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Abstract The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10–10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

This study presents an innovative approach to predicting the water level in the San Juan River, Chocó, Colombia, by implementing two hybrid models: nonlinear auto-regressive with exogenous inputs (NARX) and long short-term memory (LSTM). These models combine artificial neural networks with smoothing techniques, including the exponential, Savitzky–Golay, and Rauch–Tung–Striebel (RTS) smoothing filters, with the aim of improving the accuracy of hydrological predictions. Given the high rainfall in the region, the San Juan River experiences significant fluctuations in its water levels, which presents a challenge for accurate prediction. The models were trained using historical data, and various smoothing techniques were applied to optimize data quality and reduce noise. The effectiveness of the models was evaluated using standard regression metrics, such as Nash–Sutcliffe efficiency (NSE), mean square error (MSE), and mean absolute error (MAE), in addition to Kling–Gupta efficiency (KGE). The results show that the combination of neural networks with smoothing filters, especially the RTS filter and smoothed Kalman filter, provided the most accurate predictions, outperforming traditional methods. This research has important implications for water resource management and flood prevention in vulnerable areas such as Chocó. The implementation of these hybrid models will allow local authorities to anticipate changes in water levels and plan preventive measures more effectively, thus reducing the risk of damage from extreme events. In summary, this study establishes a solid foundation for future research in water level prediction, highlighting the importance of integrating advanced technologies in water resources management.

This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed approach is presented in a direct-quadrature framework, where the quadrature current and the rotational speed are selected as outputs and the direct and quadrature voltages are selected as inputs. A nonlinear difference equation is selected to describe the physical dynamics of the PMSM, and a PINN is designed based on the aforementioned structure. A simplified training scheme is designed for the PINN based on a least-squares structure to facilitate online training in real time. A nonlinear controller based on exact feedback linearization is designed by considering the nonlinear model of the system identified based on the PINN. Therefore, the proposed approach involves identification and control in real time, where the PINN is trained online. In order to track the reference for the rotational speed, a nonlinear controller with integral action based on exact feedback linearization is designed based on a linear quadratic regulator. As a result, the proposed approach can be used to identify the system to be controlled in real time, and it is able to track any small change in the real model; in addition, it is robust to both external and internal disturbances, such as variations in torque load and resistance. The proposed approach is evaluated through simulation and using a real PMSM, and the results of reference tracking are evaluated under disturbances. The identification performance is evaluated by using a Taylor diagram under closed-loop and open-loop structures, where ARX and NARX structures are used for comparison. It is thereby verified that this novel proposed control approach involving a PINN-based model can adequately track the dynamics of a PMSM system, where the performance of the proposed nonlinear control is maintained even when using the identified model based on the PINN.

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

This paper presents an innovative multivariate forecasting application for hydrological variables, using an online model based on backpropagation (BP) neural networks. The effectiveness of this approach is verified by using the Nonlinear Autoregressive with Exogenous Inputs (NARX) and the linear Autoregressive with Exogenous Inputs (ARX) methods. The proposed methodology is implemented in two hydrological stations in the department of Chocó, Colombia, located along the Atrato River. The BP neural network structure is applied to establish correlations between water level, flow, and precipitation. This structure is designed to handle the dynamic complexity of the hydrological variables and their interconnections. In addition, a short-term water level prediction is developed for incorporation into a flood early warning system. Validation of the approach is performed by comparing various estimation errors using regression metrics such as root mean square error (RMSE), relative mean square error (MSRE), mean absolute error (MAE), relative mean absolute error (MARE), and Nash–Sutcliffe efficiency coefficient (NSE). The computation time and the accuracy of the estimated weights versus real data are also considered. The results show that the proposed structure is the most suitable for accurately predicting water levels. It is shown that the backpropagation neural network structure of the proposed model is more effective for water level prediction. This is observed when evaluating the real data compared to the estimated data, obtaining values corresponding to the regression metrics. In particular, for output 2, the RMSE is 0.0742. Compared to the NARX model (RMSE of 0.0985) and the ARX model (RMSE of 0.1265), it is observed that the backpropagation neural network model improves the estimation by 32.6 over the NARX model and by 70.4% over the ARX model. As for the NSE coefficient for output 2 of the model, the backpropagation neural network is the closest to 1 compared to the other two models. For output 2, the backpropagation model obtains a value of 0.9966, the NARX model 0.9958, and the ARX model 0.9914. Another evaluation metric applied to this model is the Kling–Gupta efficiency coefficient (KGE), which is used to evaluate the performance of hydrological models. For the output of the two hydrological stations located in the Atrato River, we observed that for the backpropagation model a value of 0.9992 was obtained, for the NARX model a value of 0.9988 was obtained, and for the linear ARX model with nonlinear structure a value of 0.9984 was obtained. This indicates that the backpropagation neural network model provides a better estimate, as its value is closer to 1. Furthermore, the computational time executed in the algorithm of the three proposed nonlinear models is calculated, where the backpropagation obtains a calculated value of 350.9 microseconds, for the NARX model the computational time is 5908.7 microseconds and for the ARX nonlinear model, the computational time is 5172.6 microseconds. The model put forward demonstrates its effectiveness in accurately predicting water levels at two key hydrological stations along Colombia’s Atrato River. By incorporating intricate interactions among hydraulic structures and dynamic hydrological conditions, it enhances prediction accuracy. This not only aids in disaster risk management but also streamlines planning processes, particularly within flood early warning systems.

