TST Intake Monitoring dataset v2
The dataset contains depth frames collected using Microsoft Kinect v1 during the execution of food and drink intake movements.
The dataset is composed by the executions of food and drink intake actions performed by 20 young actors.
Each actor repeated each action 3 times, generating a total number of 60 sequences. The repetitions involve different movements:
- repetition 1: eat a snack using the hand and drink water from a glass (Test 1,4,7,10,.. ,58);
- repetition 2: eat a soup with a spoon and pour/drink water (Test 2,5,8,11,.. ,59);
- repetition 3: use knife and fork for the main meal and finally wiping the mouth with a napkin (Test 3,6,9,12,..,60).
For each sequence, the following data are available:
- depth frames, resolution of 320x240, captured by Kinect v1 in top-view configuration;
- coordinates of nodes computed by three unsupervised algorithms (SOM, SOM_Ex, GNG) and coordinates of ground-truth nodes (only for hands and head, manually identified): http://www.tlc.dii.univpm.it/dbkinect/FoodIntake_vr2/LoadNets_And_GT_vr2...
This code can be used to read data with MATLAB, and to convert the depth frame from pixel domain to the point cloud domain:
If you use the dataset, please cite the following paper:
S. Gasparrini, E. Cippitelli, E. Gambi, S. Spinsante and F. Florez-Revuelta, “Performance Analysis of Self-Organising Neural Networks Tracking Algorithms for Intake Monitoring Using Kinect,” 1st IET International Conference on Technologies for Active and Assisted Living (TechAAL), 6th November 2015, Kingston upon Thames (UK).
- Test 1-10 TST_Intake_Monitoring_V2_Test1_10.zip (213.09 MB)
- Test 11-20 TST_Intake_Monitoring_V2_Test11_20.zip (205.34 MB)
- Test 21-30 TST_Intake_Monitoring_V2_Test21_30.zip (207.36 MB)
- Test 31-40 TST_Intake_Monitoring_V2_Test31_40.zip (200.75 MB)
- Test 41-50 TST_Intake_Monitoring_V2_Test41_50.zip (200.38 MB)
- Test 51-60 TST_Intake_Monitoring_V2_Test51_60.zip (202.56 MB)
- LoadNets_And_GT_vr2.zip (253.56 MB)
- Matlab Code TST_Intake_Monitoring_V2_FromDepth2PC.zip (23.20 kB)