This study aimed to examine the influence of RunScribe location (i.e. lace shoe vs heel shoe) on the accuracy of spatiotemporal gait characteristics during running by comparing data with a high-speed video analysis system at 1000 Hz. A total of 49 endurance runners performed a running protocol on a treadmill at comfortable velocity. Two systems were used to determine spatiotemporal parameters (i.e. contact time, flight time, step frequency, and step length) during running: high-speed video analysis at 1000 Hz and two different RunScribe placements (i.e. lace shoe vs heel shoe). The pairwise comparisons showed some between-system differences in both lace shoe (contact time: p = 0.009; step frequency: p = 0.001) and heel shoe (flight time: p = 0.006; step frequency: p = 0.010), although the effect sizes were small (effect size < 0.3 in all cases). The intraclass correlation coefficients revealed an almost perfect association between systems for contact time and flight time (intraclass correlation coefficient: 0.85–0.90), and step length and step frequency (intraclass correlation coefficient: 0.96–0.97), regardless of the RunScribe placement. Bland–Altman plots revealed that the lace shoe location yielded smaller systematic bias, random errors, and narrower limits of agreement for spatiotemporal parameters during running, except for SF, which had a higher accuracy in a heel shoe location. The results suggest that RunScribe is a valid system to measure spatiotemporal parameters during running on a treadmill according to a high-speed video analysis at 1000 Hz. In addition, the data indicate that the location of the RunScribe system (lace shoe vs heel shoe) plays an important role on the accuracy of spatiotemporal parameters. The lace shoe placement showed smaller systematic bias, random errors, and narrower limits of agreement for contact time, flight time, and step length, whereas the heel shoe placement was slightly more accurate for the step frequency.

Markerless motion capture systems (MCS) have been developed as an alternative solution to overcome the limitations of 3D MCS as they provide a more practical and efficient setup process given, among other factors, the lack of sensors attached to the body. However, this might affect the accuracy of the measures recorded. Thus, this study is aimed at evaluating the level of agreement between a markerless MSC (i.e., MotionMetrix) and an optoelectronic MCS (i.e., Qualisys). For such purpose, 24 healthy young adults were assessed for walking (at 5 km/h) and running (at 10 and 15 km/h) in a single session. The parameters obtained from MotionMetrix and Qualisys were tested in terms of level of agreement. When walking at 5 km/h, the MotionMetrix system significantly underestimated the stance and swing phases, as well as the load and pre-swing phases (p < 0.05) reporting also relatively low systematic bias (i.e., ≤ −0.03 s) and standard error of the estimate (SEE) (i.e., ≤0.02 s). The level of agreement between measurements was perfect (r > 0.9) for step length left and cadence and very large (r > 0.7) for step time left, gait cycle, and stride length. Regarding running at 10 km/h, bias and SEE analysis revealed significant differences for most of the variables except for stride time, rate and length, swing knee flexion for both legs, and thigh flexion left. The level of agreement between measurements was very large (r > 0.7) for stride time and rate, stride length, and vertical displacement. At 15 km/h, bias and SEE revealed significant differences for vertical displacement, landing knee flexion for both legs, stance knee flexion left, thigh flexion, and extension for both legs. The level of agreement between measurements in running at 15 km/h was almost perfect (r > 0.9) when comparing Qualisys and MotionMetrix parameters for stride time and rate, and stride length. The agreement between the two motion capture systems varied for different variables and speeds of locomotion, with some variables demonstrating high agreement while others showed poor agreement. Nonetheless, the findings presented here suggest that the MotionMetrix system is a promising option for sports practitioners and clinicians interested in measuring gait variables, particularly in the contexts examined in the study.

The use of markerless motion capture systems is becoming more popular for walking and running analysis given their user-friendliness and their time efficiency but in some cases their validity is uncertain. Here, the test-retest reliability of the MotionMetrix software combined with the use of Kinect sensors is tested with 24 healthy volunteers for walking (at 5 km·h⁻¹) and running (at 10 and 15 km·h⁻¹) gait analysis in two different trials. All the parameters given by the MotionMetrix software for both walking and running gait analysis are tested in terms of reliability. No significant differences (p > 0.05) were found for walking gait parameters between both trials except for the phases of loading response and double support, and the spatiotemporal parameters of step length and step frequency. Additionally, all the parameters exhibit acceptable reliability (CV < 10%) but step width (CV > 10%). When analyzing running gait, although the parameters here tested exhibited different reliability values at 10 km·h⁻¹, the system provided reliable measurements for most of the kinematic and kinetic parameters (CV < 10%) when running at 15 km·h⁻¹. Overall, the results obtained show that, although some variables must be interpreted with caution, the Kinect + MotionMetrix system may be useful for walking and running gait analysis. Nevertheless, the validity still needs to be determined against a gold standard system to fully trust this technology and software combination.

The biomechanics of walking and running, in both ground and treadmill conditions, have been extensively analysed and important differences have been reported. Despite some previous studies having examined the validity and reliability of the OptoGait™ system for measuring gait characteristics during walking, no previous works have determined the reliability and validity of this system while running on a treadmill. Therefore, this study aimed to determine the absolute reliability (within-subject variation) and evaluate the concurrent validity of the OptoGait™ system for measuring spatiotemporal variables while running at a comfortable speed by comparing data with a highly accurate system of measuring those parameters (i.e. video analysis at 1000 Hz). Forty-nine endurance runners performed a running protocol on a treadmill at a comfortable speed. Two systems were used to collect data: OptoGait™ system and high-speed video analysis at 1000 Hz. The coefficient of variation (CV) was calculated as a measure of absolute reliability. The OptoGait™ system reported a CV range between 2.2% and 11.4% for spatiotemporal parameters, while the video analysis showed a CV range between 0.02% and 9.9%. To determine concurrent validity, intra class correlation coefficients (ICC) and pairwise comparisons of means (t-test) were calculated between data from both systems. Although the paired t-test demonstrated significant differences between systems, a high level of agreement (ICC > 0.89) was obtained in spatiotemporal parameters between systems. When compared to a high-speed video analysis at 1000 Hz, the results indicate that the OptoGait™ system is a reliable and valid tool to measure spatiotemporal gait characteristics while running on a treadmill at a comfortable speed.