Step length and grade effects on energy absorption and impact attenuation in running

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

doi:10.1080/17461391.2019.1664639

Cited by 15 publications

(21 citation statements)

References 36 publications

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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%

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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Section: Subjectsmentioning

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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“…rear-, mid-and fore-foot) can influence the mechanical strain applied to lower limb muscles during running [214][215][216][217]. Accordingly, manipulating foot stride frequency [218] and foot strike pattern [219] during DR could reduce EIMD including neuromuscular fatigue. Following an intermittent 45-min treadmill DR (9 × 5-min; slope: − 15%; speed: 10.5 km h −1 ), Rowlands et al [68] reported lower MVC torque decrements post-DR using an understride strategy (− 8.8% MVC torque; 92% preferred stride length) compared with an overstride (− 14.7% MVC torque; 108% preferred stride length) or preferred stride strategy (− 15.5% MVC torque).…”

Section: Optimisation Of Stride and Foot Strike Patternmentioning

confidence: 99%

“…3 a Schematic representation of the time course of alterations following downhill running (DR) and b current scientific evidence on the benefits of different adaptation strategies to DR (i.e. prior exposure to DR [ 46 , 63 , 67 , 68 , 85 , 92 , 121 , 141 , 143 , 165 , 181 , 184 , 185 , 192 , 193 , 196 , 197 ], preconditioning strategies [ 16 , 81 ], DR training [ 14 , 209 , 210 , 254 ], changes in stride pattern [ 32 , 67 , 68 , 218 , 219 , 221 , 220 , 228 , 255 ], the use of lower limb compression garments [ 15 , 236 , 237 ], and the use of specific footwear [ 28 , 149 , 253 , 256 ]. In a and b , orange, blue, red, purple, and green spheres correspond to isometric MVC force/torque loss, changes in running economy and mechanics, ultrastructural alterations, inflammation and oedema, and muscle soreness, respectively.…”

Section: Factors Influencing the Magnitude Of Muscle Damage Followingmentioning

confidence: 99%

“…Recently, Vincent et al [ 221 ] corroborated previous results by showing that slight changes in PSF (± 5%) during DR had no detrimental effect on the energy cost of running. In addition, the choice of stride length affects the energy absorption and impact attenuation characteristics of lower extremity joints during DR [ 218 ]. More precisely, the authors reported that shortening step length by 10% (i.e.…”

Section: Adaptation Strategies To Downhill Runningmentioning

confidence: 99%

“…using an overstride strategy) reduced knee joint (at a slope corresponding to − 8.8% and − 17.6%) and hip energy absorption (at a slope corresponding to − 8.8%) and enhanced impact attenuation. Such a strategy may be applied to reduce EIMD and maintain running performance [ 218 ]. It is worth mentioning that, on level grade, novice runners ran with a PSF that was about 8% lower than their optimal stride frequency (established from polynomial equations with stride frequency, and HR) whereas the PSF of experienced and trained runners was significantly closer to their optimal one (~ − 3%) [ 222 ].…”

Section: Adaptation Strategies To Downhill Runningmentioning

confidence: 99%

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Downhill Running: What Are The Effects and How Can We Adapt? A Narrative Review

et al. 2020

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Downhill running (DR) is a whole-body exercise model that is used to investigate the physiological consequences of eccentric muscle actions and/or exercise-induced muscle damage (EIMD). In a sporting context, DR sections can be part of running disciplines (off-road and road running) and can accentuate EIMD, leading to a reduction in performance. The purpose of this narrative review is to: (1) better inform on the acute and delayed physiological effects of DR; (2) identify and discuss, using a comprehensive approach, the DR characteristics that affect the physiological responses to DR and their potential interactions; (3) provide the current state of evidence on preventive and in-situ strategies to better adapt to DR. Key findings of this review show that DR may have an impact on exercise performance by altering muscle structure and function due to EIMD. In the majority of studies, EIMD are assessed through isometric maximal voluntary contraction, blood creatine kinase and delayed onset muscle soreness, with DR characteristics (slope, exercise duration, and running speed) acting as the main influencing factors. In previous studies, the median (25th percentile, Q1; 75th percentile, Q3) slope, exercise duration, and running speed were − 12% (− 15%; − 10%), 40 min (30 min; 45 min) and 11.3 km h−1 (9.8 km h−1; 12.9 km h−1), respectively. Regardless of DR characteristics, people the least accustomed to DR generally experienced the most EIMD. There is growing evidence to suggest that preventive strategies that consist of prior exposure to DR are the most effective to better tolerate DR. The effectiveness of in-situ strategies such as lower limb compression garments and specific footwear remains to be confirmed. Our review finally highlights important discrepancies between studies in the assessment of EIMD, DR protocols and populations, which prevent drawing firm conclusions on factors that most influence the response to DR, and adaptive strategies to DR.

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