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Modular Dashboard for Flexible in Car HMI Testing
Published in Gavriel Salvendy, Advances in Human Aspects of Road and Rail Transportation, 2012
Frank Sulzmann, Vivien Melcher, Frederik Diederichs, Rafael Sayar
Beside the light tubes also light settings of the two implemented displays are adjustable. The modular dashboard comes with a large screen behind the steering wheel to simulate all kinds of head unit designs, including minimalized screens for night driving or environmental adaptive brightness. In the center stack a large monitor allows for simulating different center stack designs and various positions and sizes for simulated center screens.
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists
Published in European Journal of Sport Science, 2023
Manuel Mateo-March, Manuel Moya-Ramón, Alejandro Javaloyes, Cristóbal Sánchez-Muñoz, Vicente J. Clemente-Suárez
As for HRV, R-R intervals (RRi) were continuously recorded (Garmin Edge 1000) for HR and HRV analyses with a transmitter belt (Polar Bluetooth H10, Oy, Finland). The transmitter belt was connected to the Garmin head unit via Bluetooth. “FIT” files for each subject were imported into Kubios 3.3.2 (Biosignal Analysis and Medical Imaging Group, Department of Physics, University of Kuopio, Kuopio, Finland) (Tarvainen, Niskanen, Lipponen, Ranta-Aho, & Karjalainen, 2014). Kubios preprocessing settings were set at the default values including the RR detrending method, which was kept at “Smoothen priors” (Lambda = 500) (Niskanen, Tarvainen, Ranta-Aho, & Karjalainen, 2004). The analysis and processing of the data were performed according to the standard criteria (Heart rate variability, 1996; Peltola, 2012). The files were corrected for ectopic beats and artifacts before the analysis using a medium level of artifact correction (Alcantara et al., 2020). The interpolation of the series was performed by a piecewise cubic spline interpolation method provided by Kubios’ software. A full description of the algorithm can be found elsewhere (Lipponen & Tarvainen, 2019). This is the recommended technique by the literature for artifact and ectopic beat corrections when examining R-R intervals (Peltola, 2012; Perrotta, Jeklin, Hives, Meanwell, & Warburton, 2017). The literature also suggests holding the 80% of normal R-R intervals for further analysis, and for the present study, only the signals with less than 20% of corrected beats were included in the analyses. For DFA-α1 estimation, the root mean square fluctuation of the integrated and detrended data was measured in 2-minute windows (Chen, Ivanov, Hu, & Stanley, 2002). The data were then plotted against the size as reported previously (Rogers et al., 2021a). DFA-α1 window width was set to 4 ≤ N ≤ 16 beats. The specific methodology for thresholds determination using DFA-α1 is detailed elsewhere (Gronwald et al., 2021; Rogers et al., 2021a; Rogers et al., 2021b). Briefly, for the detection of the “first HRV threshold”, a DFA-α1 value of 0.75 (“DFA-α1–0.75”) was chosen based on this being the midpoint between a fractal behaviour of the HR time series of 1.0 (observed in low-intensity exercise) and an uncorrelated value of 0.5 with a random behaviour of the HR time series (corresponding to high-intensity exercise) (Platisa & Gal, 2008). For the detection of the “second HRV threshold”, a DFA-α1 value of 0.5 (“DFA-α1–0.5”) was chosen (Rogers et al., 2021b). Thereafter, these breakpoints (DFA-α1 values of 0.5 and 0.75) were matched to the HR and PO value obtained during the GXT.