Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Development of robotic platform for biomechanical simulation of lower limb support under reduced gravity: design and validation(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Meza Flores, Carlos Joaquin; Chairez Oria, Jorge Isaac; emipsanchez; García González, Alejandro; School of Engineering and Sciences; Campus Monterrey; Bello Robles, Juan CarlosAs humanity prepares for long-duration missions to the Moon, Mars, and beyond, the need to understand how altered gravitational environments affect the human body has never been more urgent. One of the primary physiological systems impacted by these conditions is the musculoskeletal system, which undergoes substantial adaptation in reduced gravity, often leading to muscle atrophy, bone density loss, and changes in motor control. These challenges not only affect astronaut performance in space but also pose significant rehabilitation demands upon return to Earth. Consequently, there is a growing demand for advanced experimental platforms that can simulate partial gravity on Earth for the purpose of studying locomotion, muscle activation, and biomechanical adaptation. This thesis addresses that need by presenting the design, development, implementation, and evaluation of a novel dual Stewart platform robotic system specifically engineered to simulate reduced gravity environments for biomechanical experimentation.At the core of the research is a dual parallel manipulator configuration, known as a dual Stewart platform, which provides high-fidelity control over six degrees of freedom for each of its two stages. This setup enables the simulation of complex support and perturbation forces typically experienced during gait in altered gravity. The upper platform serves as the primary interface for subject interaction, capable of supporting a test subject’s lower limbs, while the lower platform is used to simulate ground reaction forces with precise control.A significant contribution of this work is the development of a robust embedded control architec- ture designed to manage the dynamic interaction between the user and the robotic system. The control framework employs a super-twisting sliding-mode control (ST-SMC) algorithm with state-dependent gain adaptation. This approach ensures robust and precise trajectory tracking of the platform’s end- effector, even in external disturbances such as user motion or force feedback. The controller was rig- orously validated through both simulation and experimental trials, demonstrating its superior stability and performance over conventional PID or linear feedback controllers, particularly in highly nonlinear operating conditions. To complement the mechanical and control systems, a multi-channel surface electromyography (sEMG) system was developed and integrated into the platform. This circuit was custom-designed to capture real-time muscle activation signals from multiple lower-limb muscle groups, providing syn- chronized neuromuscular data during locomotion trials. The sEMG system enables high-resolution monitoring of muscle recruitment patterns, allowing researchers to study how gravitational changes af- fect neuromuscular coordination and effort during walking or balance tasks. The integration of sEMG data with motion control feedback creates a powerful experimental tool that bridges the gap between kinematic performance and physiological response.Experimental validation of the complete system was conducted using simulated gait patterns. Preliminary results demonstrated the system’s capacity to reproduce biomechanically plausible motion trajectories and consistent sEMG activation profiles corresponding to expected changes in muscle load and coordination. These findings validate the platform’s functionality as a reliable testbed for studying locomotion under variable gravity conditions.Overall, this thesis presents a novel, multi-disciplinary approach that merges robotics, control theory, biomechanics, and neurophysiology into a single integrated system. The platform has potential applications in astronaut training, rehabilitation engineering, and human performance research.
