Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running.

Measuring impact-related quantities in running is of interest to improve the running technique. Many quantities are typically measured in a controlled laboratory setting, even though most runners run in uncontrolled outdoor environments. While monitoring running mechanics in an uncontrolled environment, a decrease in speed or stride frequency can mask fatigue-related changes in running mechanics. Hence, this study aimed to quantify and correct the subject-specific effects of running speed and stride frequency on changes in impact-related running mechanics during a fatiguing outdoor run. Seven runners ran a competitive marathon while peak tibial acceleration and knee angles were measured with inertial measurement units. Running speed was measured through sports watches. Median values over segments of 25 strides throughout the marathon were computed and used to create subject-specific multiple linear regression models. These models predicted peak tibial acceleration, knee angles at initial contact, and maximum stance phase knee flexion based on running speed and stride frequency. Data were corrected for individual speed and stride frequency effects during the marathon. The speed and stride frequency corrected and uncorrected data were divided into ten stages to investigate the effect of marathon stage on mechanical quantities. This study showed that running speed and stride frequency explained, on average, 20%-30% of the variance in peak tibial acceleration, knee angles at initial contact, and maximum stance phase knee angles while running in an uncontrolled setting. Regression coefficients for speed and stride frequency varied strongly between subjects. Speed and stride frequency corrected peak tibial acceleration, and maximum stance phase knee flexion increased throughout the marathon. At the same time, uncorrected maximum stance phase knee angles showed no significant differences between marathon stages due to a decrease in running speed. Hence, subject-specific effects of changes in speed and stride frequency influence the interpretation of running mechanics and are relevant when monitoring, or comparing the gait pattern between runs in uncontrolled environments.

Peak tibial acceleration should not be used as indicator of tibial bone loading during running.

Peak tibial acceleration (PTA) is a widely used indicator of tibial bone loading. Indirect bone loading measures are of interest to reduce the risk of stress fractures during running. However, tibial compressive forces are caused by both internal muscle forces and external ground reaction forces. PTA might reflect forces from outside the body, but likely not the compressive force from muscles on the tibial bone. Hence, the strength of the relationship between PTA and maximum tibial compression forces in rearfoot-striking runners was investigated. Twelve runners ran on an instrumented treadmill while tibial acceleration was captured with accelerometers. Force plate and inertial measurement unit data were spatially aligned with a novel method based on the centre of pressure crossing a virtual toe marker. The correlation coefficient between maximum tibial compression forces and PTA was 0.04 ± 0.14 with a range of -0.15 to +0.28. This study showed a very weak and non-significant correlation between PTA and maximum tibial compression forces while running on a level treadmill at a single speed. Hence, PTA as an indicator for tibial bone loading should be reconsidered, as PTA does not provide a complete picture of both internal and external compressive forces on the tibial bone. .

Effects of level running-induced fatigue on running kinematics: A systematic review and meta-analysis.

BACKGROUND: Runners have a high risk of acquiring a running-related injury. Understanding the mechanisms of impact force attenuation into the body when a runner fatigues might give insight into the role of running kinematics on the aetiology of overuse injuries. RESEARCH QUESTIONS: How do running kinematics change due to running-induced fatigue? And what is the influence of experience level on changes in running kinematics due to fatigue? METHODS: Three electronic databases were searched: PubMed, Web of Science, and Scopus. This resulted in 33 articles and 19 kinematic quantities being included in this review. A quality assessment was performed on all included articles and meta-analyses were performed for 18 kinematic quantities. RESULTS AND SIGNIFICANCE: Main findings included an increase in peak acceleration at the tibia and a decrease in leg stiffness after a fatiguing protocol. Additionally, level running-induced fatigue increased knee flexion at initial contact and maximum knee flexion during swing. An increase in vertical centre of mass displacement was found in novice but not in experienced runners with fatigue. Overall, runners changed their gait pattern due to fatigue by moving to a smoother gait pattern (i.e. more knee flexion at initial contact and during swing, decreased leg stiffness). However, these changes were not sufficient to prevent an increase in peak accelerations at the tibia after a fatigue protocol. Large inter-individual differences in responses to fatigue were reported. Hence, it is recommended to investigate changes in running kinematics as a result of fatigue on a subject-specific level since group-level analysis might mask individual responses.

Drift-Free 3D Orientation and Displacement Estimation for Quasi-Cyclical Movements Using One Inertial Measurement Unit: Application to Running.

