1. Reliability improvements for in-wheel motorGašper Petelin, Rok Hribar, Stane Ciglarič, Jernej Herman, Anton Biasizzo, Peter Korošec, Gregor Papa, 2024, independent scientific component part or a chapter in a monograph Abstract: Setting up a reliable electric propulsion system in the automotive sector requires an intelligent condition monitoring device capable of reliably assessing the state and the health of the electric motor. To allow for a massive integration of such monitoring devices, they must be inexpensive and small. These requirements limit their accuracy. However, we show in this chapter that these limitations can be significantly reduced by appropriate processing of the sensor data. We have used machine learning models (random forest and XGBoost) to transform very noisy motor winding insulation resistance measurements made by a low-cost device into a much more reliable value that can compete with measurements made by a high-priced state-of-the-art measurement system. The proposed method is an important building block for a future smart condition monitoring system and enables a cost-effective and accurate assessment of the condition of electric motor health in connection with the condition of their winding insulation.
Keywords: machine learning models, low-cost device, electric motor
Published in DiRROS: 23.07.2024; Views: 927; Downloads: 397
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2. Optimal sensor set for decoding motor imagery from EEGArnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uroš Marušič, Sidney Grosprêtre, Ann Nowé, Romain Meeusen, Kevin De Pauw, 2023, original scientific article Abstract: Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.
Keywords: brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning
Published in DiRROS: 03.04.2023; Views: 1748; Downloads: 833
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3. Additional exergames to regular tennis training improves cognitive-motor functions of children but may temporarily affect tennis technique : a single-blind randomized controlled trialLuka Šlosar, Eling D. de Bruin, Eduardo Bodnariuc Fontes, Matej Plevnik, Rado Pišot, Boštjan Šimunič, Uroš Marušič, 2021, original scientific article Abstract: This study evaluated the effects of an exergame program (TennisVirtua-4, Playstation Kinect) combined with traditional tennis training on autonomic regulation, tennis technique, gross motor skills, clinical reaction time, and cognitive inhibitory control in children. Sixty-three children were randomized into four groups (1st % two exergame and two regular trainings sessions/week, 2nd % one exergame and one regular training sessions/week, 3rd % two regular trainings sessions/week, and 4th % one regular training session/week) and compared at baseline, 6-month immediately post intervention and at 1-year follow-up post intervention. At 6-month post intervention the combined exergame and regular training sessions revealed: higher breathing frequency, heart rate (all ps % 0.001) and lower skin conductance levels (p = 0.001) during exergaming; additional benefits in the point of contact and kinetic chain elements of the tennis forehand and backhand technique (all ps % 0.001); negative impact on the shot preparation and the follow-through elements (all ps % 0.017); higher ball skills (as part of the gross motor skills) (p < 0.001); higher percentages of clinical reaction time improvement (1st %9.7% vs 3rd group %7.4% and 2nd %6.6% vs 4th group %4.4%, all ps % 0.003) and cognitive inhibitory control improvement in both congruent (1st %20.5% vs 3rd group %18.4% and 2nd %11.5% vs 4th group %9.6%, all ps % 0.05) and incongruent (1st group %19.1% vs 3rd group %12.5% and 2nd group %11.4% vs 4th group %6.5%, all ps % 0.001) trials. The 1-year follow-up test showed no differences in the tennis technique, clinical reaction time and cognitive inhibitory control improvement between groups with the same number of trainings per week. The findings support exergaming as an additional training tool, aimed to improve important cognitive-motor tennis skills by adding dynamics to the standardized training process. Caution should be placed to planning this training, e.g., in a mesocycle, since exergaming might decrease the improvement of specific tennis technique parts of the trainees. (ClinicalTrials.gov; ID: NCT03946436).
Keywords: tennis, training, performance, children, motor learning, cognitive learning, teaching, strategies
Published in DiRROS: 17.03.2021; Views: 2180; Downloads: 1892
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