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Damascening video databases for evaluation of face tracking and recognition -- The DXM2VTS database +
<p>Performance quantification of biometric systems, such as face tracking and recognition highly depend on the database used for testing the systems. Systems trained and tested on realistic and representative databases evidently perform better. Actually, the main reason for evaluating any system on test data is that these data sets represent problems that systems might face in the real world. However, building biometric video databases with realistic background for testing is expensive especially due to its high demand of cooperation from the side of the participants. For example, XM2VTS database contain thousands of video recorded in a studio from 295 subjects. Recording these subjects repeatedly in public places such as supermarkets, offices, streets, etc., is not realistic. To this end, we present a procedure to separate the background of a video recorded in studio conditions with the purpose to replace it with an arbitrary complex background, e.g., outdoor scene containing motion, to measure performance, e.g., eye tracking. Furthermore, we present how an affine transformation and synthetic noise can be incorporated into the production of the new database to simulate natural noise, e.g. motion blur due to translation, zooming and rotation. The entire system is applied to the XM2VTS database, which already consists of several terabytes of data, to produce the DXM2VTS–Damascened XM2VTS database essentially without an increase in resource consumption, i.e., storage, bandwidth, and most importantly, the time of clients populating the database, and the time of the operators.</p> +
<p>Accurate and comprehensive healthcare data coupled with modern analytical tools can play a vital role in enabling care providers to make better-informed decisions, leading to effective and cost-efficient care delivery. This paper describes a novel strategic healthcare analysis and research platform that encapsulates 360-degree pseudo-anonymized data covering clinical, operational capacity and financial data on over 500,000 patients treated since 2009 across all care delivery units in the county of Halland, Sweden. The over-arching goal is to develop a comprehensive healthcare data infrastructure that captures complete care processes at individual, organizational and population levels. These longitudinal linked healthcare data are a valuable tool for research in a broad range of areas including health economy and process development using real world evidence.</p><p>Key messages</p><p>Structured and standardized variables have been linked from different regional healthcare sources into a research information platform including all healthcare visits in the county of Halland in Sweden, from 2009 to date.</p><p>Since 2015, the regional information platform integrates a cost component to each healthcare visit: thus being able to quantify patient level value, safety and cost efficiency across the continuum of care.</p> +
Data Profile : Regional healthcare information platform in Halland, Sweden, a dedicated environment for healthcare research +
<p>Accurate and comprehensive healthcare data coupled with modern analytical tools can play a vital role in enabling care providers to make better-informed decisions, leading to effective and cost-efficient care delivery. This paper describes a novel strategic healthcare analysis and research platform that encapsulates 360-degree pseudo-anonymized data covering clinical, operational capacity and financial data on over 500,000 patients treated since 2009 across all care delivery units in the county of Halland, Sweden. The over-arching goal is to develop a comprehensive healthcare data infrastructure that captures complete care processes at individual, organizational and population levels. These longitudinal linked healthcare data are a valuable tool for research in a broad range of areas including health economy and process development using real world evidence.</p><p>Key messages</p><p>Structured and standardized variables have been linked from different regional healthcare sources into a research information platform including all healthcare visits in the county of Halland in Sweden, from 2009 to date.</p><p>Since 2015, the regional information platform integrates a cost component to each healthcare visit: thus being able to quantify patient level value, safety and cost efficiency across the continuum of care.</p> +
<p>This research aims to develop data-driven methods that extract information from the available data in distribution grids for detecting weak spots, including the components with degraded reliability and areas with power quality problems. The results enable power distribution companies to change from reactive maintenance to predictive maintenance by deriving benefits from available data. In particular, the data is exploited for three purposes: (a) failure pattern discovery, (b) reliability evaluation of power cables, and (c) analyzing and modeling propagation of power quality disturbances (PQDs) in low-voltage grids.</p><p>To analyze failure characteristics it is important to discover which failures share common features, e.g., if there are any types of failures that happen mostly in certain parts of the grid or at certain times. This analysis provides information about correlation between different features and identifying the most vulnerable components. In this case, we applied statistical analysis and association rules to discover failure patterns. Furthermore, we propose a visualization of the correlations between different factors representing failures by using an approximated Bayesian network. We show that the Bayesian Network constructed based on the interesting rules of two items is a good approximation of the real dataset.</p><p>The main focus of reliability evaluation is on failure rate estimation and reliability ranking. In case of power cables, the limited amount of recorded events makes it difficult to perform failure rate modeling. Therefore, we propose a method for interpreting the results of goodness-of-fit measures with confidence intervals, estimated using synthetic data.