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34 results for “HRI”

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zenodo52/100

PE-HRI-temporal: A Multimodal Temporal Dataset in a robot mediated Collaborative Educational Setting

Please note that this dataset corresponds to the training data used in "Social robots as skilled ignorant peers for supporting learning "[7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).    This data set consists of multi-modal temporal team behaviors as well as learning outcomes collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink [1,2]. The data set can be useful for those looking to explore evolution of log actions, speech behavior, affective states, and gaze patterns for students to model constructs such as engagement, motivation, collaboration, etc. in educational settings.  In this data set, team level data is collected from 34 teams of two (68 children) where the children are aged between 9 and 12. There are two files:   PE-HRI_learning_and_performance.csv: This file consists of the team level performance and learning metrics which are defined below:  <ul> <li> last_error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task.  </li> <li> T_LG_absolute: It is a team-level learning outcome that we calculate by taking the average of the two individual absolute learning gains of the team members. The individual absolute gain is the difference between a participant’s post-test and pre-test score, divided by the maximum score that can be achieved (10), which grasps how much the participant learned of all the knowledge available. </li> <li> T_LG_relative: It is a team-level learning outcome that we calculate by taking the average of the two individual relative learning gains of the team members. The individual relative gain is the difference between a participant’s post-test and pre-test score, divided by the difference between the maximum score that can be achieved and the pre-test score. This grasps how much the participant learned of the knowledge that he/she didn’t possess before the activity.  </li> <li> T_LG_joint_abs: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity </li> </ul> PE-HRI_behavioral_timeseries_w_labels.csv: In this file, for each team, the interaction of around 20-25 minutes is organized in windows of 10 seconds; hence, we have a total of 5048 windows of 10 seconds each. We report team level log actions, speech behavior, affective states, and gaze patterns for each window. More specifically, within each window, 26 features are generated in two ways:  <ol> <li>non-incremental</li> <li>incremental</li> </ol> A non-incremental type would mean the value of a feature in that particular time window while an incremental type would mean the value of a feature until that particular time window. The incremental type is indicated by an "_inc" at the end of the feature name. Hence, in the end, within each window, we have 52 values:  <ul> <li> T_add/(_inc): The number of times a team added an edge on the map in that window/(until that window). </li> <li> T_remove/(_inc): The number of times a team removed an edge from the map in that window/(until that window). </li> <li> T_ratio_add_rem/(_inc): The ratio of addition of edges over deletion of edges by a team in that window/(until that window). </li> <li> T_action/(_inc): The total number of actions taken by a team (add, delete, submit, presses on the screen) in that window/(until that window). </li> <li> T_hist/(_inc): The number of times a team opened the sub-window with history of their previous solutions in that window/(until that window). </li> <li> T_help/(_inc): The number of times a team opened the instructions manual in that window/(until that window). Please note that the robot initially gives all the instructions before the game-play while a video is played for demonstration of the functionality of the game.  </li> <li> T1_T1_rem/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I add an edge, I then delete it in that window/(until that window). </li> <li> T1_T1_add/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I delete an edge, I add it back in that window/(until that window). </li> <li> T1_T2_rem/(_inc): The number of times the members of the team followed the pattern consecutively: I add an edge, you then delete it in that window/(until that window). </li> <li> T1_T2_add/(_inc): The number of times the members of the team followed the pattern consecutively: I delete an edge, you add it back in that window/(until that window). </li> <li> redundant_exist/(_inc): The number of times the team had redundant edges in their map in that window/(until that window). </li> <li> positive_valence/(_inc): The average value of positive valence for the team in that window/(until that window). </li> <li> negative_valence/(_inc): The average value of negative valence for the team in that window/(until that window). </li> <li> difference_in_valence/(_inc): The difference of the average value of positive and negative valence for the team in that window/(until that window). </li> <li> arousal/(_inc): The average value of arousal for the team in that window/(until that window). </li> <li> gaze_at_partner/(_inc): The average of the the two team member's gaze when looking at their partner in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_robot/(_inc): The average of the the two team member's gaze when looking at the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_other/(_inc): The average of the the two team member's gaze when looking in the direction opposite to the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_left/(_inc): The average of the the two team member's gaze when looking at the left side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_right/(_inc): The average of the the two team member's gaze when looking at the right side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> T_speech_activity/(_inc): The average of the two team member's speech activity in that window/(until that window). Each individual member's speech activity is calculated as a percentage of time that they are speaking in that window/(until that window).  </li> <li> T_silence/(_inc): The average of the two team member's silence in that window/(until that window). Each individual member's silence is calculated as a percentage of time in that window/(until that window).  </li> <li> T_short_pauses/(_inc): The average of the two team member's short pauses over their speech activity in that window/(until that window). Each individual member's short pause refers to a brief pause of 0.15 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_long_pauses/(_inc): The average of the two team members long pauses over their speech activity in that window/(until that window). Each individual member's long pause refers to a pause of 1.5 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_overlap/(_inc): The average percentage of time the speech of the team members overlaps in that window/(until that window). </li> <li> T_overlap_to_speech_ratio/(_inc): The ratio of the speech overlap over the speech activity of the team in that window/(until that window). </li> </ul> Apart from these 52 values, within each window, we also indicate:  <ul> <li>team: The team to which the window belongs to.</li> <li>time_in_secs: Time in seconds until that window.</li> <li>window: The window number.</li> <li>normalized_time: The time when this window occurred with respect to the total duration of the task for a particular team. </li> <li>cluster_labels: The cluster number associated with each time window in reference to the productive and non-productive clusters found in [3]</li> <li>PE_score: The Productive Engagement score in each window</li> </ul> Lastly, we briefly elaborate on how the features are operationalised. We extract log behaviors from the recorded rosbags while the behaviors related to both gaze and affective states are computed through the open source library OpenFace [6] that returns both facial actions units (AUs) as well as gaze angles. For voice activity detection (VAD), that classifies if a piece of audio is voiced or unvoiced, we made use of the python wrapper for the open source Google WebRTC VAD. The literature that inspired our log, audio and video features as well as the tools used to extract them are described in more detail in [3,4]. However, in those papers, we make use of only the aggregate version of this data [5]. Please note that this dataset corresponds to the training data used in [7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).

