The JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) is a surgical activity dataset for human motion modeling. The data was collected through a collaboration between The Johns Hopkins University (JHU) and Intuitive Surgical, Inc. (Sunnyvale, CA. ISI) within an IRB-approved study. The release of this dataset has been approved by the Johns Hopkins University IRB. The dataset was captured using the da Vinci Surgical System from eight surgeons with different levels of skill performing five repetitions of three elementary surgical tasks on a bench-top model: suturing, knot-tying and needle-passing, which are standard components of most surgical skills training curricula. The JIGSAWS dataset consists of three components:
72 PAPERS • 3 BENCHMARKS
A new multitask action quality assessment (AQA) dataset, the largest to date, comprising of more than 1600 diving samples; contains detailed annotations for fine-grained action recognition, commentary generation, and estimating the AQA score. Videos from multiple angles provided wherever available.
15 PAPERS • 2 BENCHMARKS
Consists of 1106 action samples from seven actions with quality scores as measured by expert human judges.
12 PAPERS • 1 BENCHMARK
UI-PRMD is a data set of movements related to common exercises performed by patients in physical therapy and rehabilitation programs. The data set consists of 10 rehabilitation exercises. A sample of 10 healthy individuals repeated each exercise 10 times in front of two sensory systems for motion capturing: a Vicon optical tracker, and a Kinect camera. The data is presented as positions and angles of the body joints in the skeletal models provided by the Vicon and Kinect mocap systems.
3 PAPERS • 1 BENCHMARK
The Rhythmic Gymnastics dataset contains videos of four different types of gymnastics routines: ball, clubs, hoop and ribbon. Each type of routine has 250 associated videos, and the length of each video is approximately 1 min 35 s. We chose high-standard international competition videos, including videos from the 36th and 37th International Artistic Gymnastics Competitions, to construct the dataset. We have edited out the irrelevant parts of the original videos (such as replay shots and athlete warmups). We have annotated each video with three scores (a difficulty score, an execution score and a total score), which were given by the referee in accordance with the official scoring system.
2 PAPERS • 1 BENCHMARK
Largest, first-of-its-kind, in-the-wild, fine-grained workout/exercise posture analysis dataset, covering three different exercises: BackSquat, Barbell Row, and Overhead Press. Seven different types of exercise errors are covered. Unlabeled data is also provided to facilitate self-supervised learning.
1 PAPER • NO BENCHMARKS YET
Dataset for multimodal skills assessment focusing on assessing piano player’s skill level. Annotations include player's skills level, and song difficulty level. Bounding box annotations around pianists' hands are also provided.
1 PAPER • 3 BENCHMARKS