Mind to move: Differences in running biomechanics between sensing and intuition shod runners.

Delving into the complexities of embodied cognition unveils the intertwined influence of mind, body, and environment. The connection of physical activity with cognition sparks a hypothesis linking motion and personality traits. Hence, this study explored whether personality traits could be linked to biomechanical variables characterizing running forms. To do so, 80 runners completed three randomized 50-m running-trials at 3.3, 4.2, and 5m/s during which their running biomechanics [ground contact time (tc), flight time (tf), duty factor (DF), step frequency (SF), leg stiffness (kleg), maximal vertical ground reaction force (Fmax), and maximal leg compression of the spring during stance (ΔL)] was evaluated. In addition, participants' personality traits were assessed through the Myers-Briggs Type Indicator (MBTI) test. The MBTI classifies personality traits into one of two possible categories along four axes: extraversion-introversion; sensing-intuition; thinking-feeling; and judging-perceiving. This exploratory study offers compelling evidence that personality traits, specifically sensing and intuition, are associated with distinct running biomechanics. Individuals classified as sensing demonstrated a more grounded running style characterized by prolonged tc, shorter tf, higher DF, and greater ΔL compared to intuition individuals (p≤0.02). Conversely, intuition runners exhibited a more dynamic and elastic running style with a shorter tc and higher kleg than their sensing counterparts (p≤0.02). Post-hoc tests revealed a significant difference in tc between intuition and sensing runners at all speeds (p≤0.02). According to the definition of each category provided by the MBTI, sensing individuals tend to focus on concrete facts and physical realities while intuition individuals emphasize abstract concepts and patterns of information. These results suggest that runners with sensing and intuition personality traits differ in their ability to use their lower limb structures as springs. Intuition runners appeared to rely more in the stretch-shortening cycle to energetically optimize their running style while sensing runners seemed to optimize running economy by promoting more forward progression than vertical oscillations. This study underscores the intriguing interplay between personality traits of individuals and their preferred movement patterns.

Exercise and gait/movement analyses in treatment and diagnosis of Parkinson's Disease.

Cardinal motor symptoms in Parkinson's disease (PD) include bradykinesia, rest tremor and/or rigidity. This symptomatology can additionally encompass abnormal gait, balance and postural patterns at advanced stages of the disease. Besides pharmacological and surgical therapies, physical exercise represents an important strategy for the management of these advanced impairments. Traditionally, diagnosis and classification of such abnormalities have relied on partially subjective evaluations performed by neurologists during short and temporally scattered hospital appointments. Emerging sports medical methods, including wearable sensor-based movement assessment and computational-statistical analysis, are paving the way for more objective and systematic diagnoses in everyday life conditions. These approaches hold promise to facilitate customizing clinical trials to specific PD groups, as well as personalizing neuromodulation therapies and exercise prescriptions for each individual, remotely and regularly, according to disease progression or specific motor symptoms. We aim to summarize exercise benefits for PD with a specific emphasis on gait and balance deficits, and to provide an overview of recent advances in movement analysis approaches, notably from the sports science community, with value for diagnosis and prognosis. Although such techniques are becoming increasingly available, their standardization and optimization for clinical purposes is critically missing, especially in their translation to complex neurodegenerative disorders such as PD. We highlight the importance of integrating state-of-the-art gait and movement analysis approaches, in combination with other motor, electrophysiological or neural biomarkers, to improve the understanding of the diversity of PD phenotypes, their response to therapies and the dynamics of their disease progression.

The effect of severe obesity on three-dimensional ground reaction force signals during walking.