Abstract High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes that can be used and to identify the optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single- and multiple-source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes (1) the localization error for each source and (2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG datasets with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can attain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases in synthetic signals and 63% in real signals, and in more than 88% and 73% of cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of cases respectively. Additionally, for such electrode numbers, lower mean errors and standard deviations than with 231 electrodes were obtained.

In this work a novel application for multivariable forecasting is presented, applied to hydrological variables and based on a multivariable NARX model. The proposed approach is designed for two hydrological stations located at the Atrato River in Colombia where the variables of water level, water flow and water precipitation are correlated by using the NARX model based on a neural network structure. The structure of the NARX-based neural network is designed in order to consider the complex dynamics of hydrological variables and their corresponding cross-correlations. A short-term water level forecasting is designed based on the NARX model, to be used as an early warning flood system. The validation of the proposed approach is performed by comparing the estimation error with an ARX dynamic model. As a result, it is shown that a NARX model structure is more suitable for water level forecasting than simplified structures.

This work presents dynamic Tikhonov state forecasting based on large-scale deep neural network constraint for the solution to a dynamic inverse problem of electroencephalographic brain mapping. The dynamic constraint is obtained by using a large-scale deep neural network to approximate the dynamics of the state evolution in a discrete large-scale state-space model. An evaluation by using neural networks with several hidden layer configurations is performed to obtain the adequate structure for large-scale system dynamic tracking. The proposed approach is evaluated over two models of 2004 and 10,016 states in discrete time. The models are related to an electroencephalographic problem for EEG generation. A comparison analysis is performed by using static and dynamic Tikhonov approaches with simplified dynamic constraints. By considering the obtained results it can be concluded that the deep neural networks adequately approximate large-scale state dynamics by improving the dynamic inverse problem solutions.

This paper proposes a comparative analysis between regular and parallel versions of FISTA and Tikhonov-like optimizations for solving the EEG brain mapping problem. Such comparison is performed in terms of computational time reduction and estimation error achieved by the parallelized methods. Two brain models (high- and low-resolution) are used to compare the algorithms. As a result, it can be seen that, if the number of parallel processes increases, computational time decreases significantly for all the head models used in this work, without compromising the reconstruction quality. In addition, it can be concluded that the use of a high-resolution head model produces an improvement in any source reconstruction method in terms of spatial resolution.

Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide a priori time-frequency information to improve the estimation of neural activity. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. However, attempts to use EMD in EEG analysis have not yet provided optimal reconstructions, due to the intrinsic mode-mixing problem of EMD. Several studies have used single-channel analysis, whereas others have used multiple-channel analysis for other applications. Here, we present the results of multiple-channel analysis using multivariate empirical mode decomposition (MEMD) to reduce the mode-mixing problem and provide useful a priori time-frequency information for the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated using real and synthetic EEG data, in which the reconstructions were performed using the multiple sparse priors (MSP) algorithm with EEG electrode montages of 32, 16, and 8 electrodes. The quality of the source reconstruction was assessed using the Wasserstein metric. A comparison of the solutions without pre-processing and those after applying MEMD showed the source reconstructions to be improved using MEMD as a priori information for the low-density montages of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced by 59.42 and 66.04% for the 8 and 16 electrode montages, respectively, and that on a simulated EEG with three active sources, by 87.31 and 31.45% for the same electrode montages.

