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Background: Running is a popular physical activity that benefits health; however, running surface characteristics may influence loading impact and injury risk. Machine learning algorithms could automatically identify running surface from wearable motion sensors to quantify running exposures, and perhaps loading and injury risk for a runner., Research Question: (1) How accurately can machine learning algorithms identify surface type from three-dimensional accelerometer sensors? (2) Does the sensor count (single or two-sensor setup) affect model accuracy?, Methods: Twenty-nine healthy adults (23.3 ± 3.6 years, 1.8 ± 0.1 m, and 63.6 ± 8.5 kg) participated in this study. Participants ran on three different surfaces (concrete, synthetic, woodchip) while fit with two three-dimensional accelerometers (lower-back and right tibia). Summary features (n = 208) were extracted from the accelerometer signals. Feature-based Gradient Boosting (GB) and signal-based deep learning Convolutional Neural Network (CNN) models were developed. Models were trained on 90% of the data and tested on the remaining 10%. The process was repeated five times, with data randomly shuffled between train-test splits, to quantify model performance variability., Results: All models and configurations achieved greater than 90% average accuracy. The highest performing models were the two-sensor GB and tibia-sensor CNN (average accuracy of 97.0 ± 0.7 and 96.1 ± 2.6%, respectively)., Significance: Machine learning algorithms trained on running data from a single- or dual-sensor accelerometer setup can accurately distinguish between surfaces types. Automatic identification of surfaces encountered during running activities could help runners and coaches better monitor training load, improve performance, and reduce injury rates., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Maximal oxygen uptake (VO2max) is often used to assess an individual's cardiorespiratory fitness. However, measuring this variable requires an athlete to perform a maximal exercise test which may be impractical, since this test requires trained staff and specialized equipment, and may be hard to incorporate regularly into training programs. The aim of this study is to develop a new model for predicting VO2max by exploiting its relationship to heart rate and accelerometer features extracted during submaximal running. To do so, we analyzed data collected from 31 recreational runners (15 men and 16 women) aged 19-26 years who performed a maximal incremental test on a treadmill. During this test, the subjects' heart rate and acceleration at three locations (the upper back, the lower back and the tibia) were continuously measured. We extracted a wide variety of features from the measurements of the warm-up and the first three stages of the test and employed a data-driven approach to select the most relevant ones. Furthermore, we evaluated the utility of combining different types of features. Empirically, we found that combining heart rate and accelerometer features resulted in the best model with a mean absolute error of 2.33 ml ⋅ kg-1 ⋅ min-1 and a mean absolute percentage error of 4.92%. The model includes four features: gender, body mass, the inverse of the average heart rate and the inverse of the variance of the total tibia acceleration during the warm-up stage of the treadmill test. Our model provides a practical tool for recreational runners in the same age range to estimate their VO2max from submaximal running on a treadmill. It requires two body-worn sensors: a heart rate monitor and an accelerometer positioned on the tibia., Competing Interests: The authors have declared that no competing interests exist.

