3,621 to 3,630 of 3,690 Results
ZIP Archive - 380.3 MB -
MD5: 7d44160b07673e160fb83724ac650720
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Garkov, Dimitar; Müller, Christoph; Klein, Karsten; Ertl, Thomas; Schreiber, Falk; Task-Force A, 2022, "Guidelines on Replication and Research Data Management", https://doi.org/10.18419/DARUS-2843, DaRUS, V1
This document summarises guidelines for reproducibility in SFB/Transregio 161 by Task Force A (TF-A). It builds upon the data management plan (version 1.0, 2020, https://doi.org/10.18419/darus-632) and focusses on three main points: Provide clear definitions of reproducibility and replicability in the context of TF-A. Coordinate efforts on research... |
May 20, 2022 -
Guidelines on Replication and Research Data Management
Adobe PDF - 286.0 KB -
MD5: ea953850363d17e9a9531f9a0d3a1517
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Chiossi, Francesco; Villa, Steeven; Hauser, Melanie; Welsch, Robin; Chuang, Lewis, 2022, "Design of On-body Tactile Displays to Enhance Situation Awareness in Automated Vehicles", https://doi.org/10.18419/DARUS-2824, DaRUS, V1, UNF:6:YHhX4VFGLE0o6fVxJAlldw== [fileUNF]
Fatalities with semi-automated vehicles typically occur when users are engaged in non-driving related tasks (NDRTs) that compromise their situational awareness (SA). This work developed a tactile display for on-body notification to support situational awareness, thus enabling users to recognize vehicle automation failures and intervene if necessary... |
May 2, 2022 -
Design of On-body Tactile Displays to Enhance Situation Awareness in Automated Vehicles
Tabular Data - 1.3 KB - 9 Variables, 21 Observations - UNF:6:YHhX4VFGLE0o6fVxJAlldw==
This datased contains the following information per column: Participant ID, assigned condition (SA-L1 = 1, SA-L3=2), distance, Max Braking intensitiy, TTC, Attention Interference, WM Interference, NASA scores, SART score . |
May 2, 2022 -
Design of On-body Tactile Displays to Enhance Situation Awareness in Automated Vehicles
Adobe PDF - 71.3 KB -
MD5: fcafd668f54f6fc66b2a3471e6dda0c5
Qualitative Questions asked within the Semi-Structured Interview |
Apr 27, 2022 - SFB-TRR 161 C06 "User-Adaptive Mixed Reality"
Chiossi, Francesco; Welsch, Robin; Villa, Steeven; Chuang, Lewis; Mayer, Sven, 2022, "Virtual Reality Adaptation using Electrodermal Activity to Support User Experience", https://doi.org/10.18419/DARUS-2820, DaRUS, V1
We report an experiment (N=18) where participants where engaged in a dual task setting in a Social VR (Virtual Reality) scenario. We present a physiologically-adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social... |
7Z Archive - 223.7 MB -
MD5: 43fc85772d525f7cb3ea8a9ee7c00539
Dataset containing data for each participant. Specifically included are :
1. Slope of the EDA tonic in -adaptation.csv ;
2. Raw Electrocardiogram (ECG) data in -ECG.csv;
3. Raw Electroencephalogram (EEG) data in -EEG.csv acquired from Value1 : Fz, P3, P, P4, PO7, Oz and PO8 electrodes.
4. Feedback presented during the N-Back task in -feedback.c... |
Python Source Code - 6.6 KB -
MD5: 0ba2a0a17cec3461196dea43efd8e1d5
Code to preprocess the raw dataset (performance, subjective and Electrodermal activity data) |
Jupyter Notebook - 790.8 KB -
MD5: 89a5f48990f612b286f13e8e9c205074
Code for Statistical analysis of preprocessed data (Jupyter Notebook file) |
