The rapid emergence of advanced therapeutics has highlighted the need for innovative drug delivery systems (DDSs) capable of overcoming biological barriers and ensuring precise spatial and temporal control. This work focuses on the design and simulation of a dual-mode microfluidic DDS that combines electrochemical and magnetic actuation mechanisms to enable reliable and minimally invasive therapeutic delivery. Computational simulations demonstrate the system’s ability to maintain laminar flow under varying conditions, including different Reynolds numbers, fluid viscosities, and multi-input configurations, ensuring robust performance across diverse scenarios. Guidelines for fabrication have been established, detailing the integration of electrochemical drug release via gold membrane dissolution and a magnetic fallback system using iron-doped PDMS membranes for power-independent operation. The front-end fluid control system has been physically implemented on the macro-scale and addresses critical challenges in localized drug delivery by combining spatiotemporal precision, adaptability, and robustness.

The myocardium is composed of sheets of brick-shaped cardiomyocytes embedded within an anisotropically aligned extracellular matrix of proteins. This intricate structural organization underlies the electrical and mechanical functionality of the heart. In vitro cardiac microphysiological systems (MPS) aim to replicate these structure-function relationships; however, existing platforms often struggle to acquire high-resolution electrophysiological data from three-dimensional tissues due to geometric mismatch, poor mechanical integration, or insufficient spatiotemporal coverage. In this thesis, we present electrically instrumented engineered cardiac ventricles that integrate ultra-flexible mesh electronics with fiber-aligned scaffolds fabricated via Focused Rotary Jet Spinning. This platform enables stimulation and recording of cardiac electrophysiology across physiologically relevant spatial and temporal domains while preserving the native tissue architecture. By providing high-fidelity, multimodal access to volumetric tissue dynamics, these electrically instrumented cardiac MPS advance the frontier of heart-on-a-chip technology and hold promise for applications in drug screening, disease modeling, and personalized cardiac medicine.

This project builds upon prior work into the use of image tactile sensors to improve gripper performance through the adjustment of marker identifying algorithms and the initial construction of a physical gripping system. I focused on improving marker tracking algorithms, refining the physical design of sensor modules, and enabling real-time data processing. The previous image tactile sensing system has been enhanced through the development of new sensor designs, integration of a multi-camera system, and incorporation of a MySQL database for real-time data storage and analysis. Marker tracking accuracy and processing speed were significantly improved by introducing techniques like k-means clustering, watershed segmentation, and frame-skipping, alongside the use of diffused lighting and optimized illumination parameters. Three novel sensor modules were fabricated, including a black rubber module with manually placed markers, a clear silicone pad for small object interactions, and a gel-tip-inspired design for dynamic sensing. These modules demonstrated improved adaptability, scalability, and accuracy for tactile sensing. MySQL integration facilitated long-term data aggregation and enabled retrospective analysis of sensor performance, paving the way for machine learning applications in predictive force estimation. The database allows for the categorization of force measurements by sensor type, object size, and timestamp, enabling continuous system refinement. Real-time feedback was achieved by addressing prior computational challenges, reducing noise, and eliminating reliance on external lighting conditions. Testing with multiple gripper models validated the system's capability for precise tactile sensing in robotic manipulation tasks, allowing for accurate handling of a larger array of object sizes. The updated system supports continuous force tracking and object recognition during gripper operation. Future work will focus on miniaturizing the sensors, further optimizing image analysis algorithms, and leveraging accumulated data for enhanced system performance. This semester's advancements mark a significant step toward developing low-cost, efficient tactile sensing systems for applications in robotics, prosthetics, and human-robot interaction. Documenting these aspects of the implementation would make improving robotic gripping and navigation more accessible for a wider group of users. Because of the limitation of time, the physical calibration of the grippers and experiments regarding improvement to object manipulation performance are future considerations.

Split Learning, Federated Learning, and recent combined approaches such as Split-Federated Learning represent significant advances in privacy preservation and individualization in neural network training. However, while these approaches offer promising solutions for large scale distributed learning, limitations in edge device computational power and memory have limited the practicality of biomedical and healthcare applications of neural networks on Wearable Medical Devices (WMDs). This work introduces a novel GRU-based SplitFed framework to achieve a balance of privacy, computational efficiency, and accuracy to satisfy the unique needs in the healthcare space. This approach addresses the computational limitations of edge devices by leveraging Gated Recurrent Units (GRUs) instead of traditional LSTM networks, while maintaining comparable classification accuracy. By implementing a hybrid architecture that combines the data privacy benefits of Split Learning with the collaborative advantages of Federated Learning, we demonstrate improved performance on ECG classification tasks. Experimental results on ECG arrhythmia classification show that our GRU-based SplitFed approach achieves up to 65.02% accuracy, outperforming both centralized models (58.86%) and traditional split learning approaches (50.21-50.94%). Our experimental analysis shows that both Split Learning and SplitFed approaches have comparable communication requirements for models of similar size while offering significant advantages in accuracy and privacy. Our experiments also revealed important stability challenges in the training process that must be addressed for reliable deployment. This work expands the applicability of privacy preserving distributed learning to resource-constrained medical devices, providing a framework that balances privacy, efficiency, and performance for secure processing of sensitive biomedical data.