</p><p>To perform reliability ranking of power cables, in addition to the age of cables, we consider other factors. Then, we use the proportional hazard model (PHM) to assess the impact of the factors and calculate the failure rate of each individual cable. In reliability evaluation, it is important to consider the fact that power cables are repairable components. We discuss that the conclusions about different factors in PHM and cables ranking will be misleading if one considers the cables as non-repairable components.</p><p>In low-voltage distribution grids, analyzing PQDs is important as we are moving towards smart grids with the next generation of producers and consumers. Installing Power Quality and Monitoring Systems (PQMS) at all the nodes in the network, for monitoring the impacts of the new consumer/producer, is prohibitively expensive. Instead, we demonstrate that power companies can utilize the available smart meters, which are widely deployed in the low-voltage grids, for monitoring power quality events and identifying areas with power quality problems. In particular, several models for propagation of PQDs, within neighbor customers in different levels of the grid topology, are investigated. The results show that meters data can be used to detect and describe propagation in low-voltage grids.</p><p>The developed methods of (a) failure pattern discovery are applied on data from Halmstad Energi och Miljö (HEM Nät), Öresundskraft, Göteborg Energy, and Växjö Energy, four different distribution system operators in Sweden. The developed methods of (b) reliability evaluation of power cables and (c) analyzing and modeling propagation of PQDs are applied on data from HEM Nät.</p>
Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation : Acoustic versus contact microphone +
<p>Comprehensive evaluation of results obtained using acoustic and contact microphones in screening for laryngeal disorders through analysis of sustained phonation is the main objective of this study. Aiming to obtain a versatile characterization of voice samples recorded using microphones of both types, 14 different sets of features are extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We propose a new, data dependent random forests-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest is also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the linear predictive coefficients (LPC) and linear predictive cosine transform coefficients (LPCTC) exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for the classification. The proposed data dependent random forest significantly outperformed the traditional random forest. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved.</p> +
Data resource profile : Regional healthcare information platform in Halland, Sweden, a dedicated environment for healthcare research +
<p>Accurate and comprehensive healthcare data coupled with modern analytical tools can play a vital role in enabling care providers to make better-informed decisions, leading to effective and cost-efficient care delivery. This paper describes a novel strategic healthcare analysis and research platform that encapsulates 360-degree pseudo-anonymized data covering clinical, operational capacity and financial data on over 500,000 patients treated since 2009 across all care delivery units in the county of Halland, Sweden. The over-arching goal is to develop a comprehensive healthcare data infrastructure that captures complete care processes at individual, organizational and population levels. These longitudinal linked healthcare data are a valuable tool for research in a broad range of areas including health economy and process development using real world evidence.</p><p>Key messages</p><p>Structured and standardized variables have been linked from different regional healthcare sources into a research information platform including all healthcare visits in the county of Halland in Sweden, from 2009 to date.</p><p>Since 2015, the regional information platform integrates a cost component to each healthcare visit: thus being able to quantify patient level value, safety and cost efficiency across the continuum of care.</p> +
<p>This research aims to develop data-driven methods that automatically exploit historical data in smart distribution grids for reliability evaluation, i.e., analyzing frequency of failures, and modeling components’ lifetime. The results enable power distribution companies to change from reactive maintenance to predictive maintenance by deriving benefits from historical data. In particular, the data is exploited for two purposes: (a) failure pattern discovery, and (b) reliability evaluation of power cables. To analyze failure characteristics it is important to discover which failures share common features, e.g., if there are any types of failures that happen mostly in certain parts of the grid or at certain times. This analysis provides information about correlation between different features and identifying the most vulnerable components. In this case, we applied statistical analysis and association rules to discover failure patterns. Furthermore, we propose an easy-to-understand visualization of the correlations between different factors representing failures by using an approximated Bayesian network. We show that the Bayesian Network constructed based on the interesting rules of two items is a good approximation of the real dataset. The main focus of reliability evaluation is on failure rate estimation and reliability ranking. In case of power cables, the limited amount of recorded events makes it difficult to perform failure rate modeling, i.e., estimating the function that describes changes in the rate of failure depending on age. Therefore, we propose a method for interpreting the results of goodness-of-fit measures with confidence intervals, estimated using synthetic data. To perform reliability ranking of power cables, in addition to the age of cables, we consider other factors. Then, we use the Cox proportional hazard model (PHM) to assess the impact of the factors and calculate the failure rate of each individual cable. In reliability evaluation, it is important to consider the fact that power cables are repairable components. We show that the conclusions about different factors in PHM and cables ranking will be misleading if one considers the cables as non-repairable components. The developed methods of (a) are applied on data from Halmstad Energi och Miljö (HEM Nät), Öresundskraft, Göteborg Energy, and Växjö Energy, four different distribution system operators in Sweden. The developed methods of (b) are applied on data from HEM Nät.</p>
