Dynamic gesture tracking using wearable data gloves with flexible FBGs

Existing wearable data gloves for gesture recognition often face challenges in achieving both high precision and real-time performance. To address these limitations, we propose a data glove design incorporating fiber Bragg gratings (FBGs) encapsulated in flexible materials and positioned near the interphalangeal joints. This setup enables effective gesture recognition. First, the principle of fiber grating curvature sensing is derived, followed by curvature calibration of the FBG data glove through flexible encapsulation. An experimental platform was constructed to assess the glove’s performance in static and dynamic digital gesture recognition. Calibration results demonstrate a linear correlation between wavelength drift and curvature changes, with a comprehensive sensitivity of 0.0126 nm/°. Static and dynamic experimental findings confirm that the FBG sensors effectively monitor wavelength shifts induced by finger curvature variations. Analysis of different digital gestures at various time intervals revealed the wavelength offsets corresponding to the straightening and bending states of each finger. The five FBG sensors, encapsulated in flexible materials and positioned at the proximal interphalangeal joints, capture curvature changes across the five fingers, enabling accurate, real-time recognition of both static and dynamic gestures. This study highlights the potential of the developed wearable data glove for tracking and recognizing fine movements of the human hand.