BACKGROUND: The gait pattern of adults with class I obesity [30 ≤ body mass index < 35kg/m2] was characterized by altered three-dimensional ground reaction force signals compared to lean adults (18.5 ≤ body mass index < 25 kg/m2). However, results might not be generalizable to adults with severe obesity (class II and III; body mass index ≥ 35 kg/m2). Hence, the purpose of the present study was to investigate the differences in relative ground reaction force signals, i.e., normalized by body weight, between adults with severe obesity and lean adults using functional principal component analysis. METHODS: Thirteen lean and eighteen sedentary adults with severe obesity performed a 5-min walking trial (1.11 m/s) on an instrumented treadmill. The first five functional principal components of the relative force signals (mediolateral, anterior-posterior, and vertical directions) were obtained using functional principal component analysis. Functional principal component scores were compared between groups using an analysis of covariance with age as covariable. FINDINGS: Functional principal component analysis reported a statistically significant group effect for first functional principal component score for mediolateral (P = 0.004), and second and fifth functional principal component scores for anterior-posterior (P ≤ 0.02) force signals. Adults with severe obesity displayed a greater mediolateral force during most of the stance but similar magnitudes of the anterior-posterior and vertical forces compared to lean adults. INTERPRETATION: Therefore, increasing the obesity level accentuates differences in mediolateral force but promotes no specific changes in anterior-posterior force likely due to chronic loading adaptation.

Isolating the speed factor is crucial in gait analysis for Parkinson's disease.

Introduction: Parkinson's disease (PD) is characterized by an alteration of the walking gait, frequently including a slower self-selected walking speed (SSWS). Although the reduction of walking speed is inherent to people with PD, such speed reduction also represents a potential confounding factor that might partly explain the observed gait differences between PD and control participants. Methods: In this study, each participant walked along a 25 m level corridor during which vertical ground reaction force signals were recorded using shoes equipped with eight pressure sensors. Vertical ground reaction force signals (using statistical parametric mapping) and temporal and kinetic variables as well as their related variability and asymmetry (using Student's t-test) were compared between PD (n = 54) and walking-speed-matched control subjects (n = 39). Results: Statistical parametric mapping did not yield significant differences between PD and control groups for the vertical ground reaction force signal along the walking stance phase. Stride time and single support time (equivalent to swing time) were shorter and peak vertical ground reaction force was larger in PD patients compared to controls (p ≤ 0.05). However, the single support time was no longer different between people with PD and healthy subjects when expressed relatively to stride time (p = 0.07). While single support, double support, and stance times were significantly more variable and asymmetric for PD than for the control group (p ≤ 0.05), stride time was similar (p ≥ 0.07). Discussion: These results indicate that at matched SSWS, PD patients adopt a higher cadence than control participants. Moreover, the temporal subdivision of the walking gait of people with PD is similar to healthy individuals but the coordination during the double support phase is different. Hence, this study indicates that isolating the speed factor is crucial in gait analysis for PD.

Biomechanical adaptations during exhaustive runs at 90 to 120% of peak aerobic speed.

The aim of this study was to examine how running biomechanics (spatiotemporal and kinetic variables) adapt with exhaustion during treadmill runs at 90, 100, 110, and 120% of the peak aerobic speed (PS) of a maximal incremental aerobic test. Thirteen male runners performed a maximal incremental aerobic test on an instrumented treadmill to determine their PS. Biomechanical variables were evaluated at the start, mid, and end of each run until volitional exhaustion. The change of running biomechanics with fatigue was similar among the four tested speeds. Duty factor and contact and propulsion times increased with exhaustion (P ≤ 0.004; F ≥ 10.32) while flight time decreased (P = 0.02; F = 6.67) and stride frequency stayed unchanged (P = 0.97; F = 0.00). A decrease in vertical and propulsive peak forces were obtained with exhaustion (P ≤ 0.002; F ≥ 11.52). There was no change in the impact peak with exhaustion (P = 0.41; F = 1.05). For runners showing impact peaks, the number of impact peaks increased (P ≤ 0.04; [Formula: see text] ≥ 6.40) together with the vertical loading rate (P = 0.005; F = 9.61). No changes in total, external, and internal positive mechanical work was reported with exhaustion (P ≥ 0.12; F ≤ 2.32). Results suggest a tendency towards a "smoother" vertical and horizontal running pattern with exhaustion. A smoother running pattern refers to the development of protective adjustments, leading to a reduction of the load applied to the musculoskeletal system at each running step. This transition seemed continuous between the start and end of the running trials and could be adopted by the runners to decrease the muscle force level during the propulsion phase. Despite these changes with exhaustion, there were no changes in either gesture speed (no alteration of stride frequency) or positive mechanical work, advocating that runners unconsciously organize themselves to maintain a constant whole-body mechanical work output.

