Subject	Computer Science Applications
Specific subject area	Fall detection, the act of differentiating ordinary daily activities from the accidental fall event.
Type of data	Table
How data were acquired	Data were acquired using the smart floor, a floor surface with embedded Force Sensing Resistors (FSR). The smart floor has 16 FSR sensors linked to an ArduinoMega microcontroller. Fall data is recorded using a Java program on a personal computer linked to the ArduinoMega. The Java program and the Arduino sketch are provided as supplementary material.
	Additional data was acquired using the questionnaire provided as supplementary material under the file name questionnaireExample.pdf.
Data format	Raw
Parameters for data collection	The data gathering was organized as an open event, where anyone could volunteer to contribute. We have not established any constraint on the age or physical traits of participants. Each participant performed 7 different fall events.
Description of data collection	The data were collected at a data gathering event organized in a gym. Each participant was asked to simulate 7 different fall events. The data was recorded using the smart floor [4]. A proprietary program that collects data from sensors and manages the data gathering process was used in the experiment. The program is added as part of the dataset, but it is also accessible on Gitlab: https://gitlab.com/Dormage/smart-floor-fall-detection. The version tag 031194d73413a7bbdb68825236bd96f457735b30 was used in data gathering process. A total of 420 fall events were recorded.
Data source location	Koper - Slovenia
Data accessibility	Repository name: Zenodo Data identification number: https://doi.org/10.5281/zenodo.4605619 Direct URL to data: https://zenodo.org/record/4605619 Instructions for accessing these data: unzip the archive, all data is distributed in folders for easy access. There are two main folders: dataset and program. Data is distributed in csv format, each line represents one experiment (one person simulating the falls).