Every new vehicle is equipped with a full suite of sensors designed to monitor blind spots and prevent collisions. Yet cyclists, who face far greater consequences in any collision, are expected to rely on shoulder checks and brief glances behind them. Our project addresses this blind-spot hazard by integrating radar and computer vision to detect rear-approaching vehicles. Haptic feedback alerts the rider to these otherwise unseen threats by conveying their relative position and speed.

Underground mine rescue occurs in GPS-denied, particulate-heavy environments where human entry is hazardous and delayed. Autonomous mapping can augment initial rescue efforts by providing situational awareness before first responders enter. Conventional LiDAR degrades in particulate-heavy conditions due to optical scattering, critically reducing localization reliability. We present a low-cost, lightweight drone for autonomous 3D mapping in degraded subterranean environments. We augment LiDAR with non-scattering millimeter-wave radar through an adaptive switching framework to maintain localization accuracy and mapping continuity under reduced visibility.

Surgeons performing ENT and endoscopic procedures face high risk of musculoskeletal disorders from prolonged static postures, yet existing ergonomic solutions fail to provide adequate support or account for this specific surgical context. We are developing a motor-driven armrest that actively supports the surgeon's forearm using force-based control, preventing unintended movement while preserving surgical precision. Effectiveness is validated through surface electromyography and computer vision posture analysis. The device will be evaluated by surgeons in a simulated environment performing standardized movements.