A pneumatic reconfigurable socket for transtibial amputees.

Many transtibial amputees rate the fit between their residual limb and prosthetic socket as the most critical factor in satisfaction with using their prosthesis. This study aims to address the issue of prosthetic socket fit by reconfiguring the socket shape at the interface of the residual limb and socket. The proposed reconfigurable socket shifts pressure from sensitive areas and compensates for residual limb volume fluctuations, the most important factors in determining a good socket fit. Computed tomography scan images are employed to create the phantom limb of an amputee and to manufacture the reconfigurable socket. The performance of the reconfigurable socket was evaluated both experimentally and numerically using finite element modelling. The study showed that the reconfigurable socket can reduce interface pressure at targeted areas by up to 61%.

Electromyographic signals analysis to assess the response of a proprioceptive neuromuscular facilitation pattern execution.

Proprioceptive Neuromuscular Facilitation is a rehabilitation technique that consists of the stimulation of a healthy muscle in one extremity of the body to produce an activation effect of a damaged muscle in another extremity, laterally or contralaterally. The use of the analysis of the electromyographic response during the process allows us to describe and evaluate if the damaged muscle produces an activation. This paper presents the progress of the results of a clinical protocol where PNF is explored in healthy subjects, manipulating the upper limb, and recording the electromyographic response of the lower limbs in three different muscles in both inferior limbs. Four activation patterns (movement sequence) with three different stages with different intensities of resistance are considered. Lateral plane video recording analysis is performed to extract the trajectory of the manipulated limb and correlate this information with electromyography signals. The electromyography signals are analyzed in their temporal response by integrating the rectified and smoothed signal according to the beginning and end of the trajectory of the manipulated superior limb. Additionally, the spectrogram analysis of the signals is performed. The partial results of the protocol allow us to establish that the response in the most distal muscles to the manipulated upper limb has been non-significant for the subjects, compared to the response of the proximal muscles, where a relationship with activation and trajectory of the arm is observed. particularly in the process with greater resistance. The observed changes in signal frequency across the spectrogram correspond to low-frequency changes in the 15 to 20Hz band.Clinical Relevance- This work presents a strategy to assess the rehabilitation process based on proprioceptive neuromuscular facilitation by analyzing the time and time-frequency response of the electromyographic signal.

Blackberry Fruit Classification in Underexposed Images Combining Deep Learning and Image Fusion Methods.

Berry production is increasing worldwide each year; however, high production leads to labor shortages and an increase in wasted fruit during harvest seasons. This problem opened new research opportunities in computer vision as one main challenge to address is the uncontrolled light conditions in greenhouses and open fields. The high light variations between zones can lead to underexposure of the regions of interest, making it difficult to classify between vegetation, ripe, and unripe blackberries due to their black color. Therefore, the aim of this work is to automate the process of classifying the ripeness stages of blackberries in normal and low-light conditions by exploring the use of image fusion methods to improve the quality of the input image before the inference process. The proposed algorithm adds information from three sources: visible, an improved version of the visible, and a sensor that captures images in the near-infrared spectra, obtaining a mean F1 score of 0.909±0.074 and 0.962±0.028 in underexposed images, without and with model fine-tuning, respectively, which in some cases is an increase of up to 12% in the classification rates. Furthermore, the analysis of the fusion metrics showed that the method could be used in outdoor images to enhance their quality; the weighted fusion helps to improve only underexposed vegetation, improving the contrast of objects in the image without significant changes in saturation and colorfulness.

Design and Analysis of a Rotational Mixer to produce 3D Bioprinting Gyroid-Helical-Patterned Scaffolds for Tissue Engineering Applications.

Tissue engineering scaffolds require complex networks for nutrient diffusion and cell attachment. They must have specific surface area and curvature, and often need a multimaterial composition, demanding advanced micro-fabrication methods. 3D extrusion bioprinting offers versatility to manufacture different scaffold, and strategies for multimaterial printing have been introduced. We propose a method to fabricate scaffolds based on gyroid-helical-patterned microfibers, providing a platform to study the effect of the gyroid minimum curvature on cellular processes, since the geometry wont be layer-by-layer approximated. The pattern is obtained by mixing inks using a gyroid-helix shaped rotational mixer, modifying the extruder of a conventional 3D printer. The mixer was simulated using computational fluid dynamics tools, varying the volumetric flow to obtain different gyroid-thickness. Due to its surface area minimization, it shows lower energy requirements than state-of-art fluid mixers, with a pressure drop of 1.7%, a power number of 39, and a rotation-induced shear stress of ∼400 Pa, enabling the use of cell-embedded bioinks.

A scoping review of the relationship between alcohol, memory consolidation and ripple activity: An overview of common methodologies to analyse ripples.

