SFU TIPS researchers are now publicly sharing a comprehensive set of kinematic data from body-worn sensors (accelerometers, gyros and magnetometers) acquired during laboratory experiments of falls, near-falls, and activities of daily living, to use in the development and testing of automatic fall detection sensor systems. The dataset can be downloaded here.
Inertial Measurement Unit Fall Detection Dataset (IMU Dataset) is a dataset devised to benchmark fall detection and prediction algorithms based on acceleration, angular velocity and magnetic fields of body-worn APDM Opal IMU sensors recording at 128 Hz at 7 body locations (right ankle, left ankle, right thigh, left thigh, head, sternum, and waist). Detailed description of the dataset and column names are in README.txt file.
Use of this dataset in publications must be acknowledged by referencing the following publication:
- Omar Aziz, Magnus Musngi, Edward J. Park, Greg Mori, Stephen N. Robinovitch. "A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials". SpringerLink Med Biol Eng Comput (2017) 55: 45.
We also appreciate if you drop us an email (email@example.com and firstname.lastname@example.org) to inform us of any publication using this dataset, so we can point to your publication on our webpage.