Data about fall events and ordinary daily activities from a sensorized smart floor.

Area of Science:

• Biomedical Engineering
• Sensor Technology
• Gerontology

Background:

• Falls are a significant health risk, especially for the elderly.
• Current fall detection systems often lack accuracy or intrusiveness.
• Smart floor technology offers a potential non-intrusive solution.

Purpose of the Study:

• To collect and organize data from simulated falls using a smart floor.
• To develop and improve non-intrusive fall detection algorithms.
• To explore the use of sensor data for enhanced fall identification.

Main Methods:

• A smart floor with 16 pressure sensors was utilized.
• 60 volunteers simulated 420 distinct fall events.
• Data was recorded, organized in CSV format, and linked to participant demographics.

Main Results:

• A comprehensive dataset of simulated fall events was successfully generated.
• The data provides a foundation for developing advanced fall detection algorithms.
• The dataset includes participant characteristics for future research.

Conclusions:

• The collected smart floor data is valuable for creating more accurate, non-intrusive fall detection solutions.
• This dataset can drive innovation in identifying accidental falls.
• The data has potential for broader applications in smart living environments.