Distinguishing near-falls from daily activities with wearable accelerometers and gyroscopes using Support Vector Machines

Abstract

Falls are the number one cause of injury in older adults. An individual's risk for falls depends on his or her frequency of imbalance episodes, and ability to recover balance following these events. However, there is little direct evidence on the frequency and circumstances of imbalance episodes (near falls) in older adults. Currently, there is rapid growth in the development of wearable fall monitoring systems based on inertial sensors. The utility of these systems would be enhanced by the ability to detect near-falls. In the current study, we conducted laboratory experiments to determine how the number and location of wearable inertial sensors influences the accuracy of a machine learning algorithm in distinguishing near-falls from activities of daily living (ADLs).