- Transformer-based hand landmark prediction from superficial electromyography(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Ramos García deAlba, Diego Armando; Chairez Oria, Jorge Isaac; emipsanchez; Sánchez Ante, Gildardo; School of Engineering and Sciences; Campus Monterrey; Fuentes Aguilar, Rita QuetziquelThe development of human-robot systems has become increasingly prominent in recent years, particularly in domains that require seamless and intuitive interactions between humans and machines, such as healthcare, manufacturing, rehabilitation, and entertainment. Within these fields, upper-limb robotics and prosthetics have experienced significant growth, where control strategies play a central role in user experience, functionality, and long-term usability. Among the available strategies, myoelectric control, which utilizes electrical activity gener- ated by muscle contractions to drive robotic actuators, stands out for its potential to provide direct and responsive user intent decoding. Despite its promise, current commercial myoelectric control systems suffer from no- table limitations. Most commercially available upper-limb robotic devices rely on binary or proportional control paradigms. Binary control allows the user to initiate simple, on/off com- mands (e.g., open or close a prosthetic hand). In contrast, a proportional control scales the degree of motion or force based on the magnitude of the input signal. While these methods are relatively straightforward to implement and train, they inherently limit the functionality of robotic systems by constraining them to a narrow range of discrete, non-adaptive actions. As a result, users often experience frustration due to unnatural movements, lack of fluidity, and the inability to perform complex, multi-joint, or continuous tasks. Pattern recognition (PR)-based control has emerged as a more advanced alternative to binary or proportional schemes. PR control systems employ machine learning algorithms to classify muscle activity into predefined gesture categories. This approach improves intuitive- ness by enabling the recognition of multiple movements and gestures, offering a more versatile control interface. However, PR control is also restricted in significant ways. Its effectiveness is typically bound by the limited number of gestures used during the training phase, making the system inflexible to untrained motions or novel hand configurations. Additionally, the reliance on discrete classification does not accommodate continuous, dynamic control, which is crucial for achieving truly natural and precise robotic movement. To address these limitations, we propose a novel method for predicting continuous hand movement using surface electromyography (sEMG) signals through the application of a mul- timodal transformer architecture. Unlike traditional PR systems that output gesture classes, our approach is designed to estimate continuous hand landmark positions, thereby enabling fluid and unrestricted movement trajectories. This method represents a paradigm shift in my- oelectric control, moving from classification-based strategies to continuous regression-based motion estimation. The proposed system employs a transformer model—a deep learning architecture orig- inally designed for natural language processing—that excels at capturing complex temporal and contextual relationships in sequential data. In the context of sEMG, transformer mod- els offer several key advantages over traditional convolutional or recurrent neural networks. First, transformers eliminate the need for handcrafted feature engineering, which has his- torically been a challenging and subjective component of EMG signal processing. Instead, the transformer architecture inherently learns relevant features from raw sEMG input data through self-attention mechanisms. Second, transformers can simultaneously model spatial and temporal dependencies within the input sequence. This is crucial for decoding sEMG signals, which exhibit both spatial complexity across different muscle groups and temporal dynamics associated with movement initiation and execution. Finally, transformer models are more parameter-efficient and scalable, making them adaptable to different limb configu- rations, electrode placements, and control environments. Our multimodal architecture takes sEMG signals as input and outputs the continuous positions of hand landmarks—key spatial reference points on the hand that define its posture and motion. By focusing on hand landmark prediction rather than gesture classification, the system bypasses the inherent limitations associated with a finite gesture vocabulary. This allows users to perform an unlimited range of movements, including intermediate postures and transitions between gestures, without retraining or expanding the model’s gesture set. The landmark-based output also facilitates integration with existing computer vision and robotic control systems, many of which use landmark-based representations for motion planning and kinematic modeling. The development and validation of our approach involved collecting synchronized sEMG and hand motion data from a cohort of participants performing a variety of hand movements. Hand landmarks were extracted using vision-based tracking systems, serving as ground truth labels for model training and evaluation. The transformer model was trained to map multi- channel sEMG signals to the corresponding hand landmark coordinates over time. Extensive experimentation demonstrated that our model not only outperformed baseline architectures in terms of accuracy and generalization but also required less training data due to the efficiency of the self-attention mechanism. Qualitative evaluations further confirmed that the predicted hand trajectories were smooth, natural, and closely aligned with actual user intent, indicating the system’s potential for real-time application in robotic prosthetics and exoskeletons. In summary, this study introduces a transformative approach to myoelectric control by leveraging a multimodal transformer architecture for continuous hand movement prediction. By shifting the focus from discrete gesture classification to continuous motion estimation via landmark regression, our method addresses several long-standing challenges in the field, including limited gesture scalability, unnatural control behavior, and reliance on handcrafted features. The use of transformers for modeling spatiotemporal dependencies in sEMG data represents a significant advancement, opening the door to more intuitive, responsive, and user- centric human-robot interaction systems.
