Quantifying and Learning Human Movement Characteristics for Fall Prevention in the Elderly Using Inertial Measurement Units and Neural Networks

According to the American Academy of Family Physicians, falls are the leading cause of injury related visits to emergency departments in the United States and the primary etiology of accidental deaths in persons over the age of 65 years. By age 75, the mortality rate increases dramatically and accounts for 70 percent of accidental deaths. One third of community-dwelling elderly persons and 60 percent of nursing home residents fall each year. Over the last several years we have started to see MEMS-based inertial measurement units (IMUs) become much more cost effective and available. These devices utilize 3-axis accelerometers, 3-axis gyroscopes, and 3-axis magnetometers for acceleration, rotational, and heading/displacement data acquisition. As a six degree of freedom (6-DOF) device, data collected can closely represent and quantify how humans move. Using IMUs for the evaluation of balance, sway, and gait scenarios in humans with the goal of relating the data to forms that can be used in triggering stability threshold values may provide valuable information in preventing falls. Artificial Neural Systems, also called neural networks (NNs), are utilized to learn movement patterns and to predict motion limits in individuals. These networks work well in non-linear domains, possess fault tolerance, and are customized for a specific individual’s movement characteristics. Data has shown that specific patterns are present in movements of daily living. These patterns provide a framework for predicting what is normal and when stability thresholds are exceeded. This provides the capability to monitor and warn the user when the risk of a fall increases. A longitudinal study involving 23 ambulatory individuals ages 19 to 90 where balance, sway, and specific movement patterns are quantified has shown identifiable patterns associated with activities of daily living. A wearable IMU is being used to gather values which are analyzed through a back-propagation neural network. The resulting predictions have been used to determine what is normal and when deviations are encountered. This can eventually lead to ergonomic wearable and wireless devices that are trained to a wearer’s movement characteristics and warn them when fall risk increases.