Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds

Imbach, Frank; Candau, Robin; Chailan, Romain; Perrey, Stéphane

doi:10.3390/sports8070103

Cited by 40 publications

(50 citation statements)

References 60 publications

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“…Leg and joint stiffness themselves are also possible targets for gait retraining interventions, especially in light of recent advances in calculating stiffness using research-grade or commercially available wearable sensors. 5 , 17 With such devices, protocols for monitoring or altering stiffness could be efficiently implemented in clinical settings on large numbers of patients. However, before interventions to monitor or change leg and joint stiffness can be implemented, it is necessary to confirm the previously hypothesized relationships between injury risk and stiffness.…”

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confidence: 99%

Leg Stiffness, Joint Stiffness, and Running-Related Injury: Evidence From a Prospective Cohort Study

Davis

Gruber

2021

Orthopaedic Journal of Sports Medicine

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Background: The spring-like behavior of the leg and the joints of the lower body during running are thought to influence a wide range of physiologic and mechanical phenomena, including susceptibility to overuse injury. If leg and joint stiffness are associated with running-related injuries, altering joint or leg stiffness may be a useful avenue for injury rehabilitation and injury prevention programs. Purpose: To test the associations between running-related injury and leg stiffness, knee stiffness, and ankle stiffness in a prospective study of recreational runners. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 49 healthy recreational runners took part in a year-long study. Participants completed a 3-dimensional kinematic and kinetic biomechanical assessment at baseline and reported training volume and injury status in a weekly survey during the follow-up period. Relationships between stiffness and injury were assessed at the level of individual legs (n = 98) using spline terms in Cox proportional hazards models. Results: During follow-up, 23 participants (29 legs) sustained injury. The median time to injury was 27 weeks (53.27 hours of training). Relative injury rate as a function of knee stiffness displayed a weak and nonsignificant U-shaped curve ( P = .187-.661); ankle and leg stiffness displayed no discernable associations with relative injury rate (leg stiffness, P = .215-.605; ankle stiffness, P = .419-.712). Conclusion: Leg and joint stiffness may not be important factors in the development of running-related injuries. Moderate changes in leg and joint stiffness are unlikely to substantially alter injury risk.

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confidence: 99%

Leg Stiffness, Joint Stiffness, and Running-Related Injury: Evidence From a Prospective Cohort Study

Davis

Gruber

2021

Orthopaedic Journal of Sports Medicine

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“…The relationship between P and speed was shown to be linear for speeds ranging from 8 to 20 km/h on a treadmill [21]. The GCT and Kleg were validated by a recent study for speeds between 8 km/h and 20 km/h on an indoor track [22].…”

Section: Introductionmentioning

confidence: 88%

Biomechanics of Trail Running Performance: Quantification of Spatio-Temporal Parameters by Using Low Cost Sensors in Ecological Conditions

Perrotin¹,

Gardan²,

Lesprillier³

et al. 2021

Applied Sciences

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The recent popularity of trail running and the use of portable sensors capable of measuring many performance results have led to the growth of new fields in sports science experimentation. Trail running is a challenging sport; it usually involves running uphill, which is physically demanding and therefore requires adaptation to the running style. The main objectives of this study were initially to use three “low-cost” sensors. These low-cost sensors can be acquired by most sports practitioners or trainers. In the second step, measurements were taken in ecological conditions orderly to expose the runners to a real trail course. Furthermore, to combine the collected data to analyze the most efficient running techniques according to the typology of the terrain were taken, as well on the whole trail circuit of less than 10km. The three sensors used were (i) a Stryd sensor (Stryd Inc. Boulder CO, USA) based on an inertial measurement unit (IMU), 6 axes (3-axis gyroscope, 3-axis accelerometer) fixed on the top of the runner’s shoe, (ii) a Global Positioning System (GPS) watch and (iii) a heart belt. Twenty-eight trail runners (25 men, 3 women: average age 36 ± 8 years; height: 175.4 ± 7.2 cm; weight: 68.7 ± 8.7 kg) of different levels completed in a single race over a 8.5 km course with 490 m of positive elevation gain. This was performed with different types of terrain uphill (UH), downhill (DH), and road sections (R) at their competitive race pace. On these sections of the course, cadence (SF), step length (SL), ground contact time (GCT), flight time (FT), vertical oscillation (VO), leg stiffness (Kleg), and power (P) were measured with the Stryd. Heart rate, speed, ascent, and descent speed were measured by the heart rate belt and the GPS watch. This study showed that on a ≤10 km trail course the criteria for obtaining a better time on the loop, determined in the test, was consistency in the effort. In a high percentage of climbs (>30%), two running techniques stand out: (i) maintaining a high SF and a short SL and (ii) decreasing the SF but increasing the SL. In addition, it has been shown that in steep (>28%) and technical descents, the average SF of the runners was higher. This happened when their SL was shorter in lower steep and technically challenging descents.

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“…In running, the direct measurement of power output is also challenging, and thus an indirect approach, where the mechanical power is estimated using inertial measurement units (IMUs), has been attempted with several commercial technologies on the market. However, the proprietary methods used for power estimation in running from IMU data are not published, and the repeatability and concurrent validity of the commercial technologies is found to be low [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski Skating

Uddin

Seeberg

Kocbach

et al. 2021

Sensors

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The ability to optimize power generation in sports is imperative, both for understanding and balancing training load correctly, and for optimizing competition performance. In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs). Thirteen athletes conducted roller ski skating trials on a treadmill with varying incline and speed. The acceleration and gyroscope data collected with the IMUs were run through statistical feature processing, before being used by the deep learning model to estimate power output. The model was thereafter used for prediction of power from test data using two approaches. First, a user-dependent case was explored, reaching a power estimation within 3.5% error. Second, a user-independent case was developed, reaching an error of 11.6% for the power estimation. Finally, the LSTM model was compared to two other machine learning models and was found to be superior. In conclusion, the user-dependent model allows for precise estimation of roller skiing power output after training the model on data from each athlete. The user-independent model provides less accurate estimation; however, the accuracy may be sufficient for providing valuable information for recreational skiers.

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Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds

Cited by 40 publications

References 60 publications

Leg Stiffness, Joint Stiffness, and Running-Related Injury: Evidence From a Prospective Cohort Study

Leg Stiffness, Joint Stiffness, and Running-Related Injury: Evidence From a Prospective Cohort Study

Biomechanics of Trail Running Performance: Quantification of Spatio-Temporal Parameters by Using Low Cost Sensors in Ecological Conditions

Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski Skating

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