<p>Fuel consumption and vehicle breakdown depend upon the driving style of the driver, for example, hard driving style leads to more wear and consequently more failures of vehicle components. Because of this, it is important to identify and classify the driver’s driving style in order to give the driver feedback through a driver assistance system. The driver would then be able to detect and learn to avoid a driving style that is not appropriate. The input data is provided by different sensors installed in the vehicle, where different drivers and driving routes have been measured. The data is subjectively classified into two different driving styles: normal and hard. Hard driving style can be characterized, for example, by rapid acceleration and braking. Since it is not trivial to build a model which is able to distinguish hard driving from normal, a data mining approach has been employed. In the paper, several classifiers are compared (including e.g. neural networks and decision trees) and a discussion is made on the advantages and disadvantages of the different methods.</p> +
<p>In this paper we present a preliminary investigation of rational agents who, aware of their own limited mental resources, use learning to augment their reasoning. In our approach an agent creates and deductively reasons about possible plans of actions ,but — aware of the fact that finding complete plans is in many cases intractable — it executes partial plans which look promising. By doing so, it can acquire new knowledge from results of performed actions, which allows it to plan further into the future in a more effective way. We describe a possible application of Inductive Logic Programming to learn which of such partial plans are most likely to lead to reaching the goal. We also discuss how one can use ILP framework for generalising partial plans, thus allowing an agent to discover, after a number of episodes, a complete plan — or at least a good approximation of it.</p> +
Dense frequency maps by Structure Tensor and logarithmic scale space : application to forensic fingerprints +
<p>Increasingly, reliable absolute frequency and orientation maps are needed, e.g. for image enhancement. Less studied is however the mutual dependence of both maps, and how to estimate them when none is known initially. We introduce a logarithmic scale space generated by the trace of Structure Tensor to study the relationship. The scale space is non-linear and absolute frequency estimation is reduced to an orientation estimation in it. We show that this offers significant advantages, including construction of efficient estimation methods, using Structure Tensor yielding dense maps of absolute frequency as well as orientation. In fingerprints, both maps can successively improve each other, combined in an image enhancement scheme via Gabor filtering. We verify that the suggested method compares favorably with state of the art, using forensic fingerprints recognition as test bed, and using test images where the ground truth is known. Furthermore, we suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. We present new evidence that the neighborhoods of the absolute frequency map are useful attributes of minutiae. In experiments, we use public data sets to support the conclusions.</p> +
<p>Search and rescue with an autonomous robot is an attractive and challenging task within the research community. This paper presents the development of an autonomous hexacopter that is designed for retrieving a lost object, like a drone, from a vast-open space, like a desert area. Navigating its path with a proposed coverage path planning strategy, the hexacopter can efficiently search for a lost target and locate it using an image-based object detection algorithm. Moreover, after the target is located, our hexacopter can grasp it with a customised gripper and transport it back to a destined location. It is also capable of avoiding static obstacles and dynamic objects. The proposed system was realised in simulations before implementing it in a real hardware setup, i.e. assembly of the drone, crafting of the gripper, software implementation and testing under real-world scenarios. The designed hexacopter won the best UAV design award at the CPS-VO 2018 Competition held in Arizona, USA.</p> +
Design for an Art Therapy Robot : An Explorative Review of the Theoretical Foundations for Engaging in Emotional and Creative Painting with a Robot +
<p>Social robots are being designed to help support people’s well-being in domestic and public environments. To address increasing incidences of psychological and emotional difficulties such as loneliness, and a shortage of human healthcare workers, we believe that robots will also play a useful role in engaging with people in therapy, on an emotional and creative level, e.g., in music, drama, playing, and art therapy. Here, we focus on the latter case, on an autonomous robot capable of painting with a person. A challenge is that the theoretical foundations are highly complex; we are only just beginning ourselves to understand emotions and creativity in human science, which have been described as highly important challenges in artificial intelligence. To gain insight, we review some of the literature on robots used for therapy and art, potential strategies for interacting, and mechanisms for expressing emotions and creativity. In doing so, we also suggest the usefulness of the responsive art approach as a starting point for art therapy robots, describe a perceived gap between our understanding of emotions in human science and what is currently typically being addressed in engineering studies, and identify some potential ethical pitfalls and solutions for avoiding them. Based on our arguments, we propose a design for an art therapy robot, also discussing a simplified prototype implementation, toward informing future work in the area.</p> +
<p>We present a map architecture based on a relational database that helps tackle the challenge of lifelong visuallocalization and mapping. The proposed design is rooted in a set of use-cases that describe the processes necessary for creating, using and analyzing visual maps. Our database and software architecture effectively expresses the requiredinteractions between map elements, such as visual frames generated by multi-camera systems. One of the major strengths of the proposed system is the ease of formulating pertinent and novel queries. We show how these queries can help us gaina better intuition about the map contents, taking into account complex data associations, even as session upon session is added to the map. Furthermore, we demonstrate how referential integrity checks, rollbacks and similar features of relational database management systems are beneficial for building long-term maps. Based on our experience with the proposed system during one year of intensive data collection and analysis, we discuss key lessons learned and indicate directions for evolving its design. These lessons show the importance of using higher relational normal forms to make the database schema even more useful for querying, as well as the need for a distributed, versioned system.</p> +
Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications +
<p>Detecting gait events is the key to many gait analysis applications which would immensely benefit if the analysis could be carried out using wearable sensors in uncontrolled outdoor environments, enabling continuous monitoring and long-term analysis. This would allow exploring new frontiers in gait analysis by facilitating the availability of more data and empower individuals, especially patients, to avail the benefits of gait analysis in their everyday lives. Previous gait event detection algorithms impose many restrictions as they have been developed from data collected incontrolled, indoor environments. This paper proposes a robust algorithm that utilizes a priori knowledge of gait in conjunction with continuous wavelet transform analysis, to accurately identify heel strike and toe off, from noisy accelerometer signals collected during indoor and outdoor walking. The accuracy of the algorithm is evaluated by using footswitches that are considered as ground truth and the results are compared with another recently published algorithm.</p> +
<p>Nowadays in printing industry most of information processing steps are highly automated, except the last one–print quality assessment and control. We present a way to assess one important aspect of print quality, namely the distortion of halftone dots printed colour pictures are made of. The problem is formulated as assessing the distortion of circles detected in microscale images of halftone dot areas. In this paper several known circle detection techniques are explored in terms of accuracy and robustness. We also present a new circle detection technique based on the fuzzy Hough transform (FHT) extended with k-means clustering for detecting positions of accumulator peaks and with an optional fine-tuning step implemented through unsupervised learning. Prior knowledge about the approximate positions and radii of the circles is utilized in the algorithm. Compared to FHT the proposed technique is shown to increase the estimation accuracy of the position and size of detected circles. The techniques are investigated using synthetic and natural images.</p> +
<p>This article is concerned with detection of objects in phytoplankton images, especially objects representing one invasive species-Prorocentrum minimum (P. minimum), - which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280x960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93,25% of the objects. The results are rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species.</p> +
<p>This study investigates signals from sustained phonation and text-dependent speech modalities for Parkinson’s disease screening. Phonation corresponds to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. Signals were recorded through two channels simultaneously, namely, acoustic cardioid (AC) and smart phone (SP) microphones. Additional modalities were obtained by splitting speech recording into voiced and unvoiced parts. Information in each modality is summarized by 18 well-known audio feature sets. Random forest (RF) is used as a machine learning algorithm, both for individual feature sets and for decision-level fusion. Detection performance is measured by the out-of-bag equal error rate (EER) and the cost of log-likelihood-ratio. Essentia audio feature set was the best using the AC speech modality and YAAFE audio feature set was the best using the SP unvoiced modality, achieving EER of 20.30% and 25.57%, respectively. Fusion of all feature sets and modalities resulted in EER of 19.27% for the AC and 23.00% for the SP channel. Non-linear projection of a RF-based proximity matrix into the 2D space enriched medical decision support by visualization. © 2017 Vaiciukynas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</p> +
<p>A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.</p> +
<p>A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.</p><p>Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.</p><p>The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.</p> +
<p>Various intelligent systems show a rapidly growing potential use of visual information processing technologies. This paper presents an example of employing visual information processing technologies for detecting and measuring rings in banknote images. The size of the rings is one of parameters used to inspect the banknote printing quality. The approach developed consists of two phases. In the first phase, based on histogram processing and data clustering, image areas containing rings are localized and edges of the rings are detected. Then, in the second phase, applying the hard and possibilistic spherical shell clustering to the extracted edge pixels the ring centre and radii are estimated. The experimental investigations performed have shown that even highly occluded rings are robustly detected. Several prototypes of the system developed have been installed in two banknote printing shops in Europe.</p> +
