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This paper presents an efficient deep neural network model for diagnosing Parkinson's disease from gait. More specifically, we introduce a hybrid ConvNet-Transformer architecture to accurately diagnose the disease by detecting the severity stage. The proposed architecture exploits the strengths of both convolutional neural networks and Transformers in a single end-to-end model, where the former is able to extract relevant local features from Vertical Ground Reaction Force (VGRF) signal, while the latter allows to capture long-term spatio-temporal dependencies in data. In this manner, our hybrid architecture achieves an improved performance compared to using either models individually. Our experimental results show that our approach is effective for detecting the different stages of Parkinson's disease from gait data, with a final accuracy of 88%, outperforming other state-of-the-art AI methods on the Physionet gait dataset. Moreover, our method can be generalized and adapted for other classification problems to jointly address the feature relevance and spatio-temporal dependency problems in 1D signals. Our source code and pre-trained models are publicly available at https://github.com/SafwenNaimi. [ABSTRACT FROM AUTHOR]

Accurate and efficient orchard tree inventories are essential for acquiring up-to-date information, which is necessary for effective treatments and crop insurance purposes. Surveying orchard trees, including tasks such as counting, locating, and assessing health status, plays a vital role in predicting production volumes and facilitating orchard management. However, traditional manual inventories are known to be labor-intensive, expensive, and prone to errors. Motivated by recent advancements in UAV imagery and computer vision methods, we propose a UAV-based computer vision framework for individual tree detection and health assessment. Our proposed approach follows a two-stage process. Firstly, we propose a tree detection model by employing a hard negative mining strategy using RGB UAV images. Subsequently, we address the health classification problem by leveraging multi-band imagery-derived vegetation indices. The proposed framework achieves an F1-score of 86.24% for tree detection and an overall accuracy of 97.52% for tree health assessment. Our study demonstrates the robustness of the proposed framework in accurately assessing orchard tree health from UAV images. Moreover, the proposed approach holds potential for application in various other plantation settings, enabling plant detection and health assessment using UAV imagery. [ABSTRACT FROM AUTHOR]

Suicide by hanging is a sentinel event and a major cause of death in prisons, with an increasing frequency over recent years. The rapid detection of suicidal behavior can reduce the mortality rate and increase the odds of survival for the suicide victim. Significant efforts have been made to develop technologies for preventing hanging attempts, but most of them use cumbersome devices, or they are mainly depending on human attention and intervention. In this paper, we propose a vision-based method to automatically detect suicide by hanging. Our intelligent video surveillance system operates using depth streams provided by an RGB-D camera, regardless of illumination conditions. The proposed algorithm is based on the exploitation of the body joints’positions to model suicidal behavior. Both dynamic and static pose characteristics are calculated in order to efficiently capture the body joints’movement and model suicidal behavior. Results from the experiments on realistic video sequences, show that our system achieves a high accuracy in detecting suicide attempts, while meeting real-time requirements. [ABSTRACT FROM AUTHOR]

This paper presents a novel structure-aware method for visual tracking. The proposed tracker relies on keypoint regions as salient and stable elements that encode the object structure efficiently. In addition to the object structural properties, the appearance model also includes global color features that we first use in a probabilistic approach to reduce the search space. The second step of our tracking procedure is based on keypoint matching to provide a preliminary prediction of the target state. Final prediction is then achieved by exploiting object structural constraints, where target keypoints vote for the corrected object location. Once the object location is obtained, we update the appearance model and structural properties, allowing tracking targets with changing appearance and non-rigid structures. Extensive experiments demonstrate that the proposed Structure-Aware Tracker (SAT) outperforms recent state-of-the-art trackers in challenging scenarios, especially when the target is partly occluded and in moderately crowded scenes. [ABSTRACT FROM AUTHOR]

Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We present a Multiple Features-Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. Since convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to obtain a richer and more efficient representation of the target. Moreover, we handle the target appearance variations by calibrating the deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks.The source code and trained models are available at . [ABSTRACT FROM AUTHOR]

We propose an alternative to the generative classifier that usually models both the class conditionals and class priors separately, and then uses the Bayes theorem to compute the posterior distribution of classes given the training set as a decision boundary. Because SVM (support vector machine) is not a probabilistic framework, it is really difficult to implement a direct posterior distribution-based discriminative classifier. As SVM lacks in full Bayesian analysis, we propose a hybrid (generative–discriminative) technique where the generative topic features from a Bayesian learning are fed to the SVM. The standard latent Dirichlet allocation topic model with its Dirichlet (Dir) prior could be defined as Dir–Dir topic model to characterize the Dirichlet placed on the document and corpus parameters. With very flexible conjugate priors to the multinomials such as generalized Dirichlet (GD) and Beta-Liouville (BL) in our proposed approach, we define two new topic models: the BL–GD and GD–BL. We take advantage of the geometric interpretation of our generative topic (latent) models that associate a K-dimensional manifold (K is the size of the topics) embedded into a V-dimensional feature space (word simplex) where V is the vocabulary size. Under this structure, the low-dimensional topic simplex (the subspace) defines a document as a single point on its manifold and associates each document with a single probability. The SVM, with its kernel trick, performs on these document probabilities in classification where it utilizes the maximum margin learning approach as a decision boundary. The key note is that points or documents that are close to each other on the manifold must belong to the same class. Experimental results with text documents and images show the merits of the proposed framework. [ABSTRACT FROM AUTHOR]

