Biomechanics of graded running: Part II—Joint kinematics and kinetics

Khassetarash, Arash; Vernillo, Gianluca; Martínez, Aaron; Baggaley, Michael; Giandolini, Marlène; Horvais, Nicolas; Millet, Guillaume Y.; Edwards, W. Brent

doi:10.1111/sms.13735

Cited by 28 publications

(36 citation statements)

References 32 publications

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“…Seki et al ( 2020 ) showed similar results between level and downhill running for maximal hip extension (level: 167° ± 13 vs. downhill: 168° ± 13). Additionally, in accordance with Khassetarash et al ( 2020 ), the present study displayed that hip angle range of motion increased with running speed, and therefore, hypothesis (c) is accepted. The increased hip angle range of motion at higher speeds is also associated with an increase in SL.…”

Section: Discussionsupporting

confidence: 92%

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Runners Adapt Different Lower-Limb Movement Patterns With Respect to Different Speeds and Downhill Slopes

Sundström

Kurz

Björklund

2021

Front. Sports Act. Living

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The aim of this study was to investigate the influence of slope and speed on lower-limb kinematics and energy cost of running. Six well-trained runners (VO2max 72 ± 6 mL·kg−1·min−1) were recruited for the study and performed (1) VO2max and energy cost tests and (2) an experimental running protocol at two speeds, 12 km·h−1 and a speed corresponding to 80% of VO2max (V80, 15.8 ± 1.3 km·h−1) on three different slopes (0°, −5°, and −10°), totaling six 5-min workload conditions. The workload conditions were randomly ordered and performed continuously. The tests lasted 30 min in total. All testing was performed on a large treadmill (3 × 5 m) that offered control over both speed and slope. Three-dimensional kinematic data of the right lower limb were captured during the experimental running protocol using eight infrared cameras with a sampling frequency of 150 Hz. Running kinematics were calculated using a lower body model and inverse kinematics approach. The generic model contained three, one, and two degrees of freedom at the hip, knee, and ankle joints, respectively. Oxygen uptake was measured throughout the experimental protocol. Maximum hip extension and flexion during the stance phase increased due to higher speed (p < 0.01 and p < 0.01, respectively). Knee extension at the touchdown and maximal knee flexion in the stance phase both increased on steeper downhill slopes (both p < 0.05). Ground contact time (GCT) decreased as the speed increased (p < 0.01) but was unaffected by slope (p = 0.73). Runners modified their hip movement pattern in the sagittal plane in response to changes in speed, whereas they altered their knee movement pattern during the touchdown and stance phases in response to changes in slope. While energy cost of running was unaffected by speed alone (p = 0.379), a shift in energy cost was observed for different speeds as the downhill gradient increased (p < 0.001). Energy cost was lower at V80 than 12 km·h−1 on a −5° slope but worse on a −10° slope. This indicates that higher speeds are more efficient on moderate downhill slopes (−5°), while lower speeds are more efficient on steeper downhill slopes (−10°).

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Section: Discussionsupporting

confidence: 92%

“…However, Buczek and Cavanagh ( 1990 ) showed that greater downhill slope was associated with greater knee flexion. Moreover, Khassetarash et al ( 2020 ) showed that greater speed was associated with a greater hip angle range of motion, at both level and downhill slopes.…”

Section: Introductionmentioning

confidence: 99%

Runners Adapt Different Lower-Limb Movement Patterns With Respect to Different Speeds and Downhill Slopes

Sundström

Kurz

Björklund

2021

Front. Sports Act. Living

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“…We analyzed a pre-existing dataset (Baggaley et al, 2019;Khassetarash et al, 2020;Vernillo et al, 2020) where 21 subjects ran at a combination of running speeds and slopes. Two subjects were excluded from the current analysis due to equipment data acquisition errors, leaving 19 subjects remaining (10 Male, 9 Female; 29 ± 9 years, 173 ± 9 cm, 68.1 ± 9.9 kg).…”

Section: Subjectsmentioning

confidence: 99%

“…These three summary statistics were normalized to a range of 0 -1 and represent 6 (3 features x 2 acceleration axes) of the 13 input features. The remaining input features were selected due to their effect on running kinetics and kinematics: subject height, body mass, running speed, slope, and percentage of steps classified as either a rearfoot, midfoot, or forefoot strike (Almeida, Davis and Lopes, 2015;Khassetarash et al, 2020;Vernillo et al, 2020;Vincent et al, 2020;Alcantara et al, 2021). We chose not to include step frequency as an input feature, despite the presence of the ±10% preferred step frequency conditions, to increase the variability in the accelerometer data used to predict the GRF waveforms.…”

