Characterizing soft tissue mechanics is of interest across biomedical disciplines, including in regenerative medicine and prosthetics development. In the context of skin cancer, there is a well-elucidated relationship with tissue stiffness; however, mechanical properties of skin are not factored into skin cancer screening and diagnostics because of a lack of established benchmark values for skin stiffness. Existing technologies for soft tissue mechanical characterization are ill-suited for assessing the elastic modulus of potential skin cancers—they are ex vivo, do not provide the resolution needed for skin lesions, and/or are not sufficiently maneuverable for in situ use. Here is reported a proof-of-concept for a novel device for the measurement of skin mechanics in situ, which is handheld, portable, and operates on the scale of skin lesions. Testing on polymers shows the device is able to reliably differentiate between materials that have elastic moduli in the range of human epidermal/dermal tissue.

Anatomic Endoscopic Enucleation of the Prostate (AEEP) is a highly effective treatment for benign prostatic hyperplasia but remains challenging to master, requiring extensive practice and direct supervision by expert surgeons. This dependence limits opportunities for independent learning and slows trainee progression. To address this, we developed a hydrogel-based simulation model that replicates male pelvic anatomy, including the prostate and bladder. The model incorporates real-time force feedback using force sensors connected to a Raspberry Pi and Arduino. Trainees receive live visual cues, green (safe), yellow (near-threshold), and red (excessive), to guide safe and effective instrument use. An IMU sensor attached to the endoscope handle enables a dynamic minimap that tracks the tool’s position within the model. After the procedure, a feedback dashboard provides performance analytics for self-guided improvement. Our system promotes independent learning and enables efficient use of expert surgeon time, ultimately enhancing technical skill acquisition and clinical decision-making through simulation-based education.

This project implements an integrated hardware-software architecture to enable robust acoustic sensing. The deployed setup pairs the microphone with a Blues Cygnet microcontroller board, which digitizes and streams audio data to a PC via USB. The wireless Blues Notecard operates by connecting to local Wi-Fi, enabling real-time environmental sound capture and automatic cloud uploads for centralized processing. Acoustic features, such as Mel-Frequency Energy (MFE) coefficients, are extracted from the raw signals to train machine learning models that are capable of distinguishing gunshots from ambient noise.

In high-intensity combat zones, military medics face immense challenges in documenting and transmitting critical patient data. The Tactical Combat Casualty Care (TCCC) card, currently used by the US military and NATO, is essential for recording injuries and treatments in the field. However, its paper-based format is prone to loss, damage, and delays in data transmission, leading to inefficiencies in medical response and treatment continuity.

Through interviews with US military personnel, we learned that 8 times out of 10, the TCCC card gets lost during evacuation, resulting in critical medical data not reaching the hospital in time. Our solution digitizes this process to enhance speed, accuracy, and integration with modern medical infrastructure

Our solution is a ruggedized military iPad application that digitizes the TCCC card through AI-powered image recognition and voice-to-text processing eliminating the inefficiencies of paper-based documentation.