This study examined the effects of a d-β-hydroxybutyrate (βHB) containing beverage on cognitive and performance measures during a bout of repeated Wingates. Fifteen healthy, college-aged males (mean ± SD; age: 23.1 ± 2.4 years, height: 165.4 ± 2.0 cm, mass: 81.4 ± 9.2 kg) volunteered for the present study. Trial 1 consisted of baseline measures and familiarization for the protocol. During trials 2 and 3, subjects reported to the laboratory, after a 10-h fast, and ingested 11.38 g of βHB or a placebo (PLA) beverage 30 min before exercise. Participants then completed a cognitive challenge (CC), consisting of a 5-min FitLight response task while cycling. At the cessation of the test, participants then completed four 15-s repeated Wingates with 4 min of rest between, followed by another 5-min CC response task. Blood ketones, glucose, and lactate were measured pre-CC and post-Wingates. βHB levels were significantly higher compared with PLA (0.53 vs. 0.21 mmol/L), respectively. A significant order effect was observed across trials 2 and 3 for total FitLight misses and hits, regardless of treatment. Further, there were no significant differences among Wingate power output between treatments, although fatigue index was higher in the βHB group compared with PLA (32.3 vs. 29.4 W/s), respectively. In conclusion, βHB did not improve high-intensity cycling or cognitive performance measures; however, these findings might be partially explained by the absolute dosing protocol used for βHB in the present study as opposed to a relative (g/kg) dosing protocol used in previous research.
The linearity of soft robotic sensors (SRS) was recently validated for movement angle assessment using a rigid body structure that accurately depicted critical movements of the foot–ankle complex. The purpose of this study was to continue the validation of SRS for joint angle movement capture on 10 participants (five male and five female) performing ankle movements in a non-weight bearing, high-seated, sitting position. The four basic ankle movements—plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)—were assessed individually in order to select good placement and orientation configurations (POCs) for four SRS positioned to capture each movement type. PF, INV, and EVR each had three POCs identified based on bony landmarks of the foot and ankle while the DF location was only tested for one POC. Each participant wore a specialized compression sock where the SRS could be consistently tested from all POCs for each participant. The movement data collected from each sensor was then compared against 3D motion capture data. R-squared and root-mean-squared error averages were used to assess relative and absolute measures of fit to motion capture output. Participant robustness, opposing movements, and gender were also used to identify good SRS POC placement for foot–ankle movement capture.
Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data “from the ground up” and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers’ hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements.
The purpose of this study was to use 3D motion capture and stretchable soft robotic sensors (SRS) to collect foot-ankle movement on participants performing walking gait cycles on flat and sloped surfaces. The primary aim was to assess differences between 3D motion capture and a new SRS-based wearable solution. Given the complex nature of using a linear solution to accurately quantify the movement of triaxial joints during a dynamic gait movement, 20 participants performing multiple walking trials were measured. The participant gait data was then upscaled (for the SRS), time-aligned (based on right heel strikes), and smoothed using filtering methods. A multivariate linear model was developed to assess goodness-of-fit based on mean absolute error (MAE; 1.54), root mean square error (RMSE; 1.96), and absolute R 2 (R 2 ; 0.854). Two and three SRS combinations were evaluated to determine if similar fit scores could be achieved using fewer sensors. Inversion (based on MAE and RMSE) and plantar flexion (based on R 2 ) sensor removal provided second-best fit scores. Given that the scores indicate a high level of fit, with further development, an SRS-based wearable solution has the potential to measure motion during gait-based tasks with the accuracy of a 3D motion capture system.
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