Sensors
Wearable Multi-Sensor Fusion for Field Assessment of Dynamic Postural Stability During the Curling Delivery in Young Adult Curlers: Integrating Trunk Kinematics, Plantar Pressure and Core-Muscle Activation
By Bowen Zhang, Jiying Wu, Tianhao Han and 2 others.
Abstract
Postural control in curling is expressed during a dynamic, sport-specific delivery on a single sliding leg, yet it is still assessed almost exclusively with static single-leg force-plate protocols that poorly reflect on-ice demands. This study developed and validated a wearable multi-sensor system that fuses trunk and pelvis inertial measurement units (IMUs), instrumented plantar-pressure insoles and core-muscle surface electromyography (sEMG) to quantify dynamic postural stability during the simulated curling delivery in 110 young adult curlers (age range 18–24 years). A four-phase segmentation of the delivery was implemented, and a compact 28-feature set was extracted and combined through a complementary Kalman fusion scheme into a single Dynamic Stability Index (DSI). Validated against a reference force plate during static single-leg standing, the IMU-derived sway path showed a small bias (~0.4 cm) with 95% limits of agreement of approximately +4.4 to −3.6 cm, and between-day reliability of the fused DSI was excellent (ICC(3,1) = 0.90, 95% CI [0.82, 0.95]). A random-forest classifier discriminated high- from low-stability deliveries with 91.8% accuracy (95% CI [87.2%, 95.1%]; AUC = 0.92, 95% CI [0.87, 0.96]), outperforming any single-sensor or static force-plate baseline. Sensor-derived dynamic metrics correlated with external-oblique strength, trunk proprioceptive error and motor self-efficacy (Spearman ρ ranging from −0.55 to 0.51, all p < 0.001 after Holm–Bonferroni correction), and dynamic instability increased monotonically across descending self-efficacy tertiles. The proposed system provides a portable, context-relevant and interpretable tool for balance profiling in young curlers, and demonstrates that multi-sensor fusion substantially improves measurement fidelity over conventional single-modality assessment.