opencc-by-4.0Oct 2021View details →

zenodo48/100

Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework

This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play.  In this data set, team level multi-modal behavioral data is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below:  <ul> <li>condition: This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li>Error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li>Learning Gain: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li>Usefulness Score: The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li>PE Score: It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li>Right_Suggestions: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li>Right_Time: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li>Exploration: This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li>Reflection: This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li>Communication: This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li>LG_status: This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> This dataset corresponds to the publication "Social robots as skilled ignorant peers for supporting learning": <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a>

opencc-by-4.0Oct 2023View details →

zenodo44/100

Metrics and Methods used in IEEE/ACM HRI and IEEE RoMan Conference Proceedings from 2015 to 2021 Data

This work analyzes a total of 1464 papers through examination of seven years of HRI Conference proceedings(2015 through 2021) and six years of Ro-Man Conference proceedings (2015 through 2020) to present a holistic snapshot of the state of methods and metrics in HRI research. Workshop proposals, late-breaking results, keynote talk abstracts, and demo presentations were omitted from this study due to varying methodologies and shorter study timeframes.

opencc-by-4.0Jan 2022View details →

zenodo44/100

Version 2 of Dataset for paper "Drivers' Manoeuvre Prediction for Safe HRI"

Second version of the dataset published for  "Drivers’ Manoeuvre Prediction for Safe HRI". Details in the README file.

opencc-by-4.0Feb 2020View details →

zenodo40/100

The AFFECT-HRI data set: physiological data for affective computing in human-robot interaction with anthropomorphic service robots

We provide a comprehensive data set AFFECT-HRI 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. The detailed data descriptor has been published in Nature Scientific Data. For more details on the data set, please check the paper below. Please cite the following paper if the dataset is used in a publication: Heinisch, J.S., Kirchhoff, J., Busch, P. et al. Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set. Sci Data 11, 333 (2024). https://doi.org/10.1038/s41597-024-03128-z Acknowledgements 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.