Concurrent endurance training with either plyometric or dynamic body-weight training both improve running economy with minimal or no changes in running biomechanics.

We compared the effects of two 8-week concurrent strength and endurance trainings (CSETs) on running economy (RE) and running biomechanics, and we explored whether the effects on running biomechanics were mediated by responder status [high vs low responder based on -2.6% change in RE]. Thirty-one male recreational runners were randomly assigned to a standard endurance running training combined with either plyometric (CSET-PLY) or dynamic body-weight (CSET-DYN) training. RE and running biomechanics [contact (tc) and flight (tf) time, step frequency (SF), duty factor (DF), and leg stiffness (kleg)] were measured pre- and post-intervention. RE significantly improved following CSET (RE = -2.1 ± 3.9%; p = 0.005) and no changes in tc, DF, SF, and kleg (p ≥ 0.10) but a shorter tf (p ≥ 0.03) from pre- to post-intervention were seen. The prevalence of high responders was 42% (RE = -5.7 ± 2.4%). Among high responders, there were no changes in running biomechanics except participants following CSET-DYN who increased their SF (+3%). These results indicate that improvements in RE obtained through CSET-PLY and CSET-DYN involve minimal to no changes in running biomechanics and that there was not a training modality, which was better than the other. More detailed biomechanical assessments involving kinematics, kinetics, and electromyography could shed light on the underlying mechanisms of RE improvement.

Comparison of different machine learning models to enhance sacral acceleration-based estimations of running stride temporal variables and peak vertical ground reaction force.

Machine learning (ML) was used to predict contact (tc) and flight (tf) time, duty factor (DF) and peak vertical force (Fv,max) from IMU-based estimations. One hundred runners ran on an instrumented treadmill (9-13 km/h) while wearing a sacral-mounted IMU. Linear regression (LR), support vector regression and two-layer neural-network were trained (80 participants) using IMU-based estimations, running speed, stride frequency and body mass. Predictions (remaining 20 participants) were compared to gold standard (kinetic data collected using the force plate) by calculating the mean absolute percentage error (MAPE). MAPEs of Fv,max did not significantly differ among its estimation and predictions (P = 0.37), while prediction MAPEs for tc, tf and DF were significantly smaller than corresponding estimation MAPEs (P ≤ 0.003). There were no significant differences among prediction MAPEs obtained from the three ML models (P ≥ 0.80). Errors of the ML models were equal to or smaller than (≤32%) the smallest real difference for the four variables, while errors of the estimations were not (15-45%), indicating that ML models were sufficiently accurate to detect a clinically important difference. The simplest ML model (LR) should be used to improve the accuracy of the IMU-based estimations. These improvements may be beneficial when monitoring running-related injury risk factors in real-world settings.

Accurate estimation of peak vertical ground reaction force using the duty factor in level treadmill running.

This study aimed to (1) construct a statistical model (SMM) based on the duty factor (DF) to estimate the peak vertical ground reaction force ( F v , max ) and (2) to compare the estimated F v , max to force plate gold standard (GSM). One hundred and fifteen runners ran at 9, 11, and 13 km/h. Force (1000 Hz) and kinematic (200 Hz) data were acquired with an instrumented treadmill and an optoelectronic system, respectively, to assess force-plate and kinematic based DFs. SMM linearly relates F v , max to the inverse of DF because DF was analytically associated with the inverse of the average vertical force during ground contact time and the latter was very highly correlated to F v , max . No systematic bias and a 4% root mean square error (RMSE) were reported between GSM and SMM using force-plate based DF values when considering all running speeds together. Using kinematic based DF values, SMM reported a systematic but small bias (0.05BW) and a 5% RMSE when considering all running speeds together. These findings support the use of SMM to estimate F v , max during level treadmill runs at endurance speeds if underlying DF values are accurately measured.

