Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

HANDS: an RGB-D dataset of static hand-gestures for human-robot interaction.

ABSTRACT: The HANDS dataset has been created for human-robot interaction research, and it is composed of spatially and temporally aligned RGB and Depth frames. It contains 12 static single-hand gestures performed with both the right-hand and the left-hand, and 3 static two-hands gestures for a total of 29 unique classes. Five actors (two females and three males) have been acquired performing the gestures, each of them adopting a different background and light conditions. For each actor, 150 RGB frames and their corresponding 150 Depth frames per gesture have been collected, for a total of 2400 RGB frames and 2400 Depth frames per actor. Data has been collected using a Kinect v2 camera intrinsically calibrated to spatially align RGB data to Depth data. The temporal alignment has been performed offline using MATLAB, aligning frames with a maximum temporal distance of 66 ms. This dataset has been used in [1] and it is freely available at http://dx.doi.org/10.17632/ndrczc35bt.1.

SUBMITTER: Nuzzi C

PROVIDER: S-EPMC7873347 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

HANDS: an RGB-D dataset of static hand-gestures for human-robot interaction.

Nuzzi Cristina C Pasinetti Simone S Pagani Roberto R Coffetti Gabriele G Sansoni Giovanna G

Data in brief 20210130

The HANDS dataset has been created for human-robot interaction research, and it is composed of spatially and temporally aligned RGB and Depth frames. It contains 12 static single-hand gestures performed with both the right-hand and the left-hand, and 3 static two-hands gestures for a total of 29 unique classes. Five actors (two females and three males) have been acquired performing the gestures, each of them adopting a different background and light conditions. For each actor, 150 RGB frames and ...[more]

PMID: 33604423

Similar Datasets

SMA micro-hand implemented in small robot for generating gestures.

Project description:Research on robots that can be used for communication with humans has become popular in recent years. Communication robots should ideally be as small as an infant in order to reduce the user's feeling of threat. In addition, non-verbal communication (such as gestures) is also important in facilitating smooth interactions between humans and robots. There are currently a few communication robots that are small sized and can generate hand gestures. In this paper, we propose a small robot hand, which is optimized for gesture communication by using a shape memory alloy (SMA). The SMA employed is a Ti-Ni alloy, which is used as an actuator. The SMA shrinks when it transforms into the austenite phase at temperatures higher than the transformation temperature. When it is in the martensitic phase at a lower temperature, it is expanded by an external force. Each finger of the robot hand is driven by an individual SMA wire. The specifications of the small robot including the hand size, operation angles in each finger joint, response times and power consumption were determined according to the human finger and existing small communication robots. These required specifications have been fulfilled by carefully designing the geometry and heating/cooling power control. A questionnaire-based survey was also conducted with a robot hand. The five-finger hand was successfully shown to generate recognizable symbolic gestures.Supplementary informationThe online version supplementary material available at 10.1007/s11370-021-00364-9.

| S-EPMC8061717 | biostudies-literature

UWB-gestures, a public dataset of dynamic hand gestures acquired using impulse radar sensors.

Project description:In the past few decades, deep learning algorithms have become more prevalent for signal detection and classification. To design machine learning algorithms, however, an adequate dataset is required. Motivated by the existence of several open-source camera-based hand gesture datasets, this descriptor presents UWB-Gestures, the first public dataset of twelve dynamic hand gestures acquired with ultra-wideband (UWB) impulse radars. The dataset contains a total of 9,600 samples gathered from eight different human volunteers. UWB-Gestures eliminates the need to employ UWB radar hardware to train and test the algorithm. Additionally, the dataset can provide a competitive environment for the research community to compare the accuracy of different hand gesture recognition (HGR) algorithms, enabling the provision of reproducible research results in the field of HGR through UWB radars. Three radars were placed at three different locations to acquire the data, and the respective data were saved independently for flexibility.

| S-EPMC8041886 | biostudies-literature

Talking with Your (Artificial) Hands: Communicative Hand Gestures as an Implicit Measure of Embodiment.

Project description:When people talk, they move their hands to enhance meaning. Using accelerometry, we measured whether people spontaneously use their artificial limbs (prostheses) to gesture, and whether this behavior relates to everyday prosthesis use and perceived embodiment. Perhaps surprisingly, one- and two-handed participants did not differ in the number of gestures they produced in gesture-facilitating tasks. However, they did differ in their gesture profile. One-handers performed more, and bigger, gesture movements with their intact hand relative to their prosthesis. Importantly, one-handers who gestured more similarly to their two-handed counterparts also used their prosthesis more in everyday life. Although collectively one-handers only marginally agreed that their prosthesis feels like a body part, one-handers who reported they embody their prosthesis also showed greater prosthesis use for communication and daily function. Our findings provide the first empirical link between everyday prosthesis use habits and perceived embodiment and a novel means for implicitly indexing embodiment.

| S-EPMC7578755 | biostudies-literature

Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures.

Project description:This paper presents an electromyography (EMG) signal dataset for use in human-computer interaction studies. The dataset includes 4-channel surface EMG data from 40 participants with an equal gender distribution. The gestures in the data are rest or neutral state, extension of the wrist, flexion of the wrist, ulnar deviation of the wrist, radial deviation of the wrist, grip, abduction of all fingers, adduction of all fingers, supination, and pronation. Data were collected from 4 forearm muscles when simulating 10 unique hand gestures and recorded with the BIOPAC MP36 device using Ag/AgCl surface bipolar electrodes. Each participant's data contains five repetitive cycles of ten hand gestures. A demographic survey was applied to the participants before the signal recording process. This data can be utilized for recognition, classification, and prediction studies in order to develop EMG-based hand movement controller systems. The dataset can also be useful as a reference to create an artificial intelligence model (especially a deep learning model) to detect gesture-related EMG signals. Additionally, it is encouraged to use the proposed dataset for benchmarking current datasets in the literature or for validation of machine learning and deep learning models created with different datasets in accordance with the participant-independent validation strategy.

| S-EPMC8844426 | biostudies-literature

From hands to minds: Gestures promote understanding.

Project description:Gestures serve many roles in communication, learning and understanding both for those who view them and those who create them. Gestures are especially effective when they bear resemblance to the thought they represent, an advantage they have over words. Here, we examine the role of conceptually congruent gestures in deepening understanding of dynamic systems. Understanding the structure of dynamic systems is relatively easy, but understanding the actions of dynamic systems can be challenging. We found that seeing gestures representing actions enhanced understanding of the dynamics of a complex system as revealed in invented language, gestures and visual explanations. Gestures can map many meanings more directly than language, representing many concepts congruently. Designing and using gestures congruent with meaning can augment comprehension and learning.

| S-EPMC5256437 | biostudies-literature

Hand matters: Left-hand gestures enhance metaphor explanation.

Project description:Research suggests that speech-accompanying gestures influence cognitive processes, but it is not clear whether the gestural benefit is specific to the gesturing hand. Two experiments tested the "(right/left) hand-specificity" hypothesis for self-oriented functions of gestures: gestures with a particular hand enhance cognitive processes involving the hemisphere contralateral to the gesturing hand. Specifically, we tested whether left-hand gestures enhance metaphor explanation, which involves right-hemispheric processing. In Experiment 1, right-handers explained metaphorical phrases (e.g., "to spill the beans," beans represent pieces of information). Participants kept the one hand (right, left) still while they were allowed to spontaneously gesture (or not) with their other free hand (left, right). Metaphor explanations were better when participants chose to gesture when their left hand was free than when they did not. An analogous effect of gesturing was not found when their right hand was free. In Experiment 2, different right-handers performed the same metaphor explanation task but, unlike Experiment 1, they were encouraged to gesture with their left or right hand or to not gesture at all. Metaphor explanations were better when participants gestured with their left hand than when they did not gesture, but the right hand gesture condition did not significantly differ from the no-gesture condition. Furthermore, we measured participants' mouth asymmetry during additional verbal tasks to determine individual differences in the degree of right-hemispheric involvement in speech production. The left-over-right-side mouth dominance, indicating stronger right-hemispheric involvement, positively correlated with the left-over-right-hand gestural benefit on metaphor explanation. These converging findings supported the "hand-specificity" hypothesis. (PsycINFO Database Record

| S-EPMC5447392 | biostudies-literature

Teachers' gestures and students' learning: sometimes "hands off" is better.

Project description:During mathematics instruction, teachers often make links between different representations of mathematical information, and they sometimes use gestures to refer to the representations that they link. In this research, we investigated the role of such gestures in students' learning from lessons about links between linear equations and corresponding graphs. Eighty-two middle-school students completed a pretest, viewed a video lesson, and then completed a posttest comparable to the pretest. In all of the video lessons, the teacher explained the links between equations and graphs in speech. The lessons varied in whether the teacher referred to the equations in gesture and in whether she referred to the graphs in gesture, yielding four conditions: neither equations nor graphs, equations only, graphs only, and both equations and graphs. In all conditions, the gestures were redundant with speech, in the sense that the referents of the gestures were also mentioned in speech (e.g., pointing to "2" while saying "2"). Students showed substantial learning in all conditions. However, students learned less when the teacher referred to the equations in gesture than when she did not. This was not the case for gesture to graphs. These findings are discussed in terms of the processing implications of redundancy between gesture and speech, and the possibility of "trade-offs" in attention to the visual representations. The findings underscore the need for a more nuanced view of the role of teachers' gestures in students' comprehension and learning.

| S-EPMC5655596 | biostudies-literature

Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information.

Project description:Two sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side and the mapping method of human hand movements (primary hand) on the robotic hand (target hand). In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. Several approaches have been presented to deal with this problem, which is still unresolved in general. In this work, we exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We therefore report on two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint-Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The general mapping approach is presented, and the two realizations are tested. In order to report the results of an evaluation of the proposed mappings for multiple robotic hand kinematic structures (both industrial grippers and anthropomorphic hands, with a variable number of fingers), a simulative evaluation was performed.

| S-EPMC9258910 | biostudies-literature

Robot Gaze Behavior Affects Honesty in Human-Robot Interaction.

Project description:As the use of humanoid robots proliferates, an increasing amount of people may find themselves face-to-"face" with a robot in everyday life. Although there is a plethora of information available on facial social cues and how we interpret them in the field of human-human social interaction, we cannot assume that these findings flawlessly transfer to human-robot interaction. Therefore, more research on facial cues in human-robot interaction is required. This study investigated deception in human-robot interaction context, focusing on the effect that eye contact with a robot has on honesty toward this robot. In an iterative task, participants could assist a humanoid robot by providing it with correct information, or potentially secure a reward for themselves by providing it with incorrect information. Results show that participants are increasingly honest after the robot establishes eye contact with them, but only if this is in response to deceptive behavior. Behavior is not influenced by the establishment of eye contact if the participant is actively engaging in honest behavior. These findings support the notion that humanoid robots can be perceived as, and treated like, social agents, since the herein described effect mirrors one present in human-human social interaction.

| S-EPMC8144295 | biostudies-literature

A robot for overground physical human-robot interaction experiments.

Project description:Many anticipated physical human-robot interaction (pHRI) applications in the near future are overground tasks such as walking assistance. For investigating the biomechanics of human movement during pHRI, this work presents Ophrie, a novel interactive robot dedicated for physical interaction tasks with a human in overground settings. Unique design requirements for pHRI were considered in implementing the one-arm mobile robot, such as the low output impedance and the ability to apply small interaction forces. The robot can measure the human arm stiffness, an important physical quantity that can reveal human biomechanics during overground pHRI, while the human walks alongside the robot. This robot is anticipated to enable novel pHRI experiments and advance our understanding of intuitive and effective overground pHRI.

| S-EPMC9648723 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data