TST Intake Monitoring dataset v1
- Citation Author(s):
- Enea Cippitelli, Ennio Gambi, Samuele Gasparrini, Susanna Spinsante
- Submitted by:
- Susanna Spinsante
- Last updated:
- Thu, 11/08/2018 - 10:34
- Data Format:
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 35 young actors.
Each actor repeated each action 1/2 times, generating a total number of 48 sequences. 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/LoadNets_And_GT.zip
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_Instake_Monitoring_Test1_10.zip (110.02 MB)
- Test 11-20 TST_Instake_Monitoring_Test11_20.zip (103.62 MB)
- Test 21-30 TST_Instake_Monitoring_Test21_30.zip (102.98 MB)
- Test 31-40 TST_Instake_Monitoring_Test31_40.zip (121.59 MB)
- Test 41-48 TST_Instake_Monitoring_Test41_48.zip (163.73 MB)
- LoadNets_And_GT.zip (60.76 MB)
- Matlab Code TST_Instake_Monitoring_FromDepth2PC.zip (23.20 kB)
- Create Video Matlab TST_Instake_Monitoring_CreateVideo.m (5.47 kB)