En este artículo se describe el modelo dinámico de un vehículo aéreo no tripulado tipo QuadCopter, se linealiza alrededor de un punto de operación y se aplica un controlador por realimentación de estados que logra llevar el sistema a un estado estable en 100ms. Se aplica un algoritmo de identificación MIMO por mínimos cuadrados al modelo no linealizado obteniendo un error medio cuadrático de 0.2562 grados en una simulación de 50s; se aplica un controlador LQR que logra controlar e identificar el sistema inicial en un tiempo de 3.7s y se compara con un controlador PID convencional obteniendo como resultado un menor tiempo de establecimiento y mejor seguimiento de referencia en el controlador LQR.

En este artículo se aplica el algoritmo de identificación por mínimos cuadrados a un vehículo aéreo no tripulado tipo QuadCopter. El modelo se linealiza alrededor de un punto de operación y posteriormente se aplican controladores por realimentación de estados y LQR; este último demostró superioridad ante un controlador PID convencional.

The localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural activity in the brain. The location and amplitude of each active source are estimated by solving the inverse problem by regularization or using Bayesian methods with spatio-temporal constraints. Frequency and spatio-temporal constraints improve the quality of the reconstructed neural activity. However, separation into frequency bands is beneficial when the relevant information is in specific sub-bands. We improved frequency-band identification and preserved good temporal resolution using EEG pre-processing techniques with good frequency band separation and temporal resolution properties. The identified frequency bands were included as constraints in the solution of the inverse problem by decomposing the EEG signals into frequency bands through various methods that offer good frequency and temporal resolution, such as empirical mode decomposition (EMD) and wavelet transform (WT). We present a comparative analysis of the accuracy of brain-source reconstruction using these techniques. The accuracy of the spatial reconstruction was assessed using the Wasserstein metric for real and simulated signals. We approached the mode-mixing problem, inherent to EMD, by exploring three variants of EMD: masking EMD, Ensemble-EMD (EEMD), and multivariate EMD (MEMD). The results of the spatio-temporal brain source reconstruction using these techniques show that masking EMD and MEMD can largely mitigate the mode-mixing problem and achieve a good spatio-temporal reconstruction of the active sources. Masking EMD and EEMD achieved better reconstruction than standard EMD, Multiple Sparse Priors, or wavelet packet decomposition when EMD was used as a pre-processing tool for the spatial reconstruction (averaged over time) of the brain sources. The spatial resolution obtained using all three EMD variants was substantially better than the use of EMD alone, as the mode-mixing problem was mitigated, particularly with masking EMD and EEMD. These findings encourage further exploration into the use of EMD-based pre-processing, the mode-mixing problem, and its impact on the accuracy of brain source activity reconstruction.

In this research article a dynamic estimation of neuronal activity and brain dynamics from electroencephalographic (EEG) signals is presented using a dual Kalman filter. The dynamic model for brain behavior is evaluated using physiological-based linear models. Filter performance is analyzed for simulated and clinical EEG data, over several noise conditions. As a result a better performance on the solution of the dynamic inverse problem is achieved, in case of time varying parameters compared with the system with fixed parameters and the static case. An evaluation of computational load is performed when predicted dynamic cases, estimated using the Kalman filter, are up to ten times faster than the static case.

This paper presents a methodology for designing a minimum variance control- (MVC) and Kalman filter- (KF) based adaptive system. MVC is a technique of great interest, and it is widely used because it can reduce either energy or material consumption, or else, it can increase production performance. The Kalman filter is a recursive method that provides stochastic support for adaptive systems, showing feasibility and good results for dynamic system identification. The methodology implementation was conducted in a multiplatform integrated development environment called Qt Creator Qt 4.7-based, yielding good results when applied to the reference tracking problem. Moreover, it can be observed that the adaptive control scheme exhibits good settling times and notoriously appropriate overshoots.

Les Amérindiens Guajiro, ou bien les Wayuu, occupent la péninsule de la Guajira entre la Colombie et le Venezuela. Ils y sont décrits comme pêcheurs, bergers, marchands, pratiquants de la polygynie et de la polyrésidentialité. Leur système de filiation est matrilinéaire. Leur pouvoir politique est segmentaire ; pourtant la société apparaît hiérarchisée. Actuellement, il y a plus de 500.000 Wayuu entre les deux pays. Dans leur cas, les frontières entre mobilité et migration s'estompent, si on tient compte du fait qu'ils traversent une frontière territoriale entre deux États, mais sont toujours à l'intérieur de leur territoire ancestral « la Grande Nation Wayuu sans frontières ». Par ailleurs, l’approche classique des Amérindiens associe leurs sociétés avec les espaces ruraux. Dans l'imaginaire occidental, être amérindien signifie habiter dans les zones rurales ou la jungle. Les villes des Amériques fondées à partir du XVIe siècle sont de lieux où le monde européen, particulièrement l'hispanique a été transposé dans le continent. Mais des travaux sur les cultures populaires urbaines montrent que les racines Amérindiennes ne disparaissent pas dans les villes et dans les zones suburbaines du continent. Dans cette même perspective, en wayuunaiki, la langue des Guajiro, les notions d'« Amérindien » et d'« Occidental » correspondent aux notions de Wayuu et d'Alijuna. Pour la notion de « ville » il y a jipiapa Alijuna – soit « la terre des Occidentaux ». Or de façon contemporaine on constate leur présence, temporaire ou permanente, dans les villes depuis des années et à proximité ou non de leur territoire traditionnel. Il s'agit donc de confronter l'idée « de la grande nation wayuu sans frontière » dans le quotidien des Wayuu bilingues, principalement entre les villes de Maracaibo au Venezuela et de Ríohacha en Colombie. Dans cette même perspective, il s'agit de proposer un regard urbain ainsi que transfrontalier de ce peuple, à travers les deux villes qui se trouvent à proximité, pourtant, des deux pays différents. Des parcours, principalement entre les deux lieux, des entretiens non directifs, des conversations spontanées, ainsi que des documents sur place sont considérés, entre 2010 et 2011. Pour articuler une perspective hémisphérique, cette communication parle d'ailleurs de la frontière entre certains termes considérés comme opposés : ethnie/nation, Amérindien/Occidental, rural/urbain.

Este artículo presenta un nuevo método para la estimación de actividad neuronal a partir de señales electroencefalográficas usando análisis de series de tiempo ponderadas. El método considera un modelo lineal basado en restricciones fisiológicas que tiene en cuenta tanto la dinámica espacial como la temporal, y una etapa de ponderación que modifica las suposiciones del modelo a partir de las observaciones. La matriz de pesos calculada es incluida en la función de costo usada para solucionar el problema inverso dinámico, y por lo tanto en la formulación del filtro de Kalman. De esta forma, se propone un filtro de Kalman ponderado que incluye la matriz de pesos. El desempeño del filtro (en términos del error de localización) se analiza para varios SNRs. El desempeño óptimo se alcanza usando el modelo lineal con matriz de ponderación calculado por el método de producto interno.

This article presents an estimation method of neuronal activity into the brain using a Kalman smoother approach that takes into account in the solution of the inverse problem the dynamic variability of the time series. This method is applied over a realistic head model calculated with the boundary element method. A comparative analysis for the dynamic estimation methods is made up from simulated EEG signals for several noise conditions. The solution of the inverse problem is achieved by using high performance computing techniques and an evaluation of the computational cost is performed for each method. As a result, the Kalman smoother approach presents better performance in the estimation task than the regularized static solution, and the direct Kalman filter.

El territorio del pueblo autóctono Wayúu se sitúa, en la actualidad, en la frontera de Colombia y Venezuela. Las representaciones acerca de “la Gran Nación Wayúu, dónde no existe la frontera” se encuentran en textos de la literatura sociológica y antropológica y también son expresadas por algunas personas de este pueblo. Pero la demarcación del límite geográfico tiene efectos reales sobre la movilidad y las percepciones dentro del territorio. Es así como, el presente artículo, busca mostrar algunas representaciones acerca de la demarcación invisible que atraviesa el territorio wayúu, que contrasta con los testimonios de lo que ocurre en la práctica para las y los wayúu, realidad confrontada a las representaciones de un territorio sin fronteras.

