Measurement error of 3D kinematic and kinetic measures during overground endurance running in recreational runners between two test sessions separated by 48 h

Reliability of Xsens IMU-Based Lower Extremity Joint Angles during In-Field Running

The Xsens Link motion capture suit has become a popular tool in investigating 3D running kinematics based on wearable inertial measurement units outside of the laboratory. In this study, we investigated the reliability of Xsens-based lower extremity joint angles during unconstrained running on stable (asphalt) and unstable (woodchip) surfaces within and between five different testing days in a group of 17 recreational runners (8 female, 9 male). Specifically, we determined the within-day and between-day intraclass correlation coefficients (ICCs) and minimal detectable changes (MDCs) with respect to discrete ankle, knee, and hip joint angles. When comparing runs within the same day, the investigated Xsens-based joint angles generally showed good to excellent reliability (median ICCs > 0.9). Between-day reliability was generally lower than the within-day estimates: Initial hip, knee, and ankle angles in the sagittal plane showed good reliability (median ICCs > 0.88), while ankle and hip angles in the frontal plane showed only poor to moderate reliability (median ICCs 0.38-0.83). The results were largely unaffected by the surface. In conclusion, within-day adaptations in lower-extremity running kinematics can be captured with the Xsens Link system. Our data on between-day reliability suggest caution when trying to capture longitudinal adaptations, specifically for ankle and hip joint angles in the frontal plane.

Between-day reliability and minimum detectable change of the Conventional Gait Model 2 and Plug-in Gait Model during running

BACKGROUND

The Plug-in Gait model (PiG) is commonly used in 3D motion analysis but has limited reliability. Although an improved version of PiG has been developed, called the Conventional Gait Model 2 (CGM2), there is limited evidence on its between-day reliability for running.

RESEARCH QUESTION

What is the between-day intraclass correlation coefficient (ICC3,k) and minimum detectable change (MDC) of lower limb kinematics and kinetics for CGM2 during running and does reliability differ between CGM2 and PiG.

METHODS

Twenty-three healthy participants performed running at a comfortable speed in two identical test sessions at least 5 days apart. Lower limb kinematic and kinetic data in the three planes of motion were calculated using CGM2 and PiG. The ICC and MDC were calculated for the kinematic and kinetic parameters at initial contact and peak during the stance phase of running.

RESULTS

CGM2 kinematics showed good-to-excellent reliability (ICC: 0.75-0.93), except for hip extension and ankle internal rotation, and less than 5° MDC (1.8°-4.9°) of the coronal and sagittal planes, except for hip extension. PiG showed poor-to-moderate reliability (ICC: -0.15 to 0.72) in the coronal and transverse planes and greater than 5° MDC (5.0°-21.8°), except for knee extension, adduction, and ankle dorsiflexion. CGM2 showed good-to-excellent reliability for peak kinetics (ICC: 0.75-0.97), except for hip internal rotation and knee extension. The ICC and MDC were higher for CGM2 than PiG, with significant differences in the coronal plane of the hip and knee joints and transverse plane of the hip joint in kinematics and in the sagittal and coronal plane of the hip and knee joints in kinetics.

SIGNIFICANCE

The between-day reliability of CGM2 was mostly good to excellent for lower limb kinematics and kinetics during running. We believe that CGM2 can more accurately assess kinematic differences between the coronal and transverse planes than the PiG.

The between-day repeatability, standard error of measurement and minimal detectable change for discrete kinematic parameters during treadmill running

BACKGROUND

Kinematic parameters of the trunk, pelvis and lower limbs are frequently associated with both running injuries and performance, and the target of clinical interventions. Currently there is limited evidence reporting the between-day repeatability of discrete kinematic parameters of the trunk, pelvis and lower limbs during treadmill running.

RESEARCH QUESTION

What is the between-day repeatability, standard error of measurement and minimal detectable change of discrete kinematic parameters of the trunk, pelvis and lower limbs during treadmill running?

METHODS

16 healthy participants attended two kinematic data collection sessions two weeks apart. Three-dimensional kinematic data were collected while participants ran on a motorised treadmill at 3.2 m/s. The interclass correlation coefficient, standard error of measurement and minimal detectable change were calculated for discrete kinematic parameters at initial contact, toe off, peak angles and joint excursions during the stance phase of running.

