A wearable system for in-home and long-term assessment of fetal movement

International audienceObjectives: This paper presents a novel wearable system for in-home and long-term fetal movementmonitoring on a reliable and easily accessible basis.Material and methods: The system mainly consists of four accelerometers for fetal movement signalacquisition, a microcontroller for signal processing and an Android-based device interacting with the mi-crocontroller via Bluetooth Low Energy (BLE), providing the mother with information related to the fetalmovement in an intelligible way.Results: The proposed system can deliver reliable results with a specicity of 0.99 and a sensitivity of0.77 for fetal movement time series signal classication.Conclusion: The proposed wearable system will provide a good alternative to optimize the use of medicalprofessionals and hospital resources, and has potential applications in the eld of e-Health home care.Besides, the fetal movement acceleration signals acquired with volunteers (pregnant women) helps establishan initial database for future medical analysis of sensor-recorded fetal behaviors

Lightweight Transformer in Federated Setting for Human Activity Recognition

Human activity recognition (HAR) is a machine learning task with applications in many domains including health care, but it has proven a challenging research problem. In health care, it is used mainly as an assistive technology for elder care, often used together with other related technologies such as the Internet of Things (IoT) because HAR can be achieved with the help of IoT devices such as smartphones, wearables, environmental and on-body sensors. Deep neural network techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used for HAR, both in centralized and federated settings. However, these techniques have certain limitations: RNNs cannot be easily parallelized, CNNs have the limitation of sequence length, and both are computationally expensive. Moreover, the centralized approach has privacy concerns when facing sensitive applications such as healthcare. In this paper, to address some of the existing challenges facing HAR, we present a novel one-patch transformer based on inertial sensors that can combine the advantages of RNNs and CNNs without their major limitations. We designed a testbed to collect real-time human activity data and used the data to train and test the proposed transformer-based HAR classifier. We also propose TransFed: a federated learning-based HAR classifier using the proposed transformer to address privacy concerns. The experimental results showed that the proposed solution outperformed the state-of-the-art HAR classifiers based on CNNs and RNNs, in both federated and centralized settings. Moreover, the proposed HAR classifier is computationally inexpensive as it uses much fewer parameters than existing CNN/RNN-based classifiers.Comment: An updated version of this paper is coming soo

Proof of Swarm Based Ensemble Learning for Federated Learning Applications

Ensemble learning combines results from multiple machine learning models in order to provide a better and optimised predictive model with reduced bias, variance and improved predictions. However, in federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Hence, a mechanism is required to combine results of local models to produce a global model. Most distributed consensus algorithms, such as Byzantine fault tolerance (BFT), do not normally perform well in such applications. This is because, in such methods predictions of some of the peers are disregarded, so a majority of peers can win without even considering other peers' decisions. Additionally, the confidence score of the result of each peer is not normally taken into account, although it is an important feature to consider for ensemble learning. Moreover, the problem of a tie event is often left un-addressed by methods such as BFT. To fill these research gaps, we propose PoSw (Proof of Swarm), a novel distributed consensus algorithm for ensemble learning in a federated setting, which was inspired by particle swarm based algorithms for solving optimisation problems. The proposed algorithm is theoretically proved to always converge in a relatively small number of steps and has mechanisms to resolve tie events while trying to achieve sub-optimum solutions. We experimentally validated the performance of the proposed algorithm using ECG classification as an example application in healthcare, showing that the ensemble learning model outperformed all local models and even the FL-based global model

Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI

Deep learning plays a vital role in classifying different arrhythmias using electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, to address the above-mentioned challenges, we design a novel end-to-end framework in a federated setting for ECG-based healthcare using explainable artificial intelligence (XAI) and deep convolutional neural networks (CNN). The federated setting is used to solve challenges such as data availability and privacy concerns. Furthermore, the proposed framework effectively classifies different arrhythmias using an autoencoder and a classifier, both based on a CNN. Additionally, we propose an XAI-based module on top of the proposed classifier for interpretability of the classification results, which helps clinical practitioners to interpret the predictions of the classifier and to make quick and reliable decisions. The proposed framework was trained and tested using the baseline Massachusetts Institute of Technology - Boston's Beth Israel Hospital (MIT-BIH) Arrhythmia database. The trained classifier outperformed existing work by achieving accuracy up to 94.5% and 98.9% for arrhythmia detection using noisy and clean data, respectively, with five-fold cross-validation. We also propose a new communication cost reduction method to reduce the communication costs and to enhance the privacy of users' data in the federated setting. While the proposed framework was tested and validated for ECG classification, it is general enough to be extended to many other healthcare applications

