3D Kinect Total Body Database for Back Stretches
收藏DataCite Commons2020-08-27 更新2025-04-16 收录
下载链接:
https://kilthub.cmu.edu/articles/3D_Kinect_Total_Body_Database_for_Back_Stretches/7999364
下载链接
链接失效反馈官方服务:
资源简介:
The data was collected by a Kinect V2 as a set of X, Y, Z coordinates at 60 fps during 6 different yoga inspired back stretches. <br>There are 541 files in the dataset, each containing position, velocity for 25 body joints. These joints include: Head, Neck, SpineShoulder, SpineMid, SpineBase, ShoulderRight, ShoulderLeft, HipRight, HipLeft, ElbowRight, WristRight, HandRight, HandTipRight, ThumbRight, ElbowLeft, WristLeft, HandLeft, HandTipLeft, ThumbLeft, KneeRight, AnkleRight, FootRight, KneeLeft, AnkleLeft, FootLeft. <br>The program used to record this data was adapted from Thomas Sanchez Langeling’s skeleton recording code. The file was set to record data for each body part as a separate file, repeated for each exercise. Each bodypart for a specific exercise is stored in a distinct folder. These folders are named with the following convention: subjNumber_stretchName_trialNumber <br>The subjNumber ranged from 0 – 8. The stretchName was one of the following: Mermaid, Seated, Sumo, Towel, Wall, Y. The trialNumber ranged from 0 – 9 and represented the repetition number. <br>These coordinates were chosen to have an origin centered at the subject’s upper chest. <br>The data was standardized to the following conditions: 1) Kinect placed at the height of 2 ft and 3 in 2) Subject consistently positioned 6.5 ft away from the camera with their chests facing the camera 3) Each participant completed 10 repetitions of each stretch before continuing on <br>Data was collected from the following population: * Adults ages 18-21 * Females: 4 * Males: 5 <br>The following types of pre-processing occurred at the time of data collection. Velocity Data: Calculated using a discrete derivative equation with a spacing of 5 frames chosen to reduce sensitivity of the velocity function v[n]=(x[n]-x[n-5])/5 Occurs for all body parts and all axes individually <br><br>Related manuscript: Capella, B., Subrmanian, D., Klatzky, R., & Siewiorek, D. Action Pose Recognition from 3D Camera Data Using Inter-frame and Inter-joint Dependencies. Preprint at link in references.
提供机构:
Carnegie Mellon University
创建时间:
2019-05-15



