Dancers face high foot, ankle, and knee injury rates due to improper form during pointe work and jumping movements from en pointe positions. Our device combines pressure mapping, haptic feedback motors, and an inertial measurement unit (IMU) to provide dancers with live feedback on their pointe work, preventing injuries before they occur. A flexible printed circuit board (PCB) with force sensors and a rigid PCB with microcontroller and IMU work together to correct dancers. The motors will vibrate to guide the dancer on how they should adjust to achieve proper form, alongside an LED for visual feedback. The sensors are housed in a 3D printed sleeve with connections to the rigid PCB worn on a band around the ankle, making it easy to integrate into a dancer’s existing routine. Ten dancers of varying skill levels will be tested to evaluate the device’s performance. We anticipate this innovation improving dancer safety by offering an accessible and affordable tool for injury prevention.

FloCync has developed an innovative menstrual blood collection system with integrated filtration technology that enables non-invasive health monitoring and diagnostics research. Our novel solution combines a specially designed menstrual cup with dual-membrane filtration and a “click-it” vacutainer mechanism that preserves critical biomarkers by separating samples immediately at collection, before clotting occurs.
Initially targeting research institutions and clinical trials, FloCync standardizes menstrual blood collection to accelerate biomarker discovery for conditions like endometriosis and PCOS. The goal of the project is to address diagnostic delays affecting millions of women globally.
Beyond research applications, FloCync will expand to consumer diagnostics, empowering women to monitor chronic conditions at home through lateral flow assays, like pregnancy tests but for disease biomarkers. By transforming a discarded biological fluid into valuable health data, FloCync is building the infrastructure for the next era of women’s health innovation.

Acquired nystagmus is a condition characterized by repetitive, involuntary eye movements that impair visual stability and quality of life. Current treatments, including pharmacological therapy and surgery, are often ineffective, non-specific, and unable to adapt to progressive symptom changes. Here, we present a real-time eye-tracking system that detects nystagmus, classifies its subtype using machine learning, and computes a corrective motion vector. The system integrates infrared cameras with Fourier-based processing to distinguish pathological oscillations from voluntary gaze shifts, achieving 99% detection accuracy with a 10 ms response latency. A computational model predicts the necessary counteracting motion to stabilize gaze, mapping corrective movements to extraocular muscles. Preliminary results demonstrate high precision in nystagmus classification and motion compensation, establishing a foundation for future electrode-based stimulation therapies. This system represents a step toward adaptive, closed-loop therapies.

Using statistical analysis on features of the golf downswing, we created a dashboard that combines users’ data with the research to provide golf insights and advice from our virtual coach.