DOI: 10.57760/sciencedb.01872
title: HA4M - Human Action Multi-Modal Monitoring in Manufacturing
title: HA4M - Human Action Multi-Modal Monitoring in Manufacturing

description: OverviewThe HA4M dataset is a collection of multi-modal data relative to actions performed by different subjects in an assembly scenario for manufacturing. It has been collected to provide a good test-bed for developing, validating and testing techniques and methodologies for the recognition of assembly actions. To the best of the authors' knowledge, few vision-based datasets exist in the context of object assembly.The HA4M dataset provides a considerable variety of multi-modal data compared to existing datasets. Six types of simultaneous data are supplied: RGB frames, Depth maps, IR frames, RGB-Depth-Aligned frames, Point Clouds and Skeleton data.These data allow the scientific community to make consistent comparisons among processing approaches or machine learning approaches by using one or more data modalities. Researchers in computer vision, pattern recognition and machine learning can use/reuse the data for different investigations in different application domains such as motion analysis, human-robot cooperation, action recognition, and so on.Dataset detailsThe dataset includes 12 assembly actions performed by 41 subjects for building an Epicyclic Gear Train (EGT).The assembly task involves three phases first, the assembly of Block 1 and Block 2 separately, and then the final setting up of both Blocks to build the EGT. The EGT is made up of a total of 12 components divided into two sets: the first eight components for building Block 1 and the remaining four components for Block 2. Finally, two screws are fixed with an Allen Key to assemble the two blocks and thus obtain the EGT.Acquisition setupThe acquisition experiment took place in two laboratories (one in Italy and one in Spain), where an acquisition area was reserved for the experimental setup. A Microsoft Azure Kinect camera acquires videos during the execution of the assembly task. It is placed in front of the operator and the table where the components are spread over. The camera is place on a tripod at an height h of 1.54 m and a distance of 1.78m. The camera is down-tilted by an angle of 17 degrees.Technical informationThe HA4M dataset contains 217 videos of the assembly task performed by 41 subjects (15 females and 26 males). Their ages ranged from 23 to 60. All the subjects participated voluntarily and were provided with a written description of the experiment. Each subject was asked to execute the task several times and to perform the actions at their own convenience (e.g. with both hands), independently from their dominant hand. The HA4M project is a growing project. So new acquisitions, planned in the next future, will expand the current dataset.ActionsTwelve actions are considered in HA4M. Actions from 1 to 4 are needed to build Block 1, then actions from 5 to 8 for building Block 2 and finally, the actions from 9 to 12 for completing the EGT. Actions are listed below:Pick up/Place CarrierPick up/Place Gear Bearings (x3)Pick up/Place Planet Gears (x3)Pick up/Place Carrier ShaftPick up/Place Sun ShaftPick up/Place Sun GearPick up/Place Sun Gear BearingPick up/Place Ring BearPick up Block 2 and place it on Block 1Pick up/Place CoverPick up/Place Screws (x2)Pick up/Place Allen Key, Turn Screws, Return Allen Key and EGTAnnotationData annotation concerns the labeling of the different actions in the video sequences.The annotation of the actions has been manually done by observing the RGB videos, frame by frame. The start frame of each action is identified as the subject starts to move the arm to the component to be grasped. The end frame, instead, is recorded when the subject releases the component, so the next frame becomes the start frame of the subsequent action.The total number of actions annotated in this study is 4123, including the “don't care” action (ID=0) and the action repetitions in the case of actions 2, 3 and 11.Available codeThe dataset has been acquired using the Multiple Azure Kinect GUI software, available at https://gitlab.com/roberto.marani/multiple-azure-kinect-gui, based on the Azure Kinect Sensor SDK v1.4.1 and Azure Kinect Body Tracking SDK v1.1.2.The software records device data to a Matroska (.mkv) file, containing video tracks, IMU samples, and device calibration. In this work, IMU samples are not considered.The same Multiple Azure Kinect GUI software processes the Matroska file and returns the different types of data provided with our dataset: RGB images, RGB-depth-Aligned (RGB-A) images, Depth images, IR images, Point Cloud and Skeleton data.
