Detection of Ground Contact Times with Inertial Sensors in Elite 100-m Sprints under Competitive Field Conditions

Association between Sprint and Jump Performance and Maximum Strength in Standing Calf Raise or Squat in Elite Youth Soccer Players

Soccer is a complex sports discipline that requires players to engage in diverse high-intensity and multidirectional activities. The optimization of strength and conditioning programs requires a comprehensive understanding of the physical attributes influencing player performance. While previous research has demonstrated the influence of knee and hip extensor muscles on the performance in sprints and other explosive movements, this study aimed to establish the relationship between plantar flexor muscle strength and high-intensity actions. Back squat (BS) and calf raise (CR) one-repetition maxima as well as linear sprint (5-, 10-, 30 m) and drop jump performance from different heights (15, 30, 45 and 60 cm) were measured in 45 elite youth players (age: 16.62 ± 1.1 years). Results showed significant negative correlations between BS strength and sprint times (r = -0.60 to -0.61), confirming the importance of lower limb extensor muscle strength in short-distance sprints. While no significant correlations were found with sprint performances, CR strength was significantly associated with drop jump test results from 45 and 60 cm drop height (r = 0.36 to 0.46). These findings demonstrate that isolated CR strength positively influences the performance in actions involving rapid stretch-shortening cycles, which suggests that current strength and conditioning programs for youth soccer players should be extended to also include exercises specifically targeting the plantar flexor muscles. While this cross-sectional study provides novel insights into the complex interplay between muscle strength and soccer-specific performance, its findings need to be corroborated in longitudinal studies directly testing the impact of plantar flexor strength training.

The Validity of a Three-Dimensional Motion Capture System and the Garmin Running Dynamics Pod in Connection with an Assessment of Ground Contact Time While Running in Place

A three-dimensional motion capture system (MoCap) and the Garmin Running Dynamics Pod can be utilised to monitor a variety of dynamic parameters during running. The present investigation was designed to examine the validity of these two systems for determining ground contact times while running in place by comparing the values obtained with those provided by the bilateral force plate (gold standard). Eleven subjects completed three 20-s runs in place at self-selected rates, starting slowly, continuing at an intermediate pace, and finishing rapidly. The ground contact times obtained with both systems differed significantly from the gold standard at all three rates, as well as for all the rates combined (p < 0.001 in all cases), with the smallest mean bias at the fastest step rate for both (11.5 ± 14.4 ms for MoCap and -81.5 ± 18.4 ms for Garmin). This algorithm was developed for the determination of ground contact times during normal running and was adapted here for the assessment of running in place by the MoCap, which could be one explanation for its lack of validity. In conclusion, the wearables developed for monitoring normal running cannot be assumed to be suitable for determining ground contact times while running in place.

Estimation of Ground Contact Time with Inertial Sensors from the Upper Arm and the Upper Back

Ground contact time (GCT) is one of the most relevant factors when assessing running performance in sports practice. In recent years, inertial measurement units (IMUs) have been widely used to automatically evaluate GCT, since they can be used in field conditions and are friendly and easy to wear devices. In this paper we describe the results of a systematic search, using the Web of Science, to assess what reliable options are available to GCT estimation using inertial sensors. Our analysis reveals that estimation of GCT from the upper body (upper back and upper arm) has rarely been addressed. Proper estimation of GCT from these locations could permit an extension of the analysis of running performance to the public, where users, especially vocational runners, usually wear pockets that are ideal to hold sensing devices fitted with inertial sensors (or even using their own cell phones for that purpose). Therefore, in the second part of the paper, an experimental study is described. Six subjects, both amateur and semi-elite runners, were recruited for the experiments, and ran on a treadmill at different paces to estimate GCT from inertial sensors placed at the foot (for validation purposes), the upper arm, and upper back. Initial and final foot contact events were identified in these signals to estimate the GCT per step, and compared to times estimated from an optical MOCAP (Optitrack), used as the ground truth. We found an average error in GCT estimation of 0.01 s in absolute value using the foot and the upper back IMU, and of 0.05 s using the upper arm IMU. Limits of agreement (LoA, 1.96 times the standard deviation) were [-0.01 s, 0.04 s], [-0.04 s, 0.02 s], and [0.0 s, 0.1 s] using the sensors on the foot, the upper back, and the upper arm, respectively.

Accuracy and Interpretation of the Acceleration from an Inertial Measurement Unit When Applied to the Sprint Performance of Track and Field Athletes

In this study, we aimed to assess sprinting using a developed instrument encompassing an inertial measurement unit (IMU) in order to analyze athlete performance during the sprint, as well as to determine the number of steps, ground contact time, flight time, and step time using a high-speed camera as a reference. Furthermore, we correlated the acceleration components (XYZ) and acceleration ratio with the performance achieved in each split time obtained using photocells. Six athletes (four males and two females) ran 40 m with the IMU placed on their fifth lumbar vertebra. The accuracy was measured through the mean error (standard deviation), correlation (r), and comparison tests. The device could identify 88% to 98% of the number of steps. The GCT, flight time, and step time had mean error rates of 0.000 (0.012) s, 0.010 (0.011) s, and 0.009 (0.009) s when compared with the high-speed camera, respectively. The step time showed a correlation rate of r = 0.793 (p = 0.001) with no statistical differences, being the only parameter with high accuracy. Additionally, we showed probable symmetries, and through linear regression models identified that higher velocities result in the maximum anteroposterior acceleration, mainly over 0-40 m. Our device based on a Wi-Fi connection can determine the step time with accuracy and can show asymmetries, making it essential for coaches and medical teams. A new feature of this study was that the IMUs allowed us to understand that anteroposterior acceleration is associated with the best performance during the 40 m sprint test.

Wearables for Running Gait Analysis: A Systematic Review

BACKGROUND

Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance.

OBJECTIVES

We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults.

METHODS

A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science and SPORTDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis and key findings.

RESULTS

A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (n = 62) [i.e. a combination of accelerometers, gyroscopes and magnetometers in a single unit] or solely accelerometers (n = 40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n = 93), using a treadmill (n = 62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g. age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards.

CONCLUSIONS

This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one inertial measurement unit sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and the reliability of devices and suitability of gait outcomes.

CLINICAL TRIAL REGISTRATION

CRD42021235527.