Wearable sensors are becoming popular for remote health monitoring as technology improves and cost reduces. One area in which wearable sensors are increasingly being used is falls monitoring. The elderly, in particular are vulnerable to falls and require continuous monitoring. Indeed, many attempts, with insufficient success have been made towards accurate, robust and generic falls and Activities of Daily Living (ADL) classification. A major challenge in developing solutions for fall detection is access to sufficiently large data set. This paper presents a description of the data set and the experimental protocols designed by the authors for the simulation of falls, near-falls and ADL. Forty-two volunteers were recruited to participate in an experiment that involved a set of scripted protocols. Four types of falls (forward, backward, lateral left and right) and several ADL were simulated. This data set is intended for the evaluation of fall detection algorithms by combining daily activities and transitions from one posture to another with falls. In our prior work, machine learning based fall detection algorithms were developed and evaluated. Results showed that our algorithm was able to discriminate between falls and ADL with an F-measure of 94%.
The files are available for download via HTTP. Link: http://skuld.cs.umass.edu/traces/mmsys/2015/paper-15/
The original link was: http://skuld.cs.umass.edu/traces/mmsys/2015/paper-15/
References and Citation
Use of the datasets in published work should be acknowledged by a full citation to the authors' papers [OGB15] at the MMSys conference (Proceedings of ACM MMSys '15, Portland, Oregon, March 18-20, 2015).
OGB15: O. Ojetola, E. Gaura, J. Brusey. Data Set of Fall Events and Daily Activities from Inertial Sensors, Proceedings of ACM MMSys '15, Portland, Oregon, March 18-20, 2015.