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27 results for “Handover”
Implications of Handover Events in commercial 5G Non-Standalone Deployments in Rome
<p>Passive and active network measurements used for analysing the implications of Handover (HO) events inn commercial 5G Non-Standalone (NSA) deployments in Rome.</p> <p> </p> <p> </p>
Human-to-robot Handovers of Cups with Water
<p><em>If you use this dataset in your work, please cite the following publication (accepted to IEEE iROS 2023):</em></p> <p><em>Lastrico, Linda, Nuno Ferreira Duarte, Alessandro Carfí, Francesco Rea, Alessandra Sciutti, Fulvio Mastrogiovanni, and José Santos-Victor. "Expressing and inferring action carefulness in human-to-robot handovers." In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9824-9831. IEEE, 2023.</em></p> <p>Setup Description:</p> <p>The experiment is presented as a collaborative task, where the human should help the robot clean the table by handing over the cups, from the rightmost cup to the leftmost, one at a time. The robot receives the cup; in case the cup contains water, it pours the content into the orange bucket, and finally, it places the empty cup in the blue drawer.</p> <p>Participants stand in front of a table with four identical plastic cups placed in a row, equidistant from each other. These cups differ in content, being two empty and two filled with water almost to the brim, constituting two types of objects to be handover: empty or full. Participants faced a Kinova Gen3 robot fixed to a table with two distinct recipients at the robot side. On the left side of the robot, there is an orange bucket meant to contain water, while the blue drawer on the right stores the empty (or emptied) cups. We adopted a within-subject study design where participants are exposed, in a randomized order, to two conditions associated with the controller used by the robot to complete the task: a neutral motion (NEU) and an expressive motion (GAN). The neutral is a simple PID controller and the expressive is Generative Adversarial Network (GAN) which generates robot trajectories that are human-inspired from a previous dataset of humans handing over cups with water and without.</p> <p>Our study involved 15 right-handed participants (8 females, 7 males, average of 26.6 (+-6.2) years old) who provided written informed consent. They were all naive regarding the purpose of the experiments and not directly involved in our research. The self-reported level of knowledge in robotics was: 40.0% professional or advanced, 33.3% average, and 26.7% little or none.<br>360 actions (15 participants x 12 handovers x 2 conditions) were recorded and performed successfully without dropping the cup or spilling the content.</p> <p>Data Description:</p> <p>All the data is synchronize using ROS timestamps.</p> <p>- motion-tracking: Motion Capture data for head, shoulder, and wrist from OptiTrack at 120 Hz + IMU wrist data at 400 Hz. Inside each participant P## folder you will find two other folders P##_neu and P##_gan related to the two interaction conditions where the kinova motion controller changed during the cup pouring and cup placing. Inside each subfolder you will find the following motion tracking data: head.csv, shoulder.csv, wrist.csv, robot.csv (from OptiTrack markers, the robot.csv is the robot's base), imu.csv (from IMU in the wrist), pupil.csv (Pupil ROS node), key.csv (manual labels). All these files have ROS_timestamps that can be used to find the matching frames for each of the sensors. The key.csv are manually picked time flags we marked to define specific moments in the experiment (you can the meaning in the additional notes below).</p> <p>- eye-tracking_#: Pupil-Labs head-mounted eye-trackers at 120Hz for pupil infra-red cameras, and 30 Hz for forward RGB camera. All 16 participants (P##) are present for both robot motion controllers (neutral NEU, and GAN).</p> <p>- go_pro_#: GoPro 1080p video of the size view of the Human-to-robot handovers experiments at 60 Hz. Note that there were 16 participants in this experiment but 3 participants did not give permission to make their image public so we removed the following participants videos: P01, P15, P16.</p> <p> </p>
Human Grasp Dataset for Human Robot Handovers
<p>The dataset consists of 278.400 RGB images of size (299, 299).</p> <p>The images are sorted into the folders:</p> <ul> <li>Angles <ul> <li>30°, 45°, and 60°</li> </ul> </li> <li>Lights <ul> <li>From_Behind, From_Front, and Full_Lighting</li> </ul> </li> <li>Objects <ul> <li>Duplo_Block, Highlighter, Plastic_Pear, Table_Tennis_Racket, and Wood_Block</li> </ul> </li> <li>Persons <ul> <li>Person_1 to Person_10</li> </ul> </li> <li>Other <ul> <li>Default_Configuration, Clutter, and Other_Grasps_And_Interactions</li> </ul> </li> </ul> <p>Each folder contains 11.600 images that have a label in their file name. l=1 for grasp and l=0 for not grasp. </p>
Multi-sensor Dataset of Multiple Sequential Human-to-Human Object Handovers in Shelving and Un-shelving Tasks
<p>We provide a multi-sensor dataset containing RGB-D and motion tracking data from sequential human-to-human object handovers. We recorded 12 pairs of participants executing shelving and un-shelving tasks involving 30 object handovers, resulting in 1440 handovers. Each recording consists of the position and orientation trajectories of 13 upper-body bones of the giver and the receiver and position trajectories of the 27 markers placed on their upper bodies, all recorded at 120Hz. The recordings also include two RGB-D data streams at 30Hz. We also provide four anthropometric measurements of the participants: height, waistline height, arm span, and weight. The dataset is valuable for investigating the body movements, grasps, and coordination strategies utilized by humans while performing tasks such as shelving which involve multiple sequential object handovers. Additionally, the dataset can be used to teach robots perform tasks involving object handovers with people, as well as self-handovers to correct grasps.<br><br>The details about the dataset collection procedure are available in the article:<br>Kshirsagar, A., Fortuna, R., Xie, Z., & Hoffman, G. (2025). Descriptor: Multi-sensor Dataset of Multiple Sequential Human-to-Human Object Handovers in Shelving and Un-shelving Tasks (MH2HO). IEEE Data Descriptions. (http://dx.doi.org/10.1109/IEEEDATA.2025.3580058)</p>
IntelliMan_WP4_Adaptive Shared Autonomy_T4.2_Advanced human-robot interaction modalities_human robot handover_v0
<p><span>The dataset contains data related to the experiments presented in the publication:</span></p> <p><em><span>M. Costanzo, C. Natale and M. Selvaggio, "Visual and Haptic Cues for Human-Robot Handover*," 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Korea, Republic of, 2023, pp. 2677-2682, doi: 10.1109/RO-MAN57019.2023.10309480.</span></em></p>
Towards safe human-to-robot handovers of unknown containers: pre-trained models and 3D hand keypoints annotations
<p>This repository contains additional data to be used with the implementation of the real-to-simulation framework of the paper <em>Towards safe human-to-robot handovers of unknown containers</em>. The data include pre-trained models and annotations of the 3D hand poses for selected recordings from the public training and testing sets of <a href="http://corsmal.eecs.qmul.ac.uk/containers_manip.html">CORSMAL Container Manipulation (CCM) dataset</a>. The pre-trained models are used for classifying the filling type and filling level of a container. 3D hand poses are annotated as 21 keypoints based on the <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a> format.</p>
Impact of an Anesthesia Care Handover-Checklist on Adverse Perioperative Outcome
ClinicalTrials.gov study NCT04582513. IPD Sharing: NO. Countries: 1. Publications: 8.
Anesthesia-handover Checklist and Perioperative Outcomes in Elderly
ClinicalTrials.gov study NCT04377633. IPD Sharing: NO. Countries: 1. Publications: 27.
Effects of Nursing Patient Handover
ClinicalTrials.gov study NCT06468631. IPD Sharing: NO. Countries: 1. Publications: 1.
A Blended Learning to Enhance Communication Skill Competence and Self-Efficacy of Nursing Students in Clinical Handovers
ClinicalTrials.gov study NCT05150067. IPD Sharing: NO. Countries: 1. Publications: 1.
Impact of Handover of Anesthesia Care on Adverse Postoperative Outcomes
ClinicalTrials.gov study NCT04016454. IPD Sharing: NO. Countries: 1. Publications: 1.
Quality Improvement in Handover of General Internal Medicine In-patients
ClinicalTrials.gov study NCT01796756. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Bedside Nursing Handovers in the Surgical Context
ClinicalTrials.gov study NCT03465839. IPD Sharing: NO. Countries: 1. Publications: 4.
