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14 results for “affective computing”
A dataset recorded during development of an affective brain-computer music interface: calibration session
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A dataset recorded during development of an affective brain-computer music interface: testing session
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A dataset recorded during development of an affective brain-computer music interface: training sessions
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Different adaptation error types in affective computing have different effects on user experience: a Wizard-of-Oz study
<p>The record consists of one Excel file that contains individual participant data for the study "Different adaptation error types in affective computing have different effects on user experience: a Wizard-of-Oz study". The study included 97 participants who were randomly divided into five groups corresponding to five adaptation behaviors (SingleSmall, SingleModerate, ImmediateLow, ImmediateHigh, IrreversibleHigh). Each participant took part in three 11-minute intervals. Difficulty changed every 60 seconds in each 11-minute interval, and there are thus 11 difficulty values per interval. At the end of each interval, participants self-reported their experience using the NASA Task Load Index (6 items) and Intrinsic Motivation Inventory (8 items). After the third interval, participants were asked to rate how much they liked the 3 intervals on a visual analog scale that was converted to 1-100 numerical scores.</p>
The AFFECT-HRI data set: physiological data for affective computing in human-robot interaction with anthropomorphic service robots
<p>We provide a comprehensive data set <strong>AFFECT-HRI </strong>containing physiological data labeled with human affect (i.e., mood and emotion) gathered during an empirical study consisting of a complex human-robot interaction (HRI). A realistic retail scenario served as an experimental environment. In prior research, we showed the necessity to combine the expertise of the research fields of psychology, computer science, and law in the design of a responsible human-centered HRI. Therefore, we implemented five conditions (neutral, transparency, liability, moral, and immoral) covering the perspectives from these three research fields and used two different anthropomorphic service robots. Our study followed a multi-method approach, resulting in a data set containing and combining objective physiological sensor data with subjective human-affect assessments. Additionally, the data set includes insights from 146 participants regarding affect, demographics, and socio-technical questionnaire ratings, as well as robot gestures and robot speech. Our study can be split into three scenes: a consultation regarding products, a request for sensitive personal information while opening a customer account, and a successful or failing handover when buying a mold remover. Thus, this data set offers for the first time the possibility to prove established or develop new emotion recognition methods and technological capabilities for HRI. Further, our data set provides the possibility to combine affective computing with research about robot behavior (gestures, speech, and handover), liability (questionnaire), transparency (questionnaire), and psychological aspects, allowing an encompassing, human-centered view of HRI.</p> <p>The detailed data descriptor has been published in Nature Scientific Data. For more details on the data set, please check the paper below.</p> <p><strong>Please cite the following paper if the dataset is used in a publication:</strong><br>Heinisch, J.S., Kirchhoff, J., Busch, P. <em>et al.</em> Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set. <em>Sci Data</em> <strong>11</strong>, 333 (2024). https://doi.org/10.1038/s41597-024-03128-z</p> <p><strong>Acknowledgements</strong><br>This research was conducted as part of RoboTrust, a project of the Centre Responsible Digitality, supported by the Hessian Minister for Digital Strategy and Innovation. The authors would like to thank all participants for their participation in the study. We particularly want to thank Ruth Stock-Homburg for her support and for making Elenoide available. Further, we want to thank Mona Kegel, Vignesh Prasad, and all the research assistants who supported the study. We also thank the leap in time lab for serving as study location. A special thanks goes to Amer Altizini, who supported us by helping to prepare the data for publication. We want to thank Niklas Jungermann for his valuable comments on the statistical evaluation.</p>
Home Based Computer Gaming Hand Exercise Regimen for People With Arthritis Affecting the Hands
ClinicalTrials.gov study NCT01635582. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Computer Guided Buccal Cortical Plate Separation for Removal of Calcified Benign Odontogenic Tumors Affecting Mandibular Angle Region
ClinicalTrials.gov study NCT05329974. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Application of LLM Care and Related Affective Computing Systems on Persons With Special Needs
ClinicalTrials.gov study NCT04390321. IPD Sharing: NO. Countries: 1. Publications: 4.
Application of LLM Care and Related Affective Computing Systems on People With Parkinson's Disease
ClinicalTrials.gov study NCT04426903. IPD Sharing: NO. Countries: 1. Publications: 3.
Evaluation of the Factors Affecting the Diagnostic Performance of Coronary Computed Tomography Angiogram (CTA) With Multi-slice Computed Tomography (MSCT)
ClinicalTrials.gov study NCT01164839. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Affect Regulation Based on Brain-computer Interface Towards Treatment for Depression
ClinicalTrials.gov study NCT03696667. IPD Sharing: NO. Countries: 1. Publications: 0.
Vertical Bone Gain and Neurosensory Affection in Computer Guided vs Conventional Sandwich Osteotomy
ClinicalTrials.gov study NCT05666414. IPD Sharing: NO. Countries: 1. Publications: 0.
Food Choices After Cognitive load: An Affective Computing Approach
<p>The dataset was gathered to examine the impact of cognitive load on eating behavior. It comprises three primary datasets:</p> <ol> <li> <p>Shimmer GSR+ device sensor data: This encompasses various sensor measurements such as galvanic skin response (electrodermal activity), photoplethysmography, accelerometer, gyroscope, magnetometer, temperature, and pressure data.</p> </li> <li> <p>Food consumption: This data records the quantities of fruits, vegetables, snacks, and liquids consumed during the experiment, measured in grams and milliliters.</p> </li> <li> <p>Questionnaires: This includes the NASA-TLX questionnaire to assess workload and the PANAS questionnaires to measure affective states.</p> </li> </ol>
How do table shape, group size, and gender affect on-task actions in computer education open-ended tasks_data
<p>The data set includes the coding scheme, results of intercoder reliability tests and results of statistical tests supporting the paper " How do table shape, group size, and gender affect on-task actions in computer education open-ended tasks "</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.