Background: Outdoor falls in community-dwelling older adults are often triggered by uneven pedestrian walkways. It remains unclear how older adults adapt to uneven surfaces typically encountered in the outdoor built-environment and whether these adaptations are associated to age-related physiological changes., Research Question: The aims of this study were to (1) compare gait parameters over uneven and flat brick walkways, (2) evaluate the differences between older and young adults for these two surfaces, and (3) assess if physiological characteristics could predict adaptations in older adults., Methods: Balance, strength, reaction-time, full-body marker positions, and acceleration signals from a trunk-mounted inertial measurement unit were collected in seventeen older (71.5 ± 4.2 years) and eighteen young (27.0 ± 4.7 years) healthy adults to compute lower-limb joint kinematics, spatio-temporal parameters, dynamic stability, and accelerometry-derived metrics (symmetry, consistency, and smoothness)., Results: Both groups increased hip flexion at foot-strike, while decreasing ankle dorsiflexion, margin of stability, symmetry, and consistency on the uneven, compared to flat, surface. Older, compared to young, adults showed a larger increase in knee flexion at foot-strike and a larger decrease in smoothness on the uneven surface. Only young adults decreased hip abduction on the uneven surface. Strength, not balance nor reaction-time, was the main predictor of hip abduction in older adults on both surfaces., Significance: While older adults may be especially vulnerable, uneven surfaces negatively impact gait, irrespective of age, and could represent a risk to all pedestrians., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Maintaining stability under dynamic conditions is an inherent challenge to bipedal running. This challenge may impose an energetic cost (Ec) thus hampering endurance running performance, yet the underlying mechanisms are not clear. Wireless triaxial trunk accelerometry is a simple tool that could be used to unobtrusively evaluate these mechanisms. Here, we test a cost of instability hypothesis by examining the contribution of trunk accelerometry-based measures (triaxial root mean square, step and stride regularity, and sample entropy) to interindividual variance in Ec (J/m) during treadmill running. Accelerometry and indirect calorimetry data were collected concurrently from 30 recreational runners (16 men; 14 women) running at their highest steady-state running speed (80.65 ± 5.99% V̇o 2max ). After reducing dimensionality with factor analysis, the effect of dynamic stability features on Ec was evaluated using hierarchical multiple regression analysis. Three accelerometry-based measures could explain an additional 10.4% of interindividual variance in Ec after controlling for body mass, attributed to anteroposterior stride regularity (5.2%), anteroposterior root mean square ratio (3.2%), and mediolateral sample entropy (2.0%). Our results lend support to a cost of instability hypothesis, with trunk acceleration waveform signals that are 1) more consistent between strides anteroposterioly, 2) larger in amplitude variability anteroposterioly, and 3) more complex mediolaterally and are energetically advantageous to endurance running performance. This study shows that wearable trunk accelerometry is a useful tool for understanding the Ec of running and that running stability is important for economy in recreational runners. NEW & NOTEWORTHY This study evaluates and more directly lends support to a cost of instability hypothesis between runners. Moreover, this hypothesis was tested using a minimalist setup including a single triaxial trunk mounted accelerometer, with potential transferability to biomechanical and performance analyses in typical outdoor settings.

Medial tibial stress syndrome (MTSS) is a common overuse running injury with pathomechanics likely to be exaggerated by fatigue. Wearable accelerometry provides a novel alternative to assess biomechanical parameters continuously while running in more ecologically valid settings. The purpose of this study was to determine the influence of outdoor running fatigue and MTSS on both dynamic loading and dynamic stability derived from trunk and tibial accelerometery. Runners with (n=14) and without (n=16) history of MTSS performed an outdoor fatigue run of 3200m. Accelerometer-based measures averaged per lap included dynamic loading of the trunk and tibia (i.e. axial peak positive acceleration, signal power magnitude, and shock attenuation) as well as dynamic trunk stability (i.e. tri-axial root mean square ratio, step and stride regularity, and sample entropy). Regression coefficients from generalised estimating equations were used to evaluate group by fatigue interactions. No evidence could be found for dynamic loading being higher with fatigue in runners with MTSS history (all measures p>0.05). One significant group by running fatigue interaction effect was detected for dynamic stability. Specifically, in MTSS only, decreases mediolateral sample entropy i.e. loss of complexity was associated with running fatigue (p<0.01). The current results indicate that entire acceleration waveform signals reflecting mediolateral trunk control is related to MTSS history, a compensation that went undetected in the non-fatigued running state. We suggest that a practical outdoor running fatigue protocol that concurrently captures trunk accelerometry-based movement complexity warrants further prospective investigation as an in-situ screening tool for MTSS individuals., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Maintaining balance is vitally important in everyday life. Investigating the effects of individual foot muscle morphology on balance may provide insights into neuromuscular balance control mechanisms. This study aimed to examine the correlation between the morphology of foot muscles and balance performance during single-leg standing. Twenty-eight recreational runners were recruited in this study. An ultrasound device was used to measure the thickness and cross-sectional area of three intrinsic foot muscles (abductor hallucis, flexor digitorum brevis and quadratus plantae) and peroneus muscles. Participants were required to perform 30s of single-leg standing for three trials on a force plate, which was used to record the center of pressure (COP). The standard deviation of the amplitude and ellipse area of the COP were calculated. In addition, stabilogram diffusion analysis (SDA) was performed on COP data. Pearson correlation coefficients were computed to examine the correlation between foot muscle morphology and traditional COP parameters as well as with SDA parameters. Our results showed that larger abductor hallucis correlated to smaller COP sway, while larger peroneus muscles correlated to larger COP sway during single-leg standing. Larger abductor hallucis also benefited open-loop dynamic stability, as well as supported a more efficient transfer from open-loop to closed loop control mechanisms. These results suggest that the morphology of foot muscles plays an important role in balance performance, and that strengthening the intrinsic foot muscles may be an effective way to improve balance., (Copyright © 2017. Published by Elsevier B.V.)

