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Advanced Sensing Technologies in Sports Biomechanics

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Dear Colleagues,

Advanced sensing technologies are reshaping sports biomechanics by providing highly accurate, real-time measurements of human motion, mechanical load, and performance. This Special Issue focuses on how both wearable and non-wearable sensors enable deeper insights into movement patterns, technique optimization, injury prevention, and training monitoring.

This Special Issue welcomes studies that develop, validate, or apply innovative sensing systems, as well as approaches involving signal processing, artificial intelligence, machine learning, or biomechanical modeling that enhance the interpretation and practical deployment of sensor-based data. The goal is to gather strong scientific evidence and advanced methodologies that improve accuracy, accessibility, and applicability of sensing technologies in real sports settings.

The topic fits squarely within the scope of Sensors, as it integrates sensor development, implementation, and evaluation with a domain—sports biomechanics—where reliable measurement is essential for performance and health. Contributions from engineering, biomechanics, sports science, data science, and related fields are encouraged.

Prof. Dr. Alejandro Molina-Molina
Prof. Dr. Luis Enrique Roche-Seruendo
Prof. Dr. Antonio Cartón-Llorente
Dr. Alberto Rubio-Peirotén
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8 pages, 868 KB

Open AccessBrief Report

The Validity of Stryd Leg Stiffness Against the Morin (2005) Sine-Wave Method: A Level-1 Assessment of Flat and Uphill Treadmill Running

by Diego Jaén-Carrillo and Antonio Cartón-Llorente

Sensors 2026, 26(10), 3244; https://doi.org/10.3390/s26103244 - 20 May 2026

Viewed by 176

Abstract

This study evaluated the validity of the leg stiffness metric provided by the Stryd running power meter against the Morin (2005) sine-wave spring–mass model. Twenty-three highly trained trail runners (11 women) completed a 12 min uphill time trial at +12% grade and one hour of submaximal level running. Leg stiffness was calculated from contact time, flight time, running speed, and leg length using Morin’s method, and compared with Stryd values. Agreement was assessed following the Dhahbi and Chamari Level-1 analytical framework, including intraclass correlation coefficient (ICC_2,1), Bland–Altman analysis, mean absolute percentage error (MAPE), and paired t-tests. Stryd and Morin estimates showed excellent agreement in both conditions: uphill running: ICC_2,1 = 0.96 (95%CI: 0.91–0.98), bias = −0.02 kN·m⁻¹, limits of agreement (LoAs) = [−0.61, 0.58] kN·m⁻¹, MAPE = 2.5% (p = 0.803); and level running: ICC_2,1 = 0.97 (95%CI: 0.93–0.99), bias = −0.04 kN·m⁻¹, LoAs = [−0.62, 0.54] kN·m⁻¹, MAPE = 2.6% (p = 0.505). The Stryd sensor provides valid leg stiffness estimates in highly trained trail runners on both level and inclined terrain. The negligible systematic bias and narrow limits of agreement support the use of Stryd for leg stiffness monitoring in field and laboratory settings. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies in Sports Biomechanics)

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