Contribution à l'évaluation et à la modélisation du bien-être des matériaux textiles habillement par l'utilisation des techniques de calcul avancé

Aujourd hui, stressés par des pressions multiples du travail et de la vie quotidienne, les consommateurs aspirent résolument au bien-être. En même temps, face à la concurrence de plus en plus intensive, l exploitation des besoins des consommateurs et le développement de nouveaux produits personnalisés à réactivité rapide et au coût réduit constituent des stratégies prioritaires pour la plupart des entreprises industrielles. Dans cette situation, la maîtrise des descripteurs sensoriels et émotionnels des produits autour de la notion du bien-être permettra de satisfaire au maximum les spécifications et la cohérence de l image véhiculée par la marque ainsi que les attentes de plus en plus complexes issues de l analyse du marché.Dans le but d apporter notre contribution aux outils du prototypage rapide et à l écoute des besoins industriels, ce mémoire présente les travaux de recherche sur la caractérisation des critères du bien-être des consommateurs dans le domaine textile et d habillement, permettant de réaliser rapidement des prototypes adaptés aux leurs besoins. Une série d outils est proposée, constituant un système d aide à la décision, permettant aux concepteurs de sélectionner des paramètres physiques de conception pertinents, de déterminer les espaces de fonctionnement (les intervalles acceptables des paramètres physiques de conception sélectionnés), de modéliser la relation entre le bien-être et les composants de conception (toucher, style et couleur), et d évaluer globalement la qualité des prototypes sur plusieurs niveaux d appréciation.Today, anxious by multiple pressures of job and daily life, consumers aspire resolutely to the well-being. At the same time, with more and more intensive competition, the exploitation of consumer requirements and the development of new personalized products with quick reactivity and reduced expense constitute priority strategies for most of the industries. In this situation, the mastery of the sensory and emotional descriptors of products around the well-being will allow to satisfy at the maximum the specifications and coherence of the brand image as well as more and more complex expectation concluded by the market analysis.To contribute in the rapid prototyping tools and satisfy the industrial requirements, this thesis introduces research works on the characterization of criteria of the consumers well-being in textile and apparel field, allowing to realise prototypes adapted to their requirements rapidly. A series of tools are proposed, constituting a decision support system, allowing the designers to choose appropriate physical design parameters, to determine the feasible operation setting space (the setting interval of the chosen physical design parameters), to model the relationship between well-being and design components (fabric hand, style and color), and to evaluate globally the quality of prototypes at several levels.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF

CLASSIFICATION AND MEASURE OF QUANTITATIVE DIFFERENCE BETWEEN POLYESTER AND COTTON FABRICS BASED ON SENSORY ANALYSIS

In this study we compare cotton and polyester (Polyethylene terephthalate) (PET) sensory attributes, as a precursor for sensory modification of polyester, for cotton replacement. We systematically identify the key sensory attributes that distinguish cotton from polyester fabrics. Rank Aggregation, Principal Component Analysis (PCA), Agglomerative Hierarchical Clustering (AHC), and the measure of distances are used to process elicited dat

Etude de relations entre les perceptions visuelles et haptiques des produits textiles