Despite frequently declared benefits of using wireless accelerometers to assess running gait in real-world settings, available research is limited. The purpose of this study was to investigate outdoor surface effects on dynamic stability and dynamic loading during running using tri-axial trunk accelerometry. Twenty eight runners (11 highly-trained, 17 recreational) performed outdoor running on three outdoor training surfaces (concrete road, synthetic track and woodchip trail) at self-selected comfortable running speeds. Dynamic postural stability (tri-axial acceleration root mean square (RMS) ratio, step and stride regularity, sample entropy), dynamic loading (impact and breaking peak amplitudes and median frequencies), as well as spatio-temporal running gait measures (step frequency, stance time) were derived from trunk accelerations sampled at 1024Hz. Results from generalized estimating equations (GEE) analysis showed that compared to concrete road, woodchip trail had several significant effects on dynamic stability (higher AP ratio of acceleration RMS, lower ML inter-step and inter-stride regularity), on dynamic loading (downward shift in vertical and AP median frequency), and reduced step frequency (p<0.05). Surface effects were unaffected when both running level and running speed were added as potential confounders. Results suggest that woodchip trails disrupt aspects of dynamic stability and loading that are detectable using a single trunk accelerometer. These results provide further insight into how runners adapt their locomotor biomechanics on outdoor surfaces in situ., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Small wireless trunk accelerometers have become a popular approach to unobtrusively quantify human locomotion and provide insights into both gait rehabilitation and sports performance. However, limited evidence exists as to which trunk accelerometry measures are suitable for the purpose of detecting movement compensations while running, and specifically in response to fatigue. The aim of this study was therefore to detect deviations in the dynamic center of mass (CoM) motion due to running-induced fatigue using tri-axial trunk accelerometry. Twenty runners aged 18-25 years completed an indoor treadmill running protocol to volitional exhaustion at speeds equivalent to their 3.2 km time trial performance. The following dependent measures were extracted from tri-axial trunk accelerations of 20 running steps before and after the treadmill fatigue protocol: the tri-axial ratio of acceleration root mean square (RMS) to the resultant vector RMS, step and stride regularity (autocorrelation procedure), and sample entropy. Running-induced fatigue increased mediolateral and anteroposterior ratios of acceleration RMS (p < .05), decreased the anteroposterior step regularity (p < .05), and increased the anteroposterior sample entropy (p < .05) of trunk accelerometry patterns. Our findings indicate that treadmill running-induced fatigue might reveal itself in a greater contribution of variability in horizontal plane trunk accelerations, with anteroposterior trunk accelerations that are less regular from step-to-step and are less predictable. It appears that trunk accelerometry parameters can be used to detect deviations in dynamic CoM motion induced by treadmill running fatigue, yet it is unknown how robust or generalizable these parameters are to outdoor running environments.