Using statistical parametric mapping to assess the association of duty factor and step frequency on running kinetic.

Duty factor (DF) and step frequency (SF) were previously defined as the key running pattern determinants. Hence, this study aimed to investigate the association of DF and SF on 1) the vertical and fore-aft ground reaction force signals using statistical parametric mapping; 2) the force related variables (peaks, loading rates, impulses); and 3) the spring-mass characteristics of the lower limb, assessed by computing the force-length relationship and leg stiffness, for treadmill runs at several endurance running speeds. One hundred and fifteen runners ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body three-dimensional kinematics (200 Hz) were acquired by an instrumented treadmill and optoelectronic system, respectively. Both lower DF and SF led to larger vertical and fore-aft ground reaction force fluctuations, but to a lower extent for SF than for DF. Besides, the linearity of the force-length relationship during the leg compression decreased with increasing DF or with decreasing SF but did not change during the leg decompression. These findings showed that the lower the DF and the higher the SF, the more the runner relies on the optimization of the spring-mass model, whereas the higher the DF and the lower the SF, the more the runner promotes forward propulsion.

PIMP Your Stride: Preferred Running Form to Guide Individualized Injury Rehabilitation.

Despite the wealth of research on injury prevention and biomechanical risk factors for running related injuries, their incidence remains high. It was suggested that injury prevention and reconditioning strategies should consider spontaneous running forms in a more holistic view and not only the injury location or specific biomechanical patterns. Therefore, we propose an approach using the preferred running form assessed through the Volodalen® method to guide injury prevention, rehabilitation, and retraining exercise prescription. This approach follows three steps encapsulated by the PIMP acronym. The first step (P) refers to the preferred running form assessment. The second step (I) is the identification of inefficiency in the vertical load management. The third step (MP) refers to the movement plan individualization. The answers to these three questions are guidelines to create individualized exercise pathways based on our clinical experience, biomechanical data, strength conditioning knowledge, and empirical findings in uninjured and injured runners. Nevertheless, we acknowledge that further scientific justifications with appropriate clinical trials and mechanistic research are required to substantiate the approach.

Examination of running pattern consistency across speeds.

Duty factor (DF) and step frequency (SF) are key running pattern determinants. However, running patterns may change with speed if DF and SF changes are inconsistent across speeds. We examined whether the relative positioning of runners was consistent: 1) across five running speeds (10-18 km/h) for four temporal variables [DF, SF, and their subcomponents: contact (tc) and flight (tf) time]; and 2) across these four temporal variables at these five speeds. Three-dimensional whole-body kinematics were acquired from 52 runners, and deviations from the median for each variable (normalised to minimum-maximum values) were extracted. Across speeds for all variables, correlations on the relative positioning of individuals were high to very high for 2-4 km/h speed differences, and moderate to high for 6-8 km/h differences. Across variables for all speeds, correlations were low between DF-SF, very high between DF-tf, and low to high between DF-tc, SF-tc, and SF-tf. Hence, the consistency in running patterns decreased as speed differences increased, suggesting that running patterns be assessed using a range of speeds. Consistency in running patterns at a given speed was low between DF and SF, corroborating suggestions that using both variables can encapsulate the full running pattern spectrum.

Duty factor and foot-strike pattern do not represent similar running pattern at the individual level.