Alcohol abuse is not only responsible for 5.3% of the total deaths in the world but also has a substantial impact on neurological and memory disabilities throughout the population. One extensively studied brain area involved in cognitive functions is the hippocampus. Evidence in several rodent models has shown that ethanol produces cognitive impairment in hippocampal-dependent tasks and that the damage is varied according to the stage of development at which the rodent was exposed to ethanol and the dose. To the authors' knowledge, there is a biomarker for cognitive processes in the hippocampus that remains relatively understudied in association with memory impairment by alcohol administration. This biomarker is called sharp wave-ripples (SWRs) which are synchronous neuronal population events that are well known to be involved in memory consolidation. Methodologies for facilitated or automatic identification of ripples and their analysis have been reported for a wider bandwidth than SWRs. This review is focused on communicating the state of the art about the relationship between alcohol, memory consolidation and ripple activity, as well as the use of the common methodologies to identify SWRs automatically.

Review on Porous Scaffolds Generation Process: A Tissue Engineering Approach.

Porous scaffolds have been widely explored for tissue regeneration and engineering in vitro three-dimensional models. In this review, a comprehensive literature analysis is conducted to identify the steps involved in their generation. The advantages and disadvantages of the available techniques are discussed, highlighting the importance of considering pore geometrical parameters such as curvature and size, and summarizing the requirements to generate the porous scaffold according to the desired application. This paper considers the available design tools, mathematical models, materials, fabrication techniques, cell seeding methodologies, assessment methods, and the status of pore scaffolds in clinical applications. This review compiles the relevant research in the field in the past years. The trends, challenges, and future research directions are discussed in the search for the generation of a porous scaffold with improved mechanical and biological properties that can be reproducible, viable for long-term studies, and closer to being used in the clinical field.

A Novel Methodology for Classifying EMG Movements Based on SVM and Genetic Algorithms.

Electromyography (EMG) processing is a fundamental part of medical research. It offers the possibility of developing new devices and techniques for the diagnosis, treatment, care, and rehabilitation of patients, in most cases non-invasively. However, EMG signals are random, non-stationary, and non-linear, making their classification difficult. Due to this, it is of vital importance to define which factors are helpful for the classification process. In order to improve this process, it is possible to apply algorithms capable of identifying which features are most important in the categorization process. Algorithms based on metaheuristic methods have demonstrated an ability to search for suitable subsets of features for optimization problems. Therefore, this work proposes a methodology based on genetic algorithms for feature selection to find the parameter space that offers the slightest classification error in 250 ms signal segments. For classification, a support vector machine is used. For this work, two databases were used, the first corresponding to the right upper extremity and the second formed by movements of the right lower extremity. For both databases, a feature space reduction of over 65% was obtained, with a higher average classification efficiency of 91% for the best subset of parameters. In addition, particle swarm optimization (PSO) was applied based on right upper extremity data, obtaining an 88% average error and a 46% reduction for the best subset of parameters. Finally, a sensitivity analysis was applied to the characteristics selected by PSO and genetic algorithms for the database of the right upper extremity, obtaining that the parameters determined by the genetic algorithms show greater sensitivity for the classification process.

Bone Tissue Scaffolds Designed With A Porosity Gradient Based On Triply Periodic Minimal Surfaces Using A Parametric Approach.

Recently, the interest in porous scaffolds design for cell culture has increased. Because of the curvotaxis property of the cells, they can respond to the curvature of the substrate in which they are seeded, like changing their morphology, despite that, curvature is little explored within scaffold design. What is more, for bone regeneration, the scaffold should ideally have a porosity gradient corresponding to the transition between compact and cancellous bone. Various studies have focused on finding the best geometry to mimic it, being the triple periodic minimum structures (TPMS) the most promising ones. However, as they are mathematically complex, researchers have approximated them with implicit equations, no longer respecting their minimum curvature when they vary the pore size, deforming the original geometry. This work proposed to approach the TPMS with parametric equations, finding an exact fundamental patch. In this way showing its potential to make customized structures with a porous gradient, thanks to the acquisition of a constant of variation. This generates a friendly user interface for the design of scaffolds. The work also presents a comparison with the implicit structures, remarking the benefits of using the parametric approach. Finally, it presents examples of 3D printed designs.

Control of differential-drive mobile robots for soft object deformation.

The aim of this work is a control scheme implementation to deform a nonrigid object in which deformation dynamics are modeled by the finite element method. The deformation of a soft object is highly difficult to model because of its non-linearity, time-dependency, and material-response characteristics. Thus, the control implementation for Differential Drive Mobile Robots (DDMR) to deform an elastic object, is a challenge. The proposed steps to solve it are: Position-control designed over DDMR kinematics. Alignment-control applied for DDMRs orientation. The desired shape of the object is achieved using two contact points as the control nodes. A centralized vision algorithm was employed in each stage to obtain positions. To show the usefulness of the proposed scheme, numerical simulation, and real-time implementation were carried out.

Design of an Auxiliary Artificial Lymphatic Vessel in Treatment of Secondary Lymphedema Due to Breast Cancer.

