findings represent an initial critical step in developing, extracting, and characterizing ERPs as vital signs, critical for subsequent evaluation of dysfunction in conditions like concussion and/or dementia.

Document type: 
Article
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De-sketching

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-06-22
Abstract: 

Many software applications exist for plotting graphs of mathematical functions, yet there are none (to our knowledge) that perform the inverse operation - estimating mathematical expressions from graphs. Since plotting graphs (especially by hand) is often referred to as "sketching," we refer to the inverse operation as "de-sketching." As the number of mathematical expressions that approximate a given curve can be quite large, in this demo we restrict our attention to polynomials, and present a deep model that performs de-sketching by finding the best second-degree polynomial to fit the curve in the input image. Currently, our trained model is able to provide reasonably accurate estimates of polynomial coefficients for both synthetically-generated and hand-drawn curves.

Document type: 
Conference presentation
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