A Drift-Free 3D Orientation and Displacement estimation method (DFOD) based on a single inertial measurement unit (IMU) is proposed and validated. Typically, body segment orientation and displacement methods rely on a constant- or zero-velocity point to correct for drift. Therefore, they are not easily applicable to more proximal segments than the foot. DFOD uses an alternative single sensor drift reduction strategy based on the quasi-cyclical nature of many human movements. DFOD assumes that the quasi-cyclical movement occurs in a quasi-2D plane and with an approximately constant cycle average velocity. DFOD is independent of a constant- or zero-velocity point, a biomechanical model, Kalman filtering or a magnetometer. DFOD reduces orientation drift by assuming a cyclical movement, and by defining a functional coordinate system with two functional axes. These axes are based on the mean acceleration and rotation axes over multiple complete gait cycles. Using this drift-free orientation estimate, the displacement of the sensor is computed by again assuming a cyclical movement. Drift in displacement is reduced by subtracting the mean value over five gait cycle from the free acceleration, velocity, and displacement. Estimated 3D sensor orientation and displacement for an IMU on the lower leg were validated with an optical motion capture system (OMCS) in four runners during constant velocity treadmill running. Root mean square errors for sensor orientation differences between DFOD and OMCS were 3.1 ± 0.4° (sagittal plane), 5.3 ± 1.1° (frontal plane), and 5.0 ± 2.1° (transversal plane). Sensor displacement differences had a root mean square error of 1.6 ± 0.2 cm (forward axis), 1.7 ± 0.6 cm (mediolateral axis), and 1.6 ± 0.2 cm (vertical axis). Hence, DFOD is a promising 3D drift-free orientation and displacement estimation method based on a single IMU in quasi-cyclical movements with many advantages over current methods.

Detection of foot contact in treadmill running with inertial and optical measurement systems.

In running assessments, biomechanics of the stance phase are often measured to understand external loads applied to the body. Identifying time of initial foot contact can be challenging in runners with different strike patterns. Peak downward velocity of the pelvis (PDVP) has been validated in a laboratory setting to detect initial contact. Inertial measurement units (IMUs) allow measurements of kinematic variables outside laboratory settings. The aim of this study was to validate the PDVP method using an inertial and optical motion capture system to detect initial contact at different speeds and foot strike patterns compared to the force sensing criterion. Twenty healthy runners ran for two minutes at 11, 13, and 15 km/h on a force-instrumented treadmill. 3D kinematics were obtained from an optical motion capture system and an 8-sensor inertial system. A generalized estimating equation showed no effect of footstrike pattern on the time difference (offset) between initial contact based on an inertial or optical system and the force sensing criterion. There was a significant main effect of speed on offset, in which offsets decreased with higher speeds. There was no interaction effect of speed and foot strike pattern on the offsets. Offsets ranged from 21.7 ± 0.2 ms for subjects running at 15 km/h (inertial versus force sensing criterion) to 27.2 ± 0.1 ms for subjects running at 11 km/h (optical versus force sensing criterion). These findings support the validity of the PDVP method obtained from optical and inertial systems to detect initial contact in different footstrike patterns and at different running speeds.

The effect of mono- versus multi-segment musculoskeletal models of the foot on simulated triceps surae lengths in pathological and healthy gait.

BACKGROUND: Estimating muscle-tendon complex (MTC) lengths is important for planning of soft tissue surgery and evaluating outcomes, e.g. in children with cerebral palsy (CP). Conventional musculoskeletal models often represent the foot as one rigid segment, called a mono-segment foot model (mono-SFM). However, a multi-segment foot model (multi-SFM) might provide better estimates of triceps surae MTC lengths, especially in patients with foot deformities. RESEARCH QUESTION: What is the effect of a mono- versus a multi-SFM on simulated ankle angles and triceps surae MTC lengths during gait in typically developing subjects and in children with CP with equinus, cavovarus or planovalgus foot deformities? METHODS: 50 subjects were included, 10 non-affected adults, 10 typically developing children, and 30 children with spastic CP and foot deformities. During walking trials, marker trajectories were collected for two marker models, including a mono- and multi-segment foot; respectively Newington gait model and Oxford foot model. Two musculoskeletal lower body models were constructed in OpenSim with either a mono- or multi-SFM based on the corresponding marker models. Normalized triceps surae MTC lengths (soleus, gastrocnemius medialis and lateralis) and ankle angles were calculated and compared between models using statistical parametric mapping RM-ANOVAs. Root mean square error values between simulated MTC lengths were compared using Wilcoxon signed-rank and rank-sum tests. RESULTS: Mono-SFM simulated significantly more ankle dorsiflexion (7.5 ± 1.2°) and longer triceps surae lengths (difference; soleus:2.6 ± 0.29 %, gastrocnemius medialis:1.7 ± 0.2 %, gastrocnemius lateralis:1.8 ± 0.2%) than a multi-SFM. Differences between models were larger in children with CP compared to typically developing children and larger in the stance compared to the swing phase of gait. Largest differences were found in children with CP presenting with planovalgus (4.8 %) or cavovarus (3.8 %) foot deformities. SIGNIFICANCE: It is advisable to use a multi-SFM in musculoskeletal models when simulating triceps surae MTC lengths, especially in individuals with planovalgus or cavovarus foot deformities.

How Can People Be so Good at Intercepting Accelerating Objects if They Are so Poor at Visually Judging Acceleration?

People are known to be very poor at visually judging acceleration. Yet, they are extremely proficient at intercepting balls that fall under gravitational acceleration. How is this possible? We previously found that people make systematic errors when trying to tap on targets that move with different constant accelerations or decelerations on interleaved trials. Here, we show that providing contextual information that indicates how the target will decelerate on the next trial does not reduce such errors. Such errors do rapidly diminish if the same deceleration is present on successive trials. After observing several targets move with a particular acceleration or deceleration without attempting to tap on them, participants tapped as if they had never experienced the acceleration or deceleration. Thus, people presumably deal with acceleration when catching or hitting a ball by compensating for the errors that they made on preceding attempts.