ECSS Paris 2023: OP-BM08
INTRODUCTION: Technical actions such as cornering are key determinants of mountain bike (MTB) performance, yet they are rarely quantified with field-based measures. Wearable inertial sensors allow performance-relevant acceleration metrics to be collected in ecological tasks, comparing expert and less-expert MTB riders (Camomilla et al., 2015). METHODS: Thirteen youth MTB riders (12–18 y.o.; 8 expert and 5 non-expert) performed repeated turns on MTB terrain (left and right), completing three trials per turning direction. IMUs were mounted on the bike front (fork area), seat tube, and on the participant’s back. A U-shaped turning test was used, consisting of a 10 m approach (start), a 180-degree turn around a semicircle with 9 m diameter, and a 10 m exit section (re-acceleration). The task was segmented into four phases: start, turn-entry, turn-exit, and re-acceleration. Mean horizontal acceleration (Acc-H) and Absolute peak horizontal acceleration (Hpeak-abs) were computed per phase. Linear mixed models tested fixed effects of turning phase, turning side, and expertise, with random intercepts for participants; turning side was included to account for unavoidable terrain-related variability between directions. RESULTS: Mean Acc-H differed across phases at all sensor locations (front, seat tube, participant: p < .001), with the same overall ordering across sensors: start > re-acceleration > turn-entry ≈ turn-exit (turn-entry vs. turn-exit: ns). For peak acceleration, phase effects were present at the front and participant sensors (p<.0001), while the seat-tube showed no overall significant effects. For both front and participant peaks, phase × direction interactions were significant (p<.01), reflecting that Right vs Left differences were phase-specific: at start, Right > Left (p<.05), whereas in re-acceleration, Left > Right (p<.05). For participant peaks, a slightly higher peaks in experts than non-experts was found (p = .05). CONCLUSION: Field-based IMU metrics captured clear phase-dependent changes in horizontal loading during MTB cornering across all sensor locations, and revealed phase-dependent left–right asymmetries that may reflect turning direction and terrain-related variability. No effects of expertise were detected on mean or peak horizontal acceleration. Expertise did not affect mean Acc-H, but showed a small effect on peak acceleration. Camomilla, V., Bergamini, E., Fantozzi, S., & Vannozzi, G. (2018). Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: A systematic review. Sensors, 18(3), 873.
Read CV Gabriele RussoECSS Paris 2023: OP-BM08
INTRODUCTION: The assessment of Force-Velocity (F-V) profiles is crucial for optimizing rowing performance. However, constructing accurate F-V profiles requires precise measurement of handle force and velocity. Currently, the gold standard for velocity measurement is optical motion capture systems (Mocap), which prevents the routine application of F-V testing in daily training. Thus, deriving velocity from IMU data to construct Force-Velocity profiles offers a more practical solution for daily training environments. This study aimed to validate the concurrent validity, reliability, and sensitivity of a custom IMU-embedded force gauge for assessing rowing kinematics and F-V profiles against a Mocap system. METHODS: Twenty-five experienced male rowers performed maximal trials on a Concept2 ergometer across 5 resistance settings (2 sets of 20 strokes). A custom sensor unit, integrating a force gauge and an IMU, was attached between the handle and the chain. Simultaneously, marker-based Mocap data (Qualisys) were collected. Raw IMU data were processed using a custom-developed algorithm to compute stroke kinematics (e.g., Velocity, Distance) and derive F-V profiles via linear regression using peak force and peak velocity values. Statistical analysis included Linear Mixed-Effects Models, Intraclass Correlation Coefficient (ICC), Bland-Altman analysis, and Minimal Detectable Change (MDC). RESULTS: The results demonstrated excellent agreement with the Mocap system for all assessed kinematic parameters. For example, Peak Drive Velocity showed a negligible bias of -0.01 m/s (ICC = 0.988, MDC=0.08 m/s, RMSE = 0.05 m/s, LOA: -0.10 to 0.08 m/s) and Drive Distance showed a bias of -0.01 m (ICC = 0.979, MDC=0.07 m, RMSE = 0.04 m, LOA: -0.08 to 0.06 m). Regarding the F-V profile, the derived parameters exhibited high concurrent validity. Theoretical Maximal Power (Pmax) showed the highest agreement, with a bias of only 0.24% (2133.1 ± 755.4 vs. 2130.6 ± 722.2 W; ICC = 0.988, LOA: -236.6 to 231.6 W). Theoretical Maximal Force (F0) (ICC = 0.967, Bias = 2.1%) and Velocity (V0) (ICC = 0.956, Bias = -1.5%) also demonstrated strong correlations with no significant differences between methods (p > 0.05). CONCLUSION: The IMU-embedded force gauge, driven by the custom processing algorithm, provides valid and sensitive measures of both rowing kinematics and F-V profiles. Given the negligible bias and high reliability, this portable system offers a robust stand-alone solution, allowing coaches and scientists to accurately monitor rowers' mechanical characteristics and technical metrics in daily training environments.
Read CV Zihan JiangECSS Paris 2023: OP-BM08
INTRODUCTION: Velocity-Based Training (VBT) facilitates objective load prescription in resistance training. While Linear Position Transducers (LPTs) remain the "gold standard" for velocity monitoring, their high cost hinders widespread practical adoption. Low-cost Inertial Measurement Units (IMUs) offer a wireless alternative; however, they are often prone to signal noise and integration drift, particularly during ballistic movements such as Olympic weightlifting derivatives. This study aimed to validate a custom signal processing algorithm designed for a cost-effective IMU to accurately monitor Hang Power Clean (HPC) velocity. METHODS: Twelve resistance-trained athletes participated in a progressive HPC loading protocol (20–100% 1RM). Vertical barbell velocity was monitored concurrently by a criterion LPT (GymAware, 50 Hz) and a barbell-mounted IMU (Movella Xsens DOT, 120 Hz). Data were matched post-hoc using a magnitude-based ranking and temporal sequencing algorithm to align valid LPT entries with IMU-detected peaks. A custom MATLAB script utilized quaternions to isolate global vertical acceleration, which was processed via a 4th-order zero-phase Butterworth low-pass filter (cutoff: 12 Hz) to mitigate noise and converted to velocity using trapezoidal integration with linear detrending. Validity and reliability were assessed on a pooled dataset of 174 repetitions using Pearson’s r, Intraclass Correlation Coefficient (ICC), bias, Coefficient of Variation (CV%), and Standard Error of Measurement (SEM). RESULTS: Both Peak Velocity (PV) and Mean Velocity (MV) demonstrated strong linearity (r > 0.80) and acceptable agreement (ICC > 0.745) between devices. Regarding measurement error, systematic biases of -0.091 m/s and -0.070 m/s were observed for PV and MV respectively, with Standard Error of Measurement (SEM) values of 0.142 m/s and 0.087 m/s. Furthermore, the system exhibited high intra-trial reliability across the load spectrum, with low coefficients of variation (CV%) for both PV (5.8%) and MV (5.7%). CONCLUSION: The custom 120 Hz signal processing algorithm effectively tracks ballistic weightlifting movements using consumer-grade IMUs. Although the raw algorithm results in consistent velocity underestimation, it maintains high linearity and low variability. Therefore, this system provides a valid, cost-effective solution for monitoring general HPC training loads and velocity trends, offering a practical alternative where "gold standard" precision is inaccessible.
Read CV Xiao Jian GuangECSS Paris 2023: OP-BM08