Real-Time True Wireless Stereo Wearing Detection Using a PPG Sensor with Edge AI

True wireless stereo (TWS) earbuds are evolving into multifunctional wearable devices, offering opportunities not only for audio streaming but also for health-related applications. A fundamental requirement for such devices is the ability to accurately detect whether they are being worn, yet conventional proximity sensors remain limited in both reliability and functionality. This work explores the use of photoplethysmography (PPG) sensors, which are widely applied in heart rate and blood oxygen monitoring, as an alternative solution for wearing detection. A PPG sensor was embedded into a TWS prototype to capture blood flow changes, and the wearing status was classified in real time using a lightweight k-nearest neighbor (k-NN) algorithm on an edge AI processor. Experimental evaluation showed that incorporating a validity check enhanced classification performance, achieving F1 scores above 0.95 across all wearing conditions. These results indicate that PPG-based sensing can serve as a robust alternative to proximity sensors and expand the capabilities of TWS devices.