Runners were classified using their duty factor (DF) and using their foot-strike pattern (FSP; rearfoot, midfoot, or forefoot strikers), determined from their foot-strike angle (FSA). High and low DF runners showed different FSPs but DF was assumed to not only reflect what happens at initial contact with the ground (more global than FSP/FSA). Hence, FSP and DF groups should not necessarily be constituted by the same runners. However, the relation between FSP and DF groups has never been investigated, leading to the aim of this study. One hundred runners ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body kinematics (200 Hz) were acquired by an instrumented treadmill and optoelectronic system and were used to classify runners according to their FSA and DF. Weak correlations were obtained between FSA and DF values and a sensitivity of 50% was reported between FSP and DF groups, i.e., only one in two runners was attributed to the DF group supposedly corresponding to the FSP group. Therefore, 'local' FSP/FSA and DF do not represent similar running pattern information when investigated at the individual level and DF should be preferred to FSP/FSA when evaluating the global running pattern of a runner.

Non-South East Asians have a better running economy and different anthropometrics and biomechanics than South East Asians.

Running biomechanics and ethnicity can influence running economy (RE), which is a critical factor of running performance. Our aim was to compare RE of South East Asian (SEA) and non-South East Asian (non-SEA) runners at several endurance running speeds (10-14 km/h) matched for on-road racing performance and sex. Secondly, we explored anthropometric characteristics and relationships between RE and anthropometric and biomechanical variables. SEA were 6% less economical (p = 0.04) than non-SEA. SEA were lighter and shorter than non-SEA, and had lower body mass indexes and leg lengths (p ≤ 0.01). In terms of biomechanics, a higher prevalence of forefoot strikers in SEA than non-SEA was seen at each speed tested (p ≤ 0.04). Furthermore, SEA had a significantly higher step frequency (p = 0.02), shorter contact time (p = 0.04), smaller footstrike angle (p < 0.001), and less knee extension at toe-off (p = 0.03) than non-SEA. Amongst these variables, only mass was positively correlated to RE for both SEA (12 km/h) and non-SEA (all speeds); step frequency, negatively correlated to RE for both SEA (10 km/h) and non-SEA (12 km/h); and contact time, positively correlated to RE for SEA (12 km/h). Despite the observed anthropometric and biomechanical differences between cohorts, these data were limited in underpinning the observed RE differences at a group level. This exploratory study provides preliminary indications of potential differences between SEA and non-SEA runners warranting further consideration. Altogether, these findings suggest caution when generalizing from non-SEA running studies to SEA runners.

A Single Sacral-Mounted Inertial Measurement Unit to Estimate Peak Vertical Ground Reaction Force, Contact Time, and Flight Time in Running.

Peak vertical ground reaction force (Fz,max), contact time (tc), and flight time (tf) are key variables of running biomechanics. The gold standard method (GSM) to measure these variables is a force plate. However, a force plate is not always at hand and not very portable overground. In such situation, the vertical acceleration signal recorded by an inertial measurement unit (IMU) might be used to estimate Fz,max, tc, and tf. Hence, the first purpose of this study was to propose a method that used data recorded by a single sacral-mounted IMU (IMU method: IMUM) to estimate Fz,max. The second aim of this study was to estimate tc and tf using the same IMU data. The vertical acceleration threshold of an already existing IMUM was modified to detect foot-strike and toe-off events instead of effective foot-strike and toe-off events. Thus, tc and tf estimations were obtained instead of effective contact and flight time estimations. One hundred runners ran at 9, 11, and 13 km/h. IMU data (208 Hz) and force data (200 Hz) were acquired by a sacral-mounted IMU and an instrumented treadmill, respectively. The errors obtained when comparing Fz,max, tc, and tf estimated using the IMUM to Fz,max, tc, and tf measured using the GSM were comparable to the errors obtained using previously published methods. In fact, a root mean square error (RMSE) of 0.15 BW (6%) was obtained for Fz,max while a RMSE of 20 ms was reported for both tc and tf (8% and 18%, respectively). Moreover, even though small systematic biases of 0.07 BW for Fz,max and 13 ms for tc and tf were reported, the RMSEs were smaller than the smallest real differences [Fz,max: 0.28 BW (11%), tc: 32.0 ms (13%), and tf: 32.0 ms (30%)], indicating no clinically important difference between the GSM and IMUM. Therefore, these results support the use of the IMUM to estimate Fz,max, tc, and tf for level treadmill runs at low running speeds, especially because an IMU has the advantage to be low-cost and portable and therefore seems very practical for coaches and healthcare professionals.

