Data Sharing

Databrary - Sharing Videos of Falls in Long Term Care

In September 2018, the IPML began sharing a unique collection of videos and related information from falls in two long term care facilities in the Vancouver area on Databrary, an NYU based data sharing network. We are continually adding to the collection. 

Interested members can request join the network and gain access to this collection for their research and education purposes. 

Please inform us if you have any questions or of any publications using this dataset via email to so we can provide a link to your publication on our webpage



The dataset contains 239 videos of falls experienced by 100 adults (52 women who accounted for 152 falls, and 48 men who accounted for 87 falls). At the time of falling, the individuals ranged in age from 58 years to 98 years (mean age = 83.3 years, SD = 7.4  years). All individuals captured falling were residing in two partnering long-term care facilities in British Columbia, where the falls occurred. All falls occurred in common areas of the long-term care homes (lounges, dining rooms and hallways). No cameras were installed in bedrooms or bathrooms.


Each folder also contains a video of the fall, an Excel file (.xlsx) with information from the fall incident report and minimum data set (MDS) assessment taken within 6 months before the fall, along with the number of camera views, original frame rate and resolution of the video. 


Shared Inertial Measurement Unit Dataset

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 ( and to inform us of any publication using this dataset, so we can point to your publication on our webpage.