1,721,005 research outputs found
Enabling Technologies for Optimized Diagnosis, Therapy and Rehabilitation: Obtained Results and Future Works
No full textMulti-DoFs Exoskeleton-Based Bilateral Teleoperation with the Time-Domain Passivity Approach
No full textSummaryIt is well known that the sense of presence in a tele-robot system for both home-based tele-rehabilitation and rescue operations is enhanced by haptic feedback. Beyond several advantages, in the presence of communication delay haptic feedback can lead to an unstable teleoperation system. During the last decades, several control techniques have been proposed to ensure a good trade-off between transparency and stability in bilateral teleoperation systems under time delays. These proposed control approaches have been extensively tested with teleoperation systems based on identical master and slave robots having few degrees of freedom (DoF). However, a small number of DoFs cannot ensure both an effective restoration of the multi-joint coordination in tele-rehabilitation and an adequate dexterity during manipulation tasks in rescue scenario. Thus, a deep understanding of the applicability of such control techniques on a real bilateral teleoperation setup is needed. In this work, we investigated the behavior of the time-domain passivity approach (TDPA) applied on an asymmetrical teleoperator system composed by a 5-DoFs impedance designed upper-limb exoskeleton and a 4-DoFs admittance designed anthropomorphic robot. The conceived teleoperation architecture is based on a velocity-force (measured) architecture with position drift compensation and has been tested with a representative set of tasks under communication delay (80 ms round-trip). The results have shown that the TDPA is suitable for a multi-DoFs asymmetrical setup composed by two isomorphic haptic interfaces characterized by different mechanical features. The stability of the teleoperator has been proved during several (1) high-force contacts against stiff wall that involve more Cartesian axes simultaneously, (2) continuous contacts with a stiff edge tests, (3) heavy-load handling tests while following a predefined path and (4) high-force contacts against stiff wall while handling a load. The found results demonstrated that the TDPA could be used in several teleoperation scenarios like home-based tele-rehabilitation and rescue operations
Towards online myoelectric control based on muscle synergies-to-force mapping for robotic applications
No full textThe development of a functional myoelectric control represents a big challenge within the researchers community, due to the complexity of mapping the user’s movement intention onto the control signals. It is continuously gaining attention since it could be useful for building natural, intuitive and tailored human–machine interfaces. In this context, muscle synergies-based approaches are playing an important role since they may be useful to exploit the modular organization of the musculoskeletal system.
Muscle synergies-based myo-control schemes have shown promising results when they are trained and validated at the same limb pose. However, dealing with a muscle-to-force mapping variability across multiple limb poses remains an open challenge, thus keeping these techniques unusable in several real application scenarios, e.g. rehabilitation contexts.
In this paper, the authors propose a method able to compute the synergies-to-force mapping of a new limb pose by interpolation, with the knowledge of the synergies-to-force mapping related to a limited set of limb poses. The proposed interpolation-based approach has been evaluated on three different kind of mappings: muscle-to-force, “Pose-Shared” synergies-to-force and “Pose-Related” synergies-to-force. The muscle-to-force mapping considers a direct map between muscles and hand force. Both synergies-to-force approaches consider a map between muscle synergies and hand force, but, the “Pose-Shared” mapping assumes that the muscle patterns can be factorized using data coming from different limb poses, whereas the “Pose-Related” one assumes that each pose has its own set of muscle primitives that can be clustered together. The generalization capability of the proposed approach has been evaluated by comparing performances obtained in untrained conditions with the ones obtained in trained upper limb poses. Results showed that synergies-based approach substantially reduce the performance loss when tested on untrained upper-limb’s poses, demonstrating that muscle synergies may be suitable to be shared across different working conditions. Moreover, the feasibility of the proposed approach has been preliminary tested in an online condition, demonstrating that the subject was able to accomplish the force task by controlling a virtual cursor with his muscular activations
Computer vision and deep learning techniques for pedestrian detection and tracking: A survey
Full text linkPedestrian detection and tracking have become an important field in the computer vision research area. This growing interest, started in the last decades, might be explained by the multitude of potential applications that could use the results of this research field, e.g. robotics, entertainment, surveillance, care for the elderly and disabled, and content-based indexing. In this survey paper, vision-based pedestrian detection systems are analysed based on their field of application, acquisition technology, computer vision techniques and classification strategies. Three main application fields have been individuated and discussed: video surveillance, human-machine interaction and analysis. Due to the large variety of acquisition technologies, this paper discusses both the differences between 2D and 3D vision systems, and indoor and outdoor systems. The authors reserved a dedicated section for the analysis of the Deep Learning methodologies, including the Convolutional Neural Networks in pedestrian detection and tracking, considering their recent exploding adoption for such a kind systems. Finally, focusing on the classification point of view, different Machine Learning techniques have been analysed, basing the discussion on the classification performances on different benchmark datasets. The reported results highlight the importance of testing pedestrian detection systems on different datasets to evaluate the robustness of the computed groups of features used as input to classifiers
A linear optimization procedure for an EMG-driven neuromusculoskeletal model parameters adjusting: Validation through a myoelectric exoskeleton control