Pour ces travaux de thèse, nous proposons pour la première fois une méthodologie systématique pour étudier les propriétés tactiles de tissu au travers de perceptions visuelles. Tout d abord, nous étudions les bases physiologiques et cognitives des perceptions visuelles et haptiques des propriétés tactiles des tissus. Ensuite, une hypothèse fondamentale est proposée pour que les propriétés tactiles des tissus puissent être interprétées à travers nos yeux. Afin de vérifier cette hypothèse, des expériences sensorielles ont été conduites sur un nombre important de produits textiles selon trois différents scénarii : vidéo, image et toucher réel. Une nouvelle approche basée sur le concept de degré d inclusion est développée pour étudier les relations entre les données tactiles obtenues à partir des différentes modalités sensorielles. De cette manière, nous concluons qu il est tout à fait possible de percevoir les propriétés tactiles des tissus à travers des représentations visuelles. Ceci confirme l hypothèse proposée précédemment. En nous appuyant sur ces résultats, afin d explorer le mécanisme interprétatif de la vision, nous effectuons de nouvelles expériences sensorielles, permettant d évaluer respectivement les caractéristiques visuelles et les propriétés tactiles des échantillons. Ensuite, nous modifions l approche mathématique proposée précédemment afin de mesurer les relations de type un à plusieurs, de manière à extraire pour chaque propriété tactile d un ensemble de caractéristiques visuelles les plus pertinentes. Enfin, ANFIS (un réseau neuronal combinant les techniques floues) est utilisé pour modéliser et interpréter quantitativement ces relations.In the current thesis, we propose for the first time a systematic methodology to study fabric tactile properties through visual perceptions. First of all, we investigate the physiological and cognitive basis of visual and haptic perceptions of fabric tactile properties. Next, we propose a fundamental hypothesis that fabric tactile properties can be, to a big extent, interpreted through our eyes. In order to verify this hypothesis, sensory experiments are carried out on a number of textile products in video, image and real-touch scenarios. A novel approach based on the concept of inclusion degree is developed to study the relations between the tactile data obtained from different sensory modalities. From this study, we conclude that it is possible to perceive fabric tactile properties through visual representations, which confirms the previously proposed hypothesis. On this basis, in order to further explore the visual interpretative mechanism, new sensory experiments are organized to evaluate samples visual features and tactile properties, respectively. The previously proposed mathematical approach is modified to be able to measure multiple-to-single relations so as to extract for each tactile property a set of relevant visual features on it. Finally, a fuzzy neural network (Adaptive network-based fuzzy inference system, in short ANFIS) is developed to model the obtained interpretative relationships.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF

Contribution au développement d'un système intelligent d'aide à la création de styles personnalisés

La mass customisation a été appliquée au marché de grande consommation de vêtements depuis plus de 20 ans. Pourtant, les travaux concernés se focalisent essentiellement sur le prototypage virtuel par utilisation des outils de CAO. Le stylisme et le marketing n ont pas été étudiés de façon systématique. Dans le cadre de ma thèse doctorale, nous proposons un système d aide à la décision orienté vers les styles afin de fournir des conseils aux créateurs. Dans ce système, nous caractérisons, d abord, les perceptions de créateurs et de consommateurs sur les morphotypes. Deux expériences ont été effectuées afin d acquérir les données des experts (descripteurs sensoriels) décrivant les corps virtuels sans et avec styles de vêtements. Une autre expérience a été réalisée pour extraire les données des consommateurs sur les relations entre les thèmes (images socioculturelles souhaités) et les descripteurs sensoriels. Ensuite, ces données perceptuelles sont formalisées et analysées par utilisation des ensembles du flou, des arbres de décision, et des cartes cognitives floues. La modélisation des relations entre ces perceptions et les mensurations du corps permettent de calculer les degrés de pertinence d un corps humain sans et avec style de vêtement par rapport à un thème spécifique. La comparaison de ces deux degrés de pertinence permet de déterminer si un nouveau style de création est faisable pour un thème donné. Le système proposé a été testé et analysé dans deux cas réels : la création des styles personnalisés et la sélection des styles pour un marché de grande consommation.Mass customization has been applied in fashion mass market for more than 20 years. However, the related work mainly focuses on application of CAD tools such as body shape modeling and garment modeling. Fashion design and fashion marketing have not been involved systematically. In fact, when developing mass customized products, we should study human perception on products, including consumer s and design expert s perception, and integrate it into the new process of design.In my PhD research project, we originally propose a fashion decision support system for supporting designer s work. In this system, we first characterize and acquire fashion expert perception and consumer perception on human body shapes. Two experiments are proposed in order to acquire expert perceptual data (sensory descriptors) on naked virtual body shapes and those with garment design styles. Another experiment is carried out for acquiring consumer perceptual data on relations between fashion themes (images desired by general public) and sensory descriptors. Next, these perceptual data are formalized and analyzed using the intelligent techniques, i.e. fuzzy set theory, decision tree and fuzzy cognitive map. The complex relations between these perceptions as well as the physical measurements of body shapes are modeled, leading to compute the relevancy degrees of a naked body and a body with a garment design style to a given fashion theme. The comparison of these two relevancy degrees will permit to determine if a new design style is feasible or not for a given fashion theme. The proposed system has been tested and analyzed in two real cases: i.e. customized design and mass market selection.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF