Kevin H.

A full-stack wearable system in collaboration with the Drexel Wireless Systems Lab for continuous, ambulatory cardiorespiratory monitoring. The project integrates signal acquisition, edge processing, and machine learning inference for real-time health monitoring outside clinical settings, and includes the IoT Sensor Framework (a HIPAA-compliant platform for collecting data from IoT-based sensors including knitted smart textiles) and the IoT Processing Framework (real-time and offline signal processing and machine learning on sensor data), both developed with the Drexel Wireless Systems Lab. Related student work includes multi-semester research in RF-based biomedical signal acquisition and analysis using wearable IoT devices (conducted across CS391 and CS394), independent study in multi-sensor data fusion techniques for wearable and IoT applications (conducted in CS392), and a machine learning pipeline for minute-level and subject-level sleep apnea detection using ECG signals (presented at Ursinus CoSA 2026 by Andrei Bogdan). We became involved in this research in 2013.

An interactive, no-code neural network visualizer that lets users upload data, define a network architecture, train a model, and explore its behavior through Sankey diagram visualizations — all in the browser. Developed by Ursinus students Kevin Hoffman and Michael Cummins. Source on GitHub. Related work includes tools for making machine learning model decisions more interpretable and transparent (presented at Ursinus CoSA 2025 by Michael Cummins), and independent studies developing methods to visualize internal feature representations in trained neural networks. Conducted by multiple students across CS391 and CS392. Development began in 2022.

Kevin Hoffman, William M. Mongan · Ursinus College