This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute5 (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases.
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Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.
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尽管最近的自动文本识别取得了进步,但在历史手稿方面,该性能仍然保持温和。这主要是因为缺乏可用的标记数据来训练渴望数据的手写文本识别(HTR)模型。由于错误率的降低,关键字发现系统(KWS)提供了HTR的有效替代方案,但通常仅限于封闭的参考词汇。在本文中,我们提出了一些学习范式,用于发现几个字符(n-gram)的序列,这些序列需要少量标记的训练数据。我们表明,对重要的n-gram的认识可以减少系统对词汇的依赖。在这种情况下,输入手写线图像中的vocabulary(OOV)单词可能是属于词典的n-gram序列。对我们提出的多代表方法进行了广泛的实验评估。
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The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.
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深度学习网络已在各种应用中表现出高性能,例如图像分类,语音识别和自然语言处理。但是,存在使用对抗攻击所利用的主要漏洞。对抗性攻击通过稍微稍微更改输入图像,使其对肉眼几乎无法检测到图像,但导致网络的分类非常不同。本文探讨了使用两种类型的体系结构:MobileNetV3和Resnet50探讨图像分割DeepLabV3模型上预计的梯度下降(PGD)攻击和自适应面膜分割攻击(ASMA),发现PGD在更改分割方面非常一致它的目标虽然ASMA对多类目标的概括不那么有效。然而,这种攻击的存在使所有图像分类深度学习网络处于剥削的危险之中。
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疟疾,一种致命但可治愈的疾病每年索赔数十万人生命。早期和正确的诊断对于避免健康复杂性至关重要,但这取决于昂贵的显微镜和培训专家分析血液涂抹幻灯片的可用性。基于深度学习的方法可能不仅可以降低专家的负担,而且还提高了低成本显微镜的诊断准确性。但是,由于没有合理的大小数据集,这是阻碍的。最具挑战性的方面之一是专家不愿意在低成本显微镜下以低放大率注释数据集。我们提出了一种数据集,以进一步研究低放大率低成本显微镜的疟疾显微镜。我们的大型数据集由来自几种疟疾感染患者的血液涂抹幻灯片的图像组成,通过显微镜在两种不同的成本谱和多个放大倍数中收集。用于在高放大率下通过高成本显微镜收集的图像的定位和寿命分类任务的疟原虫细胞。我们设计了一种机制,将这些注释从高倍率从高倍率转移到低成本显微镜,多倍放大。多个对象探测器和域适配方法作为基准。此外,引入了部分监督的域适配方法以使对象检测器适应从低成本显微镜收集的图像上的工作。该数据集将在发布后公开可用。
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深度神经网络已经显示出使用医学图像数据的疾病检测和分类结果。然而,他们仍然遭受处理真实世界场景的挑战,特别是可靠地检测分配(OOD)样本。我们提出了一种方法来强化皮肤和疟疾样本的ood样本,而无需在训练期间获得标记的OOD样品。具体而言,我们使用度量学习以及Logistic回归来强制深度网络学习众多丰富的类代表功能。要指导对OOD示例的学习过程,我们通过删除图像或置换图像部件中的类特定的突出区域并远离分布式样本来生成ID类似的示例。在推理时间期间,用于检测分布外样品的K +互易邻居。对于皮肤癌ood检测,我们使用两个标准基准皮肤癌症ISIC数据集AS ID,六种不同的数据集具有不同难度水平的数据集被视为出于分配。对于疟疾检测,我们使用BBBC041 Malaria DataSet作为ID和五个不同的具有挑战性的数据集,如分销。我们在先前的先前皮肤癌和疟疾OOD检测中,我们在TNR @ TPR95%中提高了最先进的结果,改善了5%和4%。
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