CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network

Mobile phone data have recently become an attractive source of information about mobility behavior. Since cell phone data can be captured in a passive way for a large user population, they can be harnessed to collect well-sampled mobility information. In this paper, we propose CT-Mapper, an unsupervised algorithm that enables the mapping of mobile phone traces over a multimodal transport network. One of the main strengths of CT-Mapper is its capability to map noisy sparse cellular multimodal trajectories over a multilayer transportation network where the layers have different physical properties and not only to map trajectories associated with a single layer. Such a network is modeled by a large multilayer graph in which the nodes correspond to metro/train stations or road intersections and edges correspond to connections between them. The mapping problem is modeled by an unsupervised HMM where the observations correspond to sparse user mobile trajectories and the hidden states to the multilayer graph nodes. The HMM is unsupervised as the transition and emission probabilities are inferred using respectively the physical transportation properties and the information on the spatial coverage of antenna base stations. To evaluate CT-Mapper we collected cellular traces with their corresponding GPS trajectories for a group of volunteer users in Paris and vicinity (France). We show that CT-Mapper is able to accurately retrieve the real cell phone user paths despite the sparsity of the observed trace trajectories. Furthermore our transition probability model is up to 20% more accurate than other naive models.Comment: Under revision in Computer Communication Journa

AccEq-DRT: Planning Demand-Responsive Transit to reduce inequality of accessibility

Accessibility measures how well a location is connected to surrounding opportunities. We focus on accessibility provided by Public Transit (PT). There is an evident inequality in the distribution of accessibility between city centers or close to main transportation corridors and suburbs. In the latter, poor PT service leads to a chronic car-dependency. Demand-Responsive Transit (DRT) is better suited for low-density areas than conventional fixed-route PT. However, its potential to tackle accessibility inequality has not yet been exploited. On the contrary, planning DRT without care to inequality (as in the methods proposed so far) can further improve the accessibility gap in urban areas. To the best of our knowledge this paper is the first to propose a DRT planning strategy, which we call AccEq-DRT, aimed at reducing accessibility inequality, while ensuring overall efficiency. To this aim, we combine a graph representation of conventional PT and a Continuous Approximation (CA) model of DRT. The two are combined in the same multi-layer graph, on which we compute accessibility. We then devise a scoring function to estimate the need of each area for an improvement, appropriately weighting population density and accessibility. Finally, we provide a bilevel optimization method, where the upper level is a heuristic to allocate DRT buses, guided by the scoring function, and the lower level performs traffic assignment. Numerical results in a simplified model of Montreal show that inequality, measured with the Atkinson index, is reduced by up to 34\%. Keywords: DRT Public, Transportation, Accessibility, Continuous Approximation, Network DesignComment: 15 page

Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease.

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions

Finger-vein quality assessment by joint representation learning from grayscale and binary images

International audienceFinger-vein as a high security biometric characteristic has been widely investigated for verification. One of challenges in finger-vein recognition is the image-quality degradation as spurious and missing features in poor quality images can increase the verification error. Despite recent advances in finger-vein quality assessment, these solutions depend on domain knowledge. Training a deep Neural Network (DNN) based on the objective labels selected automatically has compromised this problem, but their performance is still limit because some quality attributes are ignored. In this work, we propose a DNN for representation learning from both grayscale and binary images to predict vein quality. In the proposed approach, the grayscale and binary images are directly input to DNN to learn the joint representations for quality assessment. Experimental results on one large public dataset demonstrates that the proposed method accurately identifies high and low quality images and outperforms other methods in terms of impact on equal error rate (EER) decreas

Deep representation for finger-vein image quality assessment

International audienceFinger-vein biometrics has been extensively investigated for personal authentication. One of the open issues in finger-vein verification is the lack of robustness against image quality degradation. Spurious and missing features in poor quality images may degrade the system performance. Despite recent advances in finger-vein quality assessment, current solutions depend on domain knowledge. In this work, we propose a deep Neural Network (DNN) for representation learning to predict image quality using very limited knowledge. Driven by the primary target of biometric quality assessment, i.e. verification error minimization, we assume that low quality images are falsely rejected in a verification system. Based on this assumption, the low and high quality images are labeled automatically. We then train a DNN on the resulting dataset to predict image quality. To further improve DNN's robustness, the finger vein image is divided into various patches, on which a patch-based DNN is trained. The deepest layers associated with the patches form together a complementary and an over-complete representation. Subsequently, the quality of each patch from a testing image is estimated and the quality scores from the image patches are conjointly input to P-SVM to boost quality assessment performance. To the best of our knowledge, this is the first proposed work of deep learning-based quality assessment, not only for finger vein biometrics, but also for other biometrics in general. The experimental results on two public finger-vein databases show that the proposed scheme accurately identifies high and low quality images and significantly outperforms existing approaches in terms of the impact on equal error rate (EER) decreas

Deep representation-based feature extraction and recovering for finger-vein verification

International audienceFinger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, we automatically identify two regions: a clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein patterns in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accurac

Multimodal sequential modeling and recognition of human activities

International audienceVideo-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support independent living of old people. In this work, we propose a new multimodal ADL recognition method by modeling the correlation between motion and object information. We encode motion using dense interest point trajectories which are robust to occlusion and speed variability. We formulate the learning problem using a two-layer SVM hidden conditional random field (HCRF) recognition model that is particularly relevant for multimodal sequence recognition. This hierarchical classifier opti-mally combines the discriminative power of SVM and the long-range feature dependencies modeling by the HCR

Methods of pathology detection by speech analysis: survey

International audienceSpeech analysis can be used for healthcare tasks such as pathology detection. Conventionally, a speech-language pathologist is specialized to detect anomalies from speech. Speech disorders result from a variety of causes such as brain injury, stroke, hearing loss, developmental delay or emotion alteration. Content of the speech is often not of interest for pathology detection, but characteristics are. In the literature of automatic pathology detection by speech analysis, physiological pathologies such as nodule and cancer are taken into account along with neurodegenerative brain disorders such as Parkinson's disease, Alzheimer's disease and mild cognitive impairment. As the problem of pathology detection from speech has become a vast research area, comprehensive reviews are needed by researchers to contribute novel approaches. In this study, a literature survey on pathology detection is provided including data types, features, classification methods and accuracy rate

HMM-based gait modeling and recognition under different walking scenarios

International audienceThis paper addresses gait recognition, the problem of identifying people by the way of their walk. The proposed system consists of a model-free approach which extracts features directly from the human silhouette. The dynamics of the gait are modeled using Hidden Markov Models. Experiments have been carried out on the CASIA dataset C consisting of 153 people under four walking scenarios: normal walking, slow walking, fast walking and walking while carrying a bag. The results obtained are promising and compare favorably with existing approache