description: OverviewThe HA4M dataset is a collection of multi-modal data relative to actions performed by different subjects in an assembly scenario for manufacturing. It has been collected to provide a good test-bed for developing, validating and testing techniques and methodologies for the recognition of assembly actions. To the best of the authors' knowledge, few vision-based datasets exist in the context of object assembly.The HA4M dataset provides a considerable variety of multi-modal data compared to existing datasets. Six types of simultaneous data are supplied: RGB frames, Depth maps, IR frames, RGB-Depth-Aligned frames, Point Clouds and Skeleton data.These data allow the scientific community to make consistent comparisons among processing approaches or machine learning approaches by using one or more data modalities. Researchers in computer vision, pattern recognition and machine learning can use/reuse the data for different investigations in different application domains such as motion analysis, human-robot cooperation, action recognition, and so on.Dataset detailsThe dataset includes 12 assembly actions performed by 41 subjects for building an Epicyclic Gear Train (EGT).The assembly task involves three phases first, the assembly of Block 1 and Block 2 separately, and then the final setting up of both Blocks to build the EGT. The EGT is made up of a total of 12 components divided into two sets: the first eight components for building Block 1 and the remaining four components for Block 2. Finally, two screws are fixed with an Allen Key to assemble the two blocks and thus obtain the EGT.Acquisition setupThe acquisition experiment took place in two laboratories (one in Italy and one in Spain), where an acquisition area was reserved for the experimental setup. A Microsoft Azure Kinect camera acquires videos during the execution of the assembly task. It is placed in front of the operator and the table where the components are spread over. The camera is place on a tripod at an height h of 1.54 m and a distance of 1.78m. The camera is down-tilted by an angle of 17 degrees.Technical informationThe HA4M dataset contains 217 videos of the assembly task performed by 41 subjects (15 females and 26 males). Their ages ranged from 23 to 60. All the subjects participated voluntarily and were provided with a written description of the experiment. Each subject was asked to execute the task several times and to perform the actions at their own convenience (e.g. with both hands), independently from their dominant hand. The HA4M project is a growing project. So new acquisitions, planned in the next future, will expand the current dataset.ActionsTwelve actions are considered in HA4M. Actions from 1 to 4 are needed to build Block 1, then actions from 5 to 8 for building Block 2 and finally, the actions from 9 to 12 for completing the EGT. Actions are listed below:Pick up/Place CarrierPick up/Place Gear Bearings (x3)Pick up/Place Planet Gears (x3)Pick up/Place Carrier ShaftPick up/Place Sun ShaftPick up/Place Sun GearPick up/Place Sun Gear BearingPick up/Place Ring BearPick up Block 2 and place it on Block 1Pick up/Place CoverPick up/Place Screws (x2)Pick up/Place Allen Key, Turn Screws, Return Allen Key and EGTAnnotationData annotation concerns the labeling of the different actions in the video sequences.The annotation of the actions has been manually done by observing the RGB videos, frame by frame. The start frame of each action is identified as the subject starts to move the arm to the component to be grasped. The end frame, instead, is recorded when the subject releases the component, so the next frame becomes the start frame of the subsequent action.The total number of actions annotated in this study is 4123, including the “don't care” action (ID=0) and the action repetitions in the case of actions 2, 3 and 11.Available codeThe dataset has been acquired using the Multiple Azure Kinect GUI software, available at https://gitlab.com/roberto.marani/multiple-azure-kinect-gui, based on the Azure Kinect Sensor SDK v1.4.1 and Azure Kinect Body Tracking SDK v1.1.2.The software records device data to a Matroska (.mkv) file, containing video tracks, IMU samples, and device calibration. In this work, IMU samples are not considered.The same Multiple Azure Kinect GUI software processes the Matroska file and returns the different types of data provided with our dataset: RGB images, RGB-depth-Aligned (RGB-A) images, Depth images, IR images, Point Cloud and Skeleton data.