There Is No Global Running Pattern More Economic Than Another at Endurance Running Speeds.

PURPOSE: The subjective Volodalen® score (V®score) and the objective duty factor metric can both assess global running patterns. The authors aimed to investigate the relation between running economy (RE) at endurance running speeds and the global running pattern quantified using both subjective and objective measures. METHODS: RE and 3-dimensional whole-body kinematics were acquired by indirect calorimetry and an optoelectronic system, respectively, for 52 trained runners during treadmill runs at 10, 12, and 14 km/h. RESULTS: Correlations between RE and V®score and RE and duty factor were negligible and nonsignificant across speeds tested (P ≥ .20), except for a low and significant correlation between RE and V®score at 10 km/h. CONCLUSIONS: These findings suggest there is no global running pattern more economic than another at endurance running speeds. Therefore, there is no advantage of choosing, favoring, or prescribing one specific global running pattern along a continuum based on V®score or duty factor metrics, and coaches should not try to modify the spontaneous running pattern of runners at endurance running speed to improve RE.

Oxygen Uptake at Critical Speed and Power in Running: Perspectives and Practical Applications.

PURPOSE: Intensity domains are recommended when prescribing exercise, and critical power/speed (CP/CS) was designated the "gold standard" when determining maximal metabolic steady state. CS is the running analog of CP for cycle ergometry. However, a CP for running could be useful for controlling intensity when training in any type of condition. Therefore, this study aimed to estimate external, internal, and total CP (CPext, CPint, and CPtot), obtained based on running power calculations, and verified whether they occurred at the same percentage of peak oxygen uptake as the usual CS. Furthermore, this study examined whether selecting strides at the start, half, or end of the exhaustive runs to calculate running power influenced the estimation of the 3 CPs. METHODS: Thirteen male runners performed a maximal incremental aerobic test and 4 exhaustive runs (90%, 100%, 110%, 120% peak speed) on a treadmill. The estimations of CS and CPs were obtained using a 3-parameter mathematical model fitted using weighted least square. RESULTS: CS was estimated at 4.3 m/s while the estimates of CPext, CPint, and CPtot were 5.2, 2.6, and 7.8 W/kg, respectively. The corresponding V˙O2 for CS was 82.5 percentage of peak oxygen uptake and 81.3, 79.7, and 80.6 percentage of peak oxygen uptake for CPext, CPint, and CPtot, respectively. No systematic bias was reported when comparing CS and CPext, as well as the 3 different CPs, whereas systematic biases of 2.8% and 1.8% were obtained for the comparison among CS and CPint and CPtot, respectively. Nonetheless, the V˙O2 for CS and CPs were not statistically different (P = .09). Besides, no effect of the time stride selection for CPs as well as their resulting V˙O2 was obtained (P ≥ .44). CONCLUSIONS: The systematic biases among V˙O2 at CS and CPint and CPtot were not clinically relevant. Therefore, CS and CPs closely represent the same fatigue threshold in running. The knowledge of CP in running might prove to be useful for both athletes and coaches, especially when combined with instantaneous running power. Indeed, this combination might help athletes controlling their targeted training intensity and coaches prescribing a training session in any type of condition.

A Multivariate Polynomial Regression to Reconstruct Ground Contact and Flight Times Based on a Sine Wave Model for Vertical Ground Reaction Force and Measured Effective Timings.

