A. Arezoomand, P. S. Razi, and M. Fakharzade, "SUTFall: a Dataset Based on Inertial and Magnetic Sensors for the Research on Elderly Fall Detection," (In Progress), 2019.
- In spite of the fact that in recent years research on Elderly Fall Detection(EDF) have been prompted due to progress in machine learning and deep learning methods, as it is an inseparable part of any data-scientific method, the need for datasets that contains a large number of samples with appropriate related tagging is getting bolder. This work demonstrates a newly released dataset that is grounded in experiences of the extensive literature of the Elderly Fall Detection and its aim covering all shortcomings that are reported. SUTFall is consists of over 2000 samples that are recorded from the Activities of Daily Life(ADL) and intentional falls of 30 subjects. All of the samples are tagged using a novel tagging method, which improves the accuracy and reliability of the machine learning and neural network methods.
A. Arezoomand, A. Alizade, and H. Khorsand, "Effect of Fabrication Process and Material Parameters on Space Holder Removal and Porosity in Titanium Base Foams for Medical Applications," (in Farsi), 3rd national conference of material, chemistry and safety engineering, Conference Article 2017.
[Online] Available: https://www.civilica.com/Paper-MCIS03-MCIS03_082.html
A. Alizade, A. Arezoomand, and H. Khorsand, "Investigation of Abrasive Agent effect on Osseointegration of Ti-6Al-4V Implants Manufactured by Sandblasting," (in Farsi), 3rd national conference of material, chemistry and safety engineering, Conference Paper 2017.
[Online] Available: https://www.civilica.com/Paper-MCIS03-MCIS03_122.html