Breast cancer is the most common malignant tumor that affects women in the United States, Europe, and Mexico. As an adverse effect when performing treatments for this condition, secondary lymphedema associated with breast cancer occurs in some cases. This complication occurs due to the interruption of lymphatic flow in the upper extremities in conjunction with other factors such as radiation, sedentary lifestyle, removal of lymph nodes, damage to lymphatic vessels, and others. This article reviews breast cancer incidence, mortality, and survival patterns, confirming that, specifically, lymphedema has high health, social, and economic impacts. Research demonstrates that it fundamentally affects women at an early age. In approximately a third of the cases, it becomes a chronic disease. Therefore, physical therapy is essential for a better quality of life in patients who survive this disease. Surgeries and manual and pharmacological treatments are the current procedures done to reduce to reduce the alterations suffered by patients with lymphedema; however, the success of the treatments depends on each patient's characteristics. To face this problem, the design of a lymphatic vessel has been proposed to assist the mechanical failure of the damaged lymphatic system. In this work, the design methodology used for the blueprint of the lymphatic vessel is presented, as well as the computer analysis of fluid simulation and the selection of the proposed material, resulting in the production of a micrometric design. In the future, it is expected that a surgeon will be able to implant the design of the vessel to restore lymph flow through the lymphatic system, thus helping to combat lymphedema.

Interface Pressure System to Compare the Functional Performance of Prosthetic Sockets during the Gait in People with Trans-Tibial Amputation.

The interface pressure between the residual limb and prosthetic socket has a significant effect on the amputee's mobility and level of comfort with their prosthesis. This paper presents a socket interface pressure (SIFP) system to compare the interface pressure differences during gait between two different types of prosthetic sockets for a transtibial amputee. The system evaluates the interface pressure in six critical regions of interest (CROI) of the lower limb amputee and identifies the peak pressures during certain moments of the gait cycle. The six sensors were attached to the residual limb in the CROIs before the participant with transtibial amputation donned a prosthetic socket. The interface pressure was monitored and recorded while the participant walked on a treadmill for 10 min at 1.4 m/s. The results show peak pressure differences of almost 0.22 kgf/cm2 between the sockets. It was observed that the peak pressure occurred at 50% of the stance phase of the gait cycle. This SIFP system may be used by prosthetists, physical therapists, amputation care centers, and researchers, as well as government and private regulators requiring comparison and evaluation of prosthetic components, components under development, and testing.

Adaptive Tracking Control of State Constraint Systems Based on Differential Neural Networks: A Barrier Lyapunov Function Approach.

The aim of this article is to investigate the trajectory tracking problem of systems with uncertain models and state restrictions using differential neural networks (DNNs). The adaptive control design considers the design of a nonparametric identifier based on a class of continuous artificial neural networks (ANNs). The design of adaptive controllers used the estimated weights on the identifier structure yielding a compensating structure and a linear correction element on the tracking error. The stability of both the identification and tracking errors, considering the DNN, uses a barrier Lyapunov function (BLF) that grow to infinity whenever its arguments approach some finite limits for the state satisfying some predefined ellipsoid bounds. The analysis guarantees the semi-globally uniformly ultimately bounded (SGUUB) solution for the tracking error, which implies the achievement of an invariant set. The suggested controller produces closed-loop bounded signals. This article also presents the comparison between the tracking states forced by the adaptive controller estimated with the DNN based on BLF and quadratic Lyapunov functions as well. The effectiveness of the proposal is demonstrated with a numerical example and an implementation in a real plant (mass-spring system). This comparison confirmed the superiority of the suggested controller based on the BLF using the estimates of the upper bounds for the system states.

Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. METHODS: To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. RESULTS: The classifier with the highest metrics was the four antibody-based Perfecto-Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial. CONCLUSIONS: Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification.

Hemodynamic Pattern Recognition During Deception Process Using Functional Near-infrared Spectroscopy.

Deception is considered a psychological process by which one individual deliberately attempts to convince another person to accept as true what the liar knows to be false. This paper presents the use of functional near-infrared spectroscopy for deception detection. This technique measures hemodynamic variations in the cortical regions induced by neural activations. The experimental setup involved a mock theft paradigm with ten subjects, where the subjects responded to a set of questions, with each of their answers belonging to one of three categories: Induced Lies, Induced Truths, and Non-Induced responses. The relative changes of the hemodynamic activity in the subject's prefrontal cortex were recorded during the experiment. From this data, the changes in blood volume were derived and represented as false color topograms. Finally, a human evaluator used these topograms as a guide to classify each answer into one of the three categories. His performance was compared with that of a support vector machine (SVM) classifier in terms of accuracy, specificity, and sensitivity. The human evaluator achieved an accuracy of 84.33 % in a tri-class problem and 92 % in a bi-class problem (induced vs. non-induced responses). In comparison, the SVM classifier correctly classified 95.63 % of the answers in a tri-class problem using cross-validation for the selection of the best features. These results suggest a tradeoff between accuracy and computational burden. In other words, it is possible for an interviewer to classify each response by only looking at the topogram of the hemodynamic activity, but at the cost of reduced prediction accuracy.