- Intelligent system for impedance stabilization of model cochlear implantable electrodes(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Buenrostro López, David; Chairez Oria, Jorge Isaac; emimmayorquin; Huegel West, Joel; Perfecto Avalos, Yocanxóchitl; Ramírez Nava, Gerardo Julian; School of Engineering and Sciences; Campus Guadalajara; Aregueta Robles, UlisesThis thesis aimed to develop a neurostimulator for testing various stimulation strategies to stabi-lize electrode impedance in medical devices like cochlear implants. These devices have restored hearing to individuals with profound deafness. However, their performance is often hindered when the immune response leads to the formation of fibrous tissue around the electrodes, which increases impedance and can impair their function. Previous research suggests that early stages of the immune response can be modulated through electrical stimulation, but testing this hypothesis is difficult due to the need for multiple stimulation pulses, making it impractical. The challenge is compounded by the lack of stimula-tors that automatically switch between signals. This study focused on developing an electrostimulator that can autonomously alternate between signals without manual intervention, enabling faster test-ing of different strategies. Electrodes designed for cell culture were used to evaluate the stimulator’s performance. Fibroblast cells were cultured on these model electrodes to simulate the tissue response in vitro, mimicking the conditions of cochlear implant electrodes. The simulator was programmed to deliver 24 different signals over 12 hours, with each signal applied for 1 minute, followed by 30 min-utes of no stimulation. This automated sequence eliminates manual intervention, allowing for a more efficient process. The main outcome of this research was the development of a neurostimulator that can test several parameters for controlling tissue responses. In this case, the stabilization of electrode impedance, which can be affected by cellular interactions that lead to the formation of fibrotic tissue. Indeed, the capabilities of this technology extend beyond impedance stabilization. For instance, to test stimulation strategies to control neuronal behavior.
- Robotic system for three-dimensional culture on curved surfaces using galvanotaxis for the generation of tissues from animal cells(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-01) Castillo Madrigal, Jesús; Chairez Oria, Jorge Isaac; emiggomez, emipsanchez; García Cuéllar, Alejandro Javier; School of Engineering and Sciences; Campus Guadalajara; Perfecto Avalos, YocanxóchitlDue to the great need to improve the methods of bioprinting processes, the implementation of bioprinting in curved surfaces is needed to reassemble the idea of 3D bioprinting and generate a novel system that could create more complex 3D structures than the ones created by 3D bioprinting in planar surfaces. This work focuses on analyzing the theory needed to implement this nonplanar system in order to develop a successful design that promote the aggregation of cells in a nonplanar surface and maintain the cell’s life. Being able to implement a 3D bioprinting system in curved surfaces is going to help others to design and print more complex structures such as organs with high vascularity. The expected results of this work are to create a prototype based on two dc motors, which allows motion in x and y axis. One of the future possible applications of this prototype is to improve the advancements in the tissue engineering area in order to print more complex structure as the apex of the heart, kidneys and neural tissue.
- Dual bio-printing system for cell deposition of hydrogels using a piston-based controlled nozzle(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022) Castillo Madrigal, Victor; Chairez Oria, Jorge Isaac; mtyahinojosa, emipsanchez; García González, Alejandro; Escuela de Ingeniería y Ciencias; Campus Guadalajara; Perfecto, YocanxóchitlIn recent years, cell culture has increasingly utilized various 3D scaffolds and hydrogels to promote advanced additive manufacturing within cell culture. Additionally, various types of cell lines have been employed, with mesenchymal stem cells (MSCs) and fibroblasts being among the most commonly used. The aim of this project is to design an automated dual bioprinting system for cell culture. This system will deposit both hydrogels and cells sequentially, creating a foundation for biological tissue.Therefore, reducing the probability of cell culture contamination and decreasing human interaction. To achieve the goal, the project was first designed following specific criteria specifications. This was facilitated by previous cell culture training, which helped in better understanding the necessities of the project.After that, the next step involved simulating the device using CAD software (Solidworks) to create a 3D representation of the system's prototype. This prototype consists of three linear actuators (X-Y-Z axis) and two extruders for each deposited material.Then, the fully virtual device is exported to Matlab Simulink in order to simulate a control process with a PD controller in each actuator and extruder using a sine signal as a reference. Finally the crafting of the prototype was achieved operating tools from the metal crafting laboratory, and experimental processes were started. The study evaluated the feasibility of developing a 3D bioprinting machine. The experiment proved that it is possible to replicate the behavior from the simulated space into a real experiment, using a dual bioprinting system for depositing cells with controlled processes. The error from the control process was below 1% in the virtual enviroment meanwhile in reality the error mantain around 3%. As future work is still to validate the system performing biotic tests with a hydrogel made of alginate and as crosslinker calcium chloride. This system can be further applied in automatized cell culture system, bioprinting on no linear surfaces or even as part of a bioreactor system.