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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.

Background Muscle co-contraction is an accepted clinical measure to quantify the effects of aging on neuromuscular control and movement efficiency. However, evidence of increased muscle co-contraction in old compared to young adults remains inconclusive. Research Question Are there differences in lower-limb agonist/antagonist muscle co-contractions in young and old adults, and males and females, during walking and stair use? Methods In a retrospective study, we analyzed data from 20 healthy young and 19 healthy old adults during walking, stair ascent, and stair descent at self-selected speeds, including marker trajectories, ground reaction force, and electromyography activity. We calculated muscle co-contraction at the knee (vastus lateralis vs. biceps femoris) and ankle (tibialis anterior vs. medial gastrocnemius) using the ratio of the common area under a muscle pairs’ filtered and normalized electromyography curves to the sum of the areas under each muscle in that pair. Results Old compared to young adults displayed 18%–22% greater knee muscle co-contractions during the entire cycle of stair use activities. We found greater (17%–29%) knee muscle co-contractions in old compared to young adults during the swing phase of walking and stair use. We found no difference in ankle muscle co-contractions between the two age groups during all three activities. We found no difference in muscle co-contraction between males and females at the knee and ankle joints for all three activities. Significance Based on our findings, we recommend clinical evaluation to quantify the effects of aging through muscle co-contraction to include the knee joint during dynamic activities like walking and stair use, and independent evaluation of the stance and swing phases.

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.