RESULTS

Good to excellent repeatability with low standard error of measurement and minimal detectable change values were observed for sagittal and frontal plane kinematics at initial contact (Range: ICC, 0.829-0.941; SEM, 0.6°- 2.6°; MDC, 1.5°- 7.2) and peak angles during stance (Range: ICC, 0.799 - 0.946; SEM, 0.6°- 2.6°; MDC, 1.7°- 7.1°). Peak transverse plane kinematics of the hip (ICC, 0.783; SEM, 3.2°; MDC, 8.7°) and knee (ICC, 0.739; SEM, 3°; MDC, 8.4°) demonstrated moderate between-day repeatability with large SEM and MDC values. Kinematics at toe off demonstrated the lowest ICC values and largest measurement errors of all parameters (Range: ICC, 0.109 - 0.900; SEM, 0.8°- 5.7°; MDC, 2.5°- 15.7°).

SIGNIFICANCE

This is the first study detailing the measurement error and minimal detectable change for discrete kinematic parameters of the trunk and pelvis during treadmill running. The reported values may provide a useful reference point for future studies investigating between-day differences in running kinematics.

Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach

Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed to address this need. However, previous methods require cumbersome sensor setup or participant-specific calibration. This study aims to validate a shoe-mounted accelerometer for sagittal plane lower extremity angle measurement during running based on a deep learning approach. A convolutional neural network (CNN) architecture was selected as the regression model to generalize in inter-participant scenarios and to minimize poorly estimated joints. Motion and accelerometer data were recorded from ten participants while running on a treadmill at five different speeds. The reference joint angles were measured by an optical motion capture system. The CNN model predictions deviated from the reference angles with a root mean squared error (RMSE) of less than 3.5° and 6.5° in intra- and inter-participant scenarios, respectively. Moreover, we provide an estimation of six important gait events with a mean absolute error of less than 2.5° and 6.5° in intra- and inter-participants scenarios, respectively. This study highlights an appealing minimal sensor setup approach for gait analysis purposes.

Distance running stride-to-stride variability for sagittal plane joint angles

Recent research indicates that distance running stride-to-stride variability (SSV) is related to performance and injury. Previous studies have primarily focused on stride characteristics (stride length and time). We assessed SSV for sagittal plane joint angles with the primary purpose of testing for significant differences among the lower body joints. The secondary purpose was to determine if strong correlations exist among joint SSV measures. Thirty recreational adult runners participated in the study (8 females, 22 males, 39 ± 10 years; 53.1 ± 25.7 km/week). A 6-camera motion capture system (200 Hz) collected kinematic data during treadmill running at a preferred pace. A 2 by 3 repeated measures factorial ANOVA (phase-stance, swing; joint-hip, knee, ankle) was run (p = 0.05). There was a significant interaction effect (p < 0.001) and post hoc analysis revealed knee swing to be the most variable condition by far. For all three joints, there were strong correlations between stance and swing SSV (r = 0.80 to r = 0.88) and correlations among the joints were moderate to strong (r = 0.55 to 0.86). This study helps to better understand the joints/phases that contribute most to variability in the overall stride. Also, the strong correlations suggest that runners appear to have an overall SSV pattern that is similar across joints/phases.

Metabolic testing does not alter distance running lower body sagittal kinematics

BACKGROUND

Distance running fitness is commonly assessed using metabolic testing (MT). During MT, the runner must wear a mask that covers the nose and mouth. It is unclear if this increased challenge alters running kinematics and/or stride-to-stride variability (SSV). In this study we thoroughly assess the sagittal plane lower body joint angles.

RESEARCH QUESTION

Are there significant differences between standard treadmill running kinematics and those collected during MT?

METHODS

Twenty recreational runners participated (34.8 ± 10.0 years; 20+ miles per week). Six Vicon Bonita cameras were used to collect kinematic data (200 Hz). A metabolic cart (Parvo Medics TrueOne 2400) was used for heart rate (HR) collection and testing. Participants ran 4 × 4 min at preferred pace: 2 control runs (CON) and 2 MT runs. Ten strides were used to generate average stance and swing joint angle plots. The phase plots were compared for CON and MT and mean difference scores were calculated (to determine the kinematic change). SSV was determined by assessing the standard deviations among the 10 strides. Repeated measures ANOVA was used to test for significant differences among CON and MT trials. Reliability was assessed for 8 discrete joint angles using ICC analysis.

RESULTS

There were no significant differences between CON and MT for both the joint angle plot comparisons and SSV. For the discrete kinematic measures, ICC scores were strong (0.89-0.99) between CON and MT. During MT, there were slight increases (p < 0.01) in HR (145 ± 14 vs. 147 ± 14) and RPE (10.4 ± 1.5 vs. 11.4 ± 1.5).

SIGNIFICANCE

Results from this study support the validity of simultaneously conducting a kinematic and MT analysis. However, clinicians and performance coaches should be aware that 1) MT is slightly more physiologically demanding than CON and 2) approaches from this study can be used during individual assessments to confirm that kinematics are similar (between CON and MT).