This work presents a control system used when the detailed parameter information of a plant in not available. Therefore, is useful to design a controller with an approximated model of the system, this is reachable through the use of parameter estimation based on the input and output signals. The estimation procedure is commonly developed with deterministic algorithms, avoiding the presented uncertainties and errors added to the system by the conditioning equipment. In this work we present an on line parameter computation, over systems that employs an error assumption in their model, the adaptive tuning of a PID controller using the Kalman filter in order to control the speed on an induction motor. Also, the control by the voltage-frequency in closed loop and the effectiveness of the control algorithm by the test developed on the asynchronous machine are described.

In this paper is presented a method for neural activity estimation over the brain that take into account, for the solution of the inverse problem, a dynamic model for the neural activity in a realistic head model calculated with bounded elements method, according to a physiologically based model that describes the real interaction between neurons. The solution of the inverse problem is calculated using high performance computing. This analysis is performed for simulated EEG signals for SNR of 25 dB, 15 dB and 5 dB. The obtained results show the robustness of the estimation method that includes the dynamic model in comparison with the static model for several levels of noise.

A linear controller is calculated by the use of a multiobjective optimization technique, and is applied over the induction machine; which is modeled in the oriented coordinate rotor flux system. The performance indexes chosen are sensitivity of state variables, closed loop gain and the mixed sensitivity. The method used is the eigenstructure assignment; in which, the objective is to determine the controller through pole placement using eigenvalues and eigenvectors; the optimization is realized by the genetic algorithms. The tests applied over the system imply the controller design in combination with the robust problem and genetic algorithms, and two cases are taken into account; one objective and multiobjective. The computational results and comparisons show that the multiobjective optimization increases the performance of the system against the one objective optimization and the H∞ controller.

Este documento presenta la aplicación de la técnica de control no lineal clásica llamada linealización por realimentación de variables de estado. Se hace una aplicación en el motor de inducción, la variable a controlar es la velocidad del eje del motor, el sistema emplea el esquema de control vectorial para máquinas de corriente alterna desarrollado en las últimas décadas; este método es análogo a la técnica de control del motor de corriente directa. El modelo del motor de inducción se describe en el sistema de coordenadas de campo orientado del flujo de rotor y se muestra una introducción al producto y la derivada de Lie, empleados en el diseño del controlador no lineal.This articles shows the application of a classic non-linear control technique called "linearization by feedback of status variables." An application on the induction engine is made. Variable to be controlled is speed of the engine shaft. The system employs a vectorial control scheme for AC engines developed during the last decades. This is a method analogous to the DC engine control technique. Induction engine model is described in the guided field coordinate system of rotor flow. Introduction to the product and Lie derivative used for designing the non-linear controller are shown.

A simple adaptive controller design is presented in this paper, the control system uses the adaptive fuzzy logic, sliding modes and is trained with the recursive least squares technique. The problem of parameter variation is solved with the adaptive controller; the use of an internal PI regulator produces that the speed control of the induction motor be achieved by the stator currents instead the input voltage. The rotor-flux oriented coordinated system model is used to develop and test the control system.

Background The acquisition of healthcare-associated infections (HAI) is characterized by developing during the patient’s hospital stay and even after the hospital stay during the recovery period, as long as it is due to factors intrinsic to the hospitalization or medical treatment of the entity, as well as surgical techniques that require prophylactic treatment and medical surveillance. Methods Type and design of research: Quantitative, observational, descriptive, cross-sectional, retrospective. The population and sample is made up of patients who underwent orthopedic surgery in an institution in the municipality of Pereira, between January and December 2022. Results The results showed that there are significant differences between genders in the incidence of certain medical conditions and response to certain treatments. Women appear to be more prone to deep infections, use more chlorhexidine as an antiseptic, and show a higher prevalence of pathologic conditions compared to men. In addition, differences in the distribution of ASA classification and surgical wound type suggest possible health disparities between genders in the context of orthopedic surgery. Conclusions The importance of implementing infection prevention and control strategies adapted to specific contexts is evident. Despite the differences in the risk factors identified, both studies highlight the importance of optimizing antibiotic prophylaxis and adequately managing specific comorbidities to reduce the incidence of postsurgical infections. Furthermore, attention to perioperative surveillance and surgical time management are suggested as key areas to improve surgical outcomes in both cohorts of patients, as they can contribute significantly to the reduction of the incidence of SSIs in patients undergoing orthopedic procedures.