TST Fall detection dataset v2

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Abstract: 

The dataset contains depth frames and skeleton joints collected using Microsoft Kinect v2 and acceleration samples provided by an IMU during the simulation of ADLs and falls.

Instructions: 

The dataset is composed by ADLs (Activity of Daily Living) and falls simulated by 11 young actors. The following actions are part of ADL category:

  • sit, the actor sits on a chair;
  • grasp, the actor walks and grasps an object from the floor;
  • walk, the actor walks back and forth;
  • lay, the actor lies down on the floor;

 

The following actions are part of the fall category:

  • front, the actor falls from the floor and ends up lying;
  • back, the actor falls backward and ends up lying;
  • side, the actor falls to the side and ends up lying;
  • EUpSit, the actor falls backward and ends up sitting.

 

Each actor repeated each action 3 times, generating a total number of 264 sequences. For each sequence, the following data are available:

  • depth frames, resolution of 512x424, captured by Kinect v2;
  • two raw acceleration streams, provided by IMUs constrained to the waist and right wrist of the actor;
  • skeleton joints in depth and skeleton space, captured by Microsoft SDK 2.0 (see JointType enumeration for the order: https://msdn.microsoft.com/en-us/library/microsoft.kinect.kinect.jointty...)
  • timing information, timestamps of Kinect frames and acceleration samples, useful for synchronization

 

This code can be used to read data with MATLAB:

http://www.tlc.dii.univpm.it/dbkinect/FallDetection/MatlabCode.zip

 

If you use the dataset, please cite the following paper:

S. Gasparrini, E. Cippitelli, E. Gambi, S. Spinsante, J. Wahslen, I. Orhan and T. Lindh, “Proposal and Experimental Evaluation of Fall Detection Solution Based on Wearable and Depth Data Fusion”, ICT Innovations 2015, Springer International Publishing, 2016. 99-108, doi: 10.1007/978-3-319-25733-4_11.

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Enea Cippitelli, Ennio Gambi, Samuele Gasparrini, Susanna Spinsante
Submitted by:
Susanna Spinsante
Last updated:
Fri, 01/06/2017 - 15:26
DOI:
10.21227/H2QP48
Data Format:
Links:
 
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[1] Enea Cippitelli, Ennio Gambi, Samuele Gasparrini, Susanna Spinsante, "TST Fall detection dataset v2", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H2QP48. Accessed: Jul. 17, 2018.
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url = {http://dx.doi.org/10.21227/H2QP48},
author = {Enea Cippitelli; Ennio Gambi; Samuele Gasparrini; Susanna Spinsante },
publisher = {IEEE Dataport},
title = {TST Fall detection dataset v2},
year = {2016} }
TY - DATA
T1 - TST Fall detection dataset v2
AU - Enea Cippitelli; Ennio Gambi; Samuele Gasparrini; Susanna Spinsante
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Enea Cippitelli, Ennio Gambi, Samuele Gasparrini, Susanna Spinsante. (2016). TST Fall detection dataset v2. IEEE Dataport. http://dx.doi.org/10.21227/H2QP48
Enea Cippitelli, Ennio Gambi, Samuele Gasparrini, Susanna Spinsante, 2016. TST Fall detection dataset v2. Available at: http://dx.doi.org/10.21227/H2QP48.
Enea Cippitelli, Ennio Gambi, Samuele Gasparrini, Susanna Spinsante. (2016). "TST Fall detection dataset v2." Web.
1. Enea Cippitelli, Ennio Gambi, Samuele Gasparrini, Susanna Spinsante. TST Fall detection dataset v2 [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H2QP48
Enea Cippitelli, Ennio Gambi, Samuele Gasparrini, Susanna Spinsante. "TST Fall detection dataset v2." doi: 10.21227/H2QP48