Effective contact ( t c e ) and flight ( t f e ) times, instead of ground contact ( t c ) and flight ( t f ) times, are usually collected outside the laboratory using inertial sensors. Unfortunately, t c e and t f e cannot be related to t c and t f because the exact shape of vertical ground reaction force is unknown. However, using a sine wave approximation for vertical force, t c e and t c as well as t f e and t f could be related. Indeed, under this approximation, a transcendental equation was obtained and solved numerically over a t c e  x   t f e grid. Then, a multivariate polynomial regression was applied to the numerical outcome. In order to reach a root-mean-square error of 0.5 ms, the final model was given by an eighth-order polynomial. As a direct application, this model was applied to experimentally measured t c e values. Then, reconstructed t c (using the model) was compared to corresponding experimental ground truth. A systematic bias of 35 ms was depicted, demonstrating that ground truth t c values were larger than reconstructed ones. Nonetheless, error in the reconstruction of t c from t c e was coming from the sine wave approximation, while the polynomial regression did not introduce further error. The presented model could be added to algorithms within sports watches to provide robust estimations of t c and t f in real time, which would allow coaches and practitioners to better evaluate running performance and to prevent running-related injuries.

A novel kinematic detection of foot-strike and toe-off events during noninstrumented treadmill running to estimate contact time.

Contact time (tc) relies upon the accuracy of foot-strike and toe-off events, for which ground reaction force (GRF) is the gold standard. However, force plates are not always available, e.g., when running on a noninstrumented treadmill. In this situation, a kinematic algorithm (KA) - an algorithm based on motion capture data - might be used if it performs equally for all foot-strike angles across speeds. The purpose of this study was to propose a novel KA, using a combination of heel and toe kinematics (three markers per foot), to detect foot-strike and toe-off and compare it to GRF at different speeds and across foot-strike angles. One hundred runners ran at 9 km/h, 11 km/h, and 13 km/h. Force data and whole-body kinematic data were acquired by an instrumented treadmill and optoelectronic system. Foot-strike and toe-off showed small systematic biases between GRF and KA at all speeds (≤5 ms), except toe-off at 11 km/h (no bias). The root mean square error (RMSE) was ≤9 ms and was mostly constant across foot-strike angles for toe-off (7.4 ms) but not for foot-strike (4.1-11.1 ms). Small systematic biases (≤8 ms) and significant differences (P ≤ 0.01) were reported for tc at all speeds, and the RMSE was ≤14 ms (≤5%). The RMSE for tc increased with increasing foot-strike angle (3.5-5.4%). Nonetheless, this novel KA computed smaller errors than existing methods for foot-strike, toe-off, and tc. Therefore, this study supports the use of this novel KA to accurately estimate foot-strike, toe-off, and tc from kinematic data obtained during noninstrumented treadmill running independent of the foot-strike angle.

Estimating effective contact and flight times using a sacral-mounted inertial measurement unit.

Effective ground contact (tce) and flight (tfe) times were proven to be more appropriate to decipher the landing-take-off asymmetry of running than usual ground contact (tc) and flight (tf) times. To measure these effective timings, force plate is the gold standard method (GSM), though not very portable overground. In such situation, alternatives could be to use portable tools such as inertial measurement unit (IMU). Therefore, the purpose of this study was to propose a method that uses the vertical acceleration recorded using a sacral-mounted IMU to estimate tce and tfe and to compare these estimations to those from GSM. Besides, tce and tfe were used to evaluate the landing-take-off asymmetry, which was further compared to GSM. One hundred runners ran at 9, 11, and 13 km/h. Force data (200 Hz) and IMU data (208 Hz) were acquired by an instrumented treadmill and a sacral-mounted IMU, respectively. The comparison between GSM and IMU method depicted root mean square error ≤22 ms (≤14%) for tce and tfe along with small systematic biases (≤20 ms) for each tested speed. These errors are similar to previously published methods that estimated usual tc and tf. The systematic biases on tce and tfe were subtracted before calculating the landing-take-off asymmetry, which permitted to correctly evaluate it at a group level. Therefore, the findings of this study support the use of this method based on vertical acceleration recorded using a sacral-mounted IMU to estimate tce and tfe for level treadmill runs and to evaluate the landing-take-off asymmetry but only after subtraction of systematic biases and at a group level.