Developing a Falls Prediction Tool Using Both Accelerometer and Video Gait Analysis Data in Older Adults

Recent advances in machine learning have disrupted the standard approach to assessing medical prognosis. Our group, consisting of academic clinicians and research engineers, seeks to create a database of stability measures ( accelerometers, gyroscopes and altitude sensors data) in older adults monitores longitudinally. This Stability Measures (SM) database will allow us to use new machine learning methods to develop and validate algorithms that predict future falls, allowing to better targetting of vulnerable individuals.

Although there have been numerous attempts to quantify fall risk in older adults using bedside scales 7-12, no previous group has attempted to use a combination of both accelerometer and video measures to assess gait stability. Since these measures will be captured in both frequently falling and infrequently falling patients, we will have SM data for various windows of time (1, 2, 3 and 4-weeks) prior to at least 100 fall events, a dataset that has never been captured before.

HYPOTHESES:

1. A combination of accelerometer, gyroscope, and video data can be used to predict falls longitudinally, first by the use of a training dataset followed by verification on a validation data set.
2. All the above sensor-based inputs can be combined as a simple, automated predcition tool to predict fall risk in older adults Current Methods of Falls Risk Assessment: Current methods of predicting falls in physician offices rely heavily on simple bedside tests12-14. Although useful, all of these measures have quite low sensitivity and specificity, with an Area Under the Curve (AUC) of approximately 0.707-12.

In fact, a recent meta-analysis "could not identify any tool which had an optimal balance between sensitivity and specificity, or which was clearly better than a simple clinical judgment of risk of falling"