Data from: Investigating clinical handover and healthcare communication for outpatients with chronic disease in India: a mixed-methods study
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Data from: A method for detecting characteristic patterns in social interactions with an application to handover interactions
Social interactions are a defining behavioural trait of social animals. Discovering characteristic patterns in the display of such behaviour is one of the fundamental endeavours in behavioural biology and psychology, as this promises to facilitate the general understanding, classification, prediction and even automation of social interactions. We present a novel approach to study characteristic patterns, including both sequential and synchronous actions in social interactions. The key concept in our analysis is to represent social interactions as sequences of behavioural states and to focus on changes in behavioural states shown by individuals rather than on the duration for which they are displayed. We extend techniques from data mining and bioinformatics to detect frequent patterns in these sequences and to assess how these patterns vary across individuals or changes in interaction tasks. To illustrate our approach and to demonstrate its potential, we apply it to novel data on a simple physical interaction, where one person hands a cup to another person. Our findings advance the understanding of handover interactions, a benchmark scenario for social interactions. More generally, we suggest that our approach permits a general perspective for studying social interactions.
Data from: Handover training for medical students – a controlled educational trial of a pilot curriculum
Background: Handovers are a critical point of patient care and a significant source of adverse events. The WHO patient safety curriculum provides some structure for handover teaching; in Europe, there is no standardized curriculum for undergraduate handover training. To address this, the Aachen Interdisciplinary Training Centre for Medical Education, developed and established a pilot curriculum for handover training in the context of the EU-funded PATIENT-project Objective: To develop and implement a handover curriculum for medical students and to assess its effect on students' awareness, confidence and knowledge regarding patient safety and handover in multiple settings. Methods: The pilot handover training curriculum was designed following Kern´s principles of curriculum development and was integrated into a curricular course led by departments for anesthesiology and intensive care (AI) at the University Hospital. A controlled educational research study was conducted with 4th year medical students (n=147) who either received the standard existing curriculum (no teaching of handover, n=78) or the pilot handover training (n=69). Paper-based questionnaires regarding attitude, confidence and knowledge towards handover and patient safety were used for pre- and post-assessment. The pilot curriculum consisted of 3 units (1-2 hours each) integrated into a 4-week course of AI. Multiple types of handover (end-of-shift, operating room/post anesthesia recovery unit/ICU, telephone, discharge) were addressed. Results: Students showed a significant increase in knowledge (p<0.01) and self-confidence for the use of standardized handover tools (p<0.01) and accurate handover performance (p<0.01) among the pilot group. Discussion/Conclusion: We developed and implemented a pilot curriculum for undergraduate handover training. Students displayed a significant increase in knowledge and self-confidence for the use of standardized handover tools and accurate handover performance. An evaluation of the curriculum by other faculties is needed. Further studies should evaluate whether the observed effect of a specific handover strategy is associated with a patient benefit.
Dataset of Bimanual Human-to-Human Object Handovers
<p>We present a multi-sensor dataset of bimanual human-to-human object handovers. The dataset consists of 240 recordings obtained from 12 pairs of participants performing bimanual object handovers with 10 objects, and 120 recordings obtained from the same 12 pairs of participants performing unimanual handovers with 5 of those objects. Each recording includes the giver and receiver's 13 upper-body bone position and orientation trajectories, position trajectories for the 27 markers placed on their upper bodies, object position and orientation trajectories, and two RGB-D data streams. The motion trajectories are recorded at 120Hz and the RGB-D streams are recorded at 30Hz. The recordings are annotated with the three handover phases: reach, transfer, and retreat. The dataset also includes four anthropometric measurements of the participants: height, waistline height, arm span, and weight. Our dataset could help investigations of the bimanual reaching motions and grasps utilized by humans while performing handovers. Also, it can be used to train robots to perform bimanual object handovers with humans.</p> <p>More details about the dataset can be found in this paper: https://doi.org/10.1016/j.dib.2023.109277</p>
Quality of Handover to a Post Anaesthesia Care Unit in a Children's Hospital
ClinicalTrials.gov study NCT02114866. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Intraoperative Handover Checklist of Anesthesia Care Improves Outcomes Among Patients Undergoing Major Surgery
ClinicalTrials.gov study NCT03597932. IPD Sharing: UNDECIDED. Countries: 0. Publications: 11.
Data from: A method for detecting characteristic patterns in social interactions with an application to handover interactions
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International Brain Laboratory public data
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