Description:	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 (stever@sfu.ca and oaziz@sfu.ca) to inform us of any publication using this dataset, so we can point to your publication on our webpage. Format of data is tabular and content type is sensor data. Software used was Excel. Confidentiality declaration: The dataset does not contain personal identifiable information. All human subjects provided written consent prior to data collection. This dataset was originally deposited in the Simon Fraser University institutional repository.
Authors:	Robinovitch, Stephen; Simon Fraser University; 0000-0003-3881-6227 Aziz, Omar; Simon Fraser University
Keywords:	Sensor Fall detection IMU Biomedical Physiology and Kinesiology Inertial Measurement Unit
Field of Research:	Health sciences > Rehabilitation medicine > Kinesiology
Publication Date:	2017-12-14
Publisher:	Federated Research Data Repository / dépôt fédéré de données de recherche
Funder:	Canadian Institutes of Health Research (CIHR)
URI:	https://doi.org/10.25314/35954791-2754-47d7-893a-39430535dfdd
Related Identifiers:	This dataset is cited by https://doi.org/10.1007/s11517-016-1504-y
Appears in Collections:	SFU Research Data