No full textThis paper presents a linear optimization procedure able to adapt a simplified EMG-driven NeuroMusculoSkeletal (NMS) model to the specific subject. The optimization procedure could be used to adjust a NMS model of a generic human articulation in order to predict the joint torque by using ElectroMyoGraphic (EMG) signals. The proposed approach was tested by modeling the human elbow joint with only two muscles. Using the cross-validation method, the adjusted elbow model has been validated in terms of both torque estimation performance and predictive ability. The experiments, conducted with healthy people, have shown both good performance and high robustness. Finally, the model was used to control directly and continuously a exoskeleton rehabilitation device through EMG signals. Data acquired during free movements prove the model ability to detect the human’s intention of movement
A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma
Full text linkBackground and Objective: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients’ follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project. Materials and Methods: Analyzed datasets included tumor-annotated radiologic images, clinical, and mutational data. A feature selection was based on univariate (UV) and multivariate (MV) survival analyses according to Overall Survival (OS) and recurrence (REC). In this study, we considered seven multi-omic datasets and compared four SML classifiers: Cox, survival random forest, generalized boosted, and support vector machines (SVM). For each classifier, we assessed the concordance (C) index on the validation set. The best classifiers for the validation set on both OS and REC underwent explainability analyses using SurvSHAP(t), which extends SHapley Additive exPlanations (SHAP). Results: According to OS, after UV and MV analyses we selected 18/37 and 10/37 multi-omic features, respectively. According to REC, based on UV and MV analyses we selected 10/35 and 5/35 determinants, respectively. Generally, SML classifiers including radiomics outperformed those modelled on clinical or mutational predictors. For OS, the Cox model encompassing radiomic, clinical, and mutational features reached 75 % of C index, outperforming other classifiers. On the other hand, for REC, the SVM model including only radiomics emerged as the best-performing, with 68 % of C index. For OS, SurvSHAP(t) identified the first order Median Gray Level (GL) intensities, the gender, the tumor grade, the Joint Energy GL Co-occurrence Matrix (GLCM), and the GLCM Informational Measures of Correlations of type 1 as the most important features. For REC, the first order Median GL intensities, the GL size zone matrix Small Area Low GL Emphasis, and first order variance of GL intensities emerged as the most discriminative. Conclusions: In this work, radiomics showed the potential for improving patients’ risk stratification in PDA. Furthermore, a deeper understanding of how radiomics can contribute to prognosis in PDA was achieved with a time-dependent explainability of the top multi-omic predictors
Deep learning for processing electromyographic signals: A taxonomy-based survey
No full textDeep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN
Deep Learning based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey
Full text linkThe recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of image-guided Robot Assisted Surgery (RAS) systems. By and large, the number of studies concerning image-driven computer assisted surgical systems using DL has increased exponentially. Additionally, the contemporary availability of surgical datasets has also boosted the DL applications in RAS. Inspired by the latest trends and contributions in surgery, this literature survey presents a summarized analysis of recent innovations of DL in image-guided RAS systems. After a thorough review, a sum of 184 articles are selected and grouped into four categories, based on the literature and the relevancy of the task in the articles, comprising 1) Surgical Tools, 2) Surgical Processes, 3) Surgical Surveillance, and 4) Surgical Performance. The survey also discusses publicly available surgical datasets and highlights the basics of the DL models. Furthermore, the legal, ethical, and technological challenges together with the intuitive predictions and recommendations related to the autonomous RAS systems are also presented. The study reveals that Convolutional Neural Network (CNN) is most widely adopted architecture, whereas, the JIGSAWS is most employed dataset in RAS. The study suggests fusing kinematic data along with image data, which produces better accuracy and precision, particularly in gesture and trajectory segmentation tasks. Additionally, CNN and long short term memory networks have shown remarkable performance, however, authors recommend employing these gigantic architectures only when simpler models have failed to produce satisfactory results. The simpler models, despite their limitations, are time and cost effective and yield considerable outcomes even on the smaller dataset
Combining an exoskeleton with 3D simulation in-the-loop
No full textBeyond robot hardware and control, one major element for
an efficient, constructive and safe mission of teleoperated robots
in disaster scenarios such as Fukushima is the quality of the
connection between operator and robot. In this contribution,
we present the concept of using an exoskeleton and utilizing 3D
simulation as a central interface component for the operator
to intuitively collaborate with mobile teleoperated robot
CALIMAR-GAN: An unpaired mask-guided attention network for metal artifact reduction in CT scans
Full text linkHigh-quality computed tomography (CT) scans are essential for accurate diagnostic and therapeutic decisions, but the presence of metal objects within the body can produce distortions that lower image quality. Deep learning (DL) approaches using image-to-image translation for metal artifact reduction (MAR) show promise over traditional methods but often introduce secondary artifacts. Additionally, most rely on paired simulated data due to limited availability of real paired clinical data, restricting evaluation on clinical scans to qualitative analysis. This work presents CALIMAR-GAN, a generative adversarial network (GAN) model that employs a guided attention mechanism and the linear interpolation algorithm to reduce artifacts using unpaired simulated and clinical data for targeted artifact reduction. Quantitative evaluations on simulated images demonstrated superior performance, achieving a PSNR of 31.7, SSIM of 0.877, and Fréchet inception distance (FID) of 22.1, outperforming state-of-the-art methods. On real clinical images, CALIMAR-GAN achieved the lowest FID (32.7), validated as a valuable complement to qualitative assessments through correlation with pixel-based metrics (r=−0.797 with PSNR, p<0.01; r=−0.767 with MS-SSIM, p<0.01). This work advances DL-based artifact reduction into clinical practice with high-fidelity reconstructions that enhance diagnostic accuracy and therapeutic outcomes. Code is available at https://github.com/roberto722/calimar-gan
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