Section: Feature Engineeringmentioning

confidence: 99%

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

Alcantara

Edwards

Millet

et al. 2021

Preprint

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BackgroundGround reaction forces (GRFs) are important for understanding the biomechanics of human movement but the measurement of GRFs is generally limited to a laboratory environment. Wearable devices like accelerometers have been used to measure biomechanical variables outside the laboratory environment, but they cannot directly measure GRFs. Previous studies have used neural networks to predict the entire GRF waveform during the stance phase from wearable device data, but these networks require normalization of GRFs to the duration of a step or stance phase, resulting in a loss of the GRF waveform’s temporal component. Additionally, previous studies have predicted GRF waveforms during level-ground, but not uphill or downhill running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes, while maintaining the GRF waveform’s temporal component, could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment.PurposeWe sought to develop a recurrent neural network capable of predicting normal GRF waveforms across a range of running speeds and slopes using data from accelerometers located on the sacrum and shoe.Methods19 subjects completed 30-s running trials on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, and 4.17 m/s) per slope. One biaxial accelerometer was adhered to the sacrum and two uniaxial accelerometers were adhered to the right shoe during all trials. Accelerometers on the shoe were used to classify foot strike patterns as rearfoot, midfoot, or forefoot, and sacral acceleration data were divided into overlapping 12-ms windows, allowing the neural network to iteratively predict the experimentally-measured normal GRF waveform frame-by-frame. The mean, SD, and range of accelerometer data for each 12-ms window were included as neural network input features, along with the subject’s body mass, height, running speed, slope, and percentage of a trial’s steps classified as a rearfoot, midfoot, or forefoot strike. We assessed the accuracy and generalizability of the neural network using leave-one-subject-out cross validation, which provided an ensemble of Root Mean Square Error (RMSE) and relative RMSE (rRMSE) values comparing the normal GRF waveform predicted by the neural network to the normal GRF waveform measured by the force-measuring treadmill. Additionally, we calculated the mean absolute percent error (MAPE) of step frequency, contact time, normal impulse, normal GRF active peak, and loading rate between the predicted and measured GRF waveforms.ResultsThe average ± SD RMSE was 0.16 ± 0.04 BW and rRMSE was 6.4 ± 1.5% for neural network predictions of each subject’s normal GRF waveform compared to measured GRF waveforms across all conditions. RMSE values were lower during slow uphill running (2.5 m/s, +10°; 0.13 ± 0.07 BW) compared to fast downhill running (4.17 m/s, −10°; 0.20 ± 0.05 BW). The MAPE ± SD for step frequency was 0.1 ± 0.1%, contact time was 4.9 ± 4.0%, normal impulse was 6.4 ± 6.9%, normal GRF active peak was 8.5 ± 8.2%, and loading rate was 27.6 ± 36.1%.ConclusionsWe developed a recurrent neural network that uses accelerometer data to predict the continuous normal GRF waveform across a range of running speeds and slopes. The neural network does not require preliminary identification of the stance phase, maintains the temporal component of the GRF waveform, can be applied to up- and downhill running, and facilitates the prediction of kinetic and kinematic variables outside the laboratory environment. This represents a substantial step towards accurately quantifying and monitoring the external loads experienced by the body when running outdoors.

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“…19 To date, however, only 1 study 20 suggested that novice runners who increased their weekly volume by more than 30% could be more prone to develop distance-based running injuries such as PFP than runners who changed their weekly running distance by less than 10%. This result may be due to the complexity of external load (influenced by, for example, speed, 21 hills, 22 foot-strike pattern, 23 and cadence 24 ) but may also be due to the difficulty of measuring maximum capacity, which may vary greatly among individuals and even among days in the same runner. As proposed by Wiese-Bjornstal, 25 the sport injury risk consists of a combination of biological, physical, psychological, and sociocultural factors.…”

Section: Educating Patients On the Balance Between Load And Capacitymentioning

confidence: 99%

A Contemporary Approach to Patellofemoral Pain in Runners

Esculier

Maggs

et al. 2020

Journal of Athletic Training

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Patellofemoral pain (PFP) is among the most common injuries in recreational runners. Current evidence does not identify alignment, muscle weakness, and patellar maltracking or a combination of these as causes of PFP. Rather than solely investigating biomechanics, we suggest a holistic approach to address the causes of PFP. Both external loads, such as changes in training parameters and biomechanics, and internal loads, such as sleep and psychological stress, should be considered. As for the management of runners with PFP, recent research suggested that various interventions can be considered to help symptoms, even if these interventions target biomechanical factors that may not have caused the injury in the first place. In this Current Concepts article, we describe how the latest evidence on education about training modifications, strengthening exercises, gait and footwear modifications, and psychosocial factors can be applied when treating runners with PFP. The importance of maintaining relative homeostasis between load and capacity will be emphasized. Recommendations for temporary or longer-term interventions will be discussed. A holistic, evidence-based approach should consist of a graded exposure to load, including movement, exercise, and running, while considering the capacity of the individual, including sleep and psychosocial factors. Cost, accessibility, and the personal preferences of patients should also be considered.

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Biomechanics of graded running: Part II—Joint kinematics and kinetics

Cited by 28 publications

References 32 publications

Runners Adapt Different Lower-Limb Movement Patterns With Respect to Different Speeds and Downhill Slopes

Runners Adapt Different Lower-Limb Movement Patterns With Respect to Different Speeds and Downhill Slopes

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

A Contemporary Approach to Patellofemoral Pain in Runners

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