Highlights • A novel computer vision system for intensive care respiratory monitoring. • An efficient positioning for the cameras in an intensive care room is proposed. • Our system offers a very high spatial coverage of the thoraco-abdominal zone. • Our system is evaluated by comparison to a mechanical ventilator. • The experiments demonstrate a high performance in estimating breathing parameters. Abstract Assessment of respiratory activity in pediatric intensive care unit allows a comprehensive view of the patient's condition. This allows the identification of high-risk cases for prompt and appropriate medical treatment. Numerous research works on respiration monitoring have been conducted in recent years. However, most of them are unsuitable for clinical environment or require physical contact with the patient, which limits their efficiency. In this paper, we present a novel system for measuring the breathing pattern based on a computer vision method and contactless design. Our 3D imaging system is specifically designed for pediatric intensive care environment, which distinguishes it from the other imaging methods. Indeed, previous works are mostly limited to the use of conventional video acquisition devices, in addition to not considering the constraints imposed by intensive care environment. The proposed system uses depth information captured by two (Red Green Blue-Depth) RGB-D cameras at different view angles, by considering the intensive care unit constraints. Depth information is then exploited to reconstruct a 3D surface of a patient's torso with high temporal and spatial resolution and large spatial coverage. Our system captures the motion information for the top of the torso surface as well as for its both lateral sides. For each reconstruction, the volume is estimated through a recursive subdivision of the 3D space into cubic unit elements. The volume change is then calculated through a subtraction technique between successive reconstructions. We tested our system in the pediatric intensive care unit of the Sainte-Justine university hospital center, where it was compared to the gold standard method currently used in pediatric intensive care units. The performed experiments showed a very high accuracy and precision of the proposed imaging system in estimating respiratory rate and tidal volume. [ABSTRACT FROM AUTHOR]

• A novel method for automatic detection of Parkinson disease and severity prediction. • 1D-Convnet architecture specifically designed to process signals from foot sensors. • Parkinsonian gate is detected without manual feature extraction. • We achieved improved accuracy compared to recent state-of-the art algorithms. Diagnosing Parkinson's disease is a complex task that requires the evaluation of several motor and non-motor symptoms. During diagnosis, gait abnormalities are among the important symptoms that physicians should consider. However, gait evaluation is challenging and relies on the expertise and subjectivity of clinicians. In this context, the use of an intelligent gait analysis algorithm may assist physicians in order to facilitate the diagnosis process. This paper proposes a novel intelligent Parkinson detection system based on deep learning techniques to analyze gait information. We used 1D convolutional neural network (1D-Convnet) to build a Deep Neural Network (DNN) classifier. The proposed model processes 18 1D-signals coming from foot sensors measuring the vertical ground reaction force (VGRF). The first part of the network consists of 18 parallel 1D-Convnet corresponding to system inputs. The second part is a fully connected network that connects the concatenated outputs of the 1D-Convnets to obtain a final classification. We tested our algorithm in Parkinson's detection and in the prediction of the severity of the disease with the Unified Parkinson's Disease Rating Scale (UPDRS). Our experiments demonstrate the high efficiency of the proposed method in the detection of Parkinson disease based on gait data. The proposed algorithm achieved an accuracy of 98.7%. To our knowledge, this is the state-of-the-start performance in Parkinson's gait recognition. Furthermore, we achieved an accuracy of 85.3% in Parkinson's severity prediction. To the best of our knowledge, this is the first algorithm to perform a severity prediction based on the UPDRS. These results show that the model is able to learn intrinsic characteristics from gait data and to generalize to unseen subjects, which could be helpful in a clinical diagnosis. [ABSTRACT FROM AUTHOR]

Background: In an intensive care units, experts in mechanical ventilation are not continuously at patient’s bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap. Objective: The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change. Data sources: Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (< 84%, 85% to 91% and c92% to 100%). Performance metrics of predictive models: Due to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with area under the curves < 0.75. Conclusion: This single center pilot study using machine learning predictive model resulted in an algorithm with poor accuracy. The comparison of machine learning models showed that bagged complex trees was a promising approach. However, there is a need to improve these models before incorporating them into a clinical decision support systems. One potentially solution for improving predictive model, would be to increase the amount of data available to limit over-fitting that is potentially one of the cause for poor classification performances for 2 of the three class labels. [ABSTRACT FROM AUTHOR]