Bach, M. M. | bioRxiv Science

bioRxiv2023

Mental fatigue has only marginal effects on static balance control in healthy young adults

We examined the influence of mental fatigue on static balance control in healthy young adults to gain greater clarity about this issue than provided in previous research. Based on the prevailing assumption in pertinent literature, we hypothesized that mental fatigue leads to a reduced cognitive regulation of quiet upright standing, as reflected in center of pressure (COP) excursions. More specifically, we hypothesized that the influence of mental fatigue on balance control depends on the attentional effort required by the balance tasks being performed. To test these hypotheses, 44 young adults (24 women and 20 men) were quasi-randomly assigned to either an experimental group that was mentally fatigued (using the TloadDback-task with individualized settings) or a control group (who watched a documentary). Before and after the intervention the participants performed six balance tasks that differed in (attentional) control requirements, while their COP was being recorded. From these time-series sway variability, mean speed, and sample entropy were calculated and analyzed statistically. Additionally, mental fatigue was assessed using VAS scales. Statistical analyses confirmed that the balance tasks differed in control characteristics and that mental fatigue was elevated in the experimental group, but not in the control group. Nevertheless, no significant main effects of mental fatigue were found on any of the COP measures of interest, except for some non-robust and difficult to interpret interaction effects involving the factor group. These results suggest that, in young adults, postural control in static balance tasks is largely automatic and unaffected by mental fatigue.

neuroscience↗

bioRxiv2022

Predicting vertical ground reaction forces from 3D accelerometery using reservoir computers leads to accurate gait event detection

Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the golden standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.

neuroscience↗