opencc-by-4.0Feb 2024View details →

zenodo36/100

Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) Dataset - Anonymized

Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions. The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1. Exchanges file contains the participant ID, the number of the turn (conversation exchange by Robot-Participant response), the start and end of the turn, the anonymized transcript for the turn, and three annotator scores for the user enjoyment in the exchange.  Overall file contains the participant ID, self-reported user perception scores from the questionnaire ("I was satisfied with my conversation with the robot", "It was fun talking to the robot", "The conversation with the robot was interesting", "It felt strange talking to the robot") and three annotator scores for the user enjoyment in the overall interaction. The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). 20 participants had no prior interaction with a robot, and only one had previously talked with a robot. The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns.  Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations. Anonymization macros: [P_NAME]: Participant's name (may include surname). The robot always uses the first name even when the surname is given. [NAME_REMOVED]: A name of another person mentioned by the participant. [LOCATION_REMOVED]: Small town/village/area where the participant lives or lived. [MEDICAL_INFO_REMOVED]: Medical information shared by the participant. [AGE_REMOVED]: Participant's or other person's age. [INFORMATION_REMOVED]: Sensitive information shared by the participant. [MISTAKEN_NAME]: Speech recognition error resulted in the name being misunderstood.

opencc-by-4.0Jun 2024View details →

zenodo32/100

Dataset for Drivers' Manoeuvre Classification for Safe HRI

Dataset for the article " Drivers' Manoeuvre Classification for Safe HRI "

opencc-by-4.0Jul 2017View details →

geo24/100

HRI activates ATF4 to promote BCL11A transcription and fetal hemoglobin silencing

GEO Series GSE143963. Homo sapiens; Mus musculus. 23 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.

openGEO-OpenApr 2020View details →

geo24/100

PPM1D suppresses p53-dependent transactivation and cell death by inhibiting the HRI-eIF2alpha-ATF4 pathway [RNA-seq]

GEO Series GSE191148. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenNov 2022View details →

geo24/100

ROCK1 orchestrates B cell differentiation in response to PAMPs and heme by controlling the heme-regulated proteins Bach2 and HRI

GEO Series GSE237201. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenApr 2025View details →

geo24/100

OMA1, DELE1 and HRI relay mitochondrial stress to the mammalian integrated stress response

GEO Series GSE134986. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJul 2019View details →

geo24/100

Cytoplasmic proteotoxicity regulates HRI-dependent phosphorylation of eIF2a via the Hsp70-Bag3 module.

GEO Series GSE171440. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenApr 2021View details →

geo24/100

HRI activates ATF4 to promote BCL11A transcription and fetal hemoglobin silencing [ChIP-seq]

GEO Series GSE143961. Homo sapiens; Mus musculus. 15 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenApr 2020View details →

geo24/100

Iron and Heme Coordinate Erythropoiesis through HRI-Mediated Regulation of Protein Translation and Gene Expression

GEO Series GSE119365. Mus musculus. 21 samples. Type: Expression profiling by high throughput sequencing; Other.

openGEO-OpenSep 2018View details →

geo24/100

HRI depletion cooperates with pharmacologic inducers to elevate fetal hemoglobin and reduce sickle cell formation

GEO Series GSE150341. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenSep 2020View details →

geo20/100

Domain-focused CRISPR-screen identifies HRI as a fetal hemoglobin regulator in human erythroid cells

GEO Series GSE115687. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJul 2018View details →

geo20/100

HRI Protects Erythroid Precursors in iron deficiency and beta-Thalassemia by Maintaining GATA-1 and Fog-1 Expression

GEO Series GSE6808. Mus musculus; Homo sapiens. 24 samples. Type: Expression profiling by array.

openGEO-OpenJun 2009View details →

geo20/100

PPM1D suppresses p53-dependent transactivation and cell death by inhibiting the HRI-eIF2alpha-ATF4 pathway [ChIP-seq]

GEO Series GSE191149. Homo sapiens. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenSep 2022View details →

geo20/100

HRI activates ATF4 to promote BCL11A transcription and fetal hemoglobin silencing [Capture-C]

GEO Series GSE143962. Homo sapiens. 8 samples. Type: Other.

openGEO-OpenApr 2020View details →

geo20/100

PPM1D suppresses p53-dependent transactivation and cell death by inhibiting the HRI-eIF2alpha-ATF4 pathway.

GEO Series GSE191150. Homo sapiens. 34 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenSep 2022View details →

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