Fall risk evaluation by surface electromyography technology

Abstract

Falls are one of the main causes of disability and death among the elderly. Several inertial-based wearable devices for automatic fall and pre-fall detection have been realized. They use the threshold-based approach above all and their mean lead-time before the impact is about 200–500 ms. The main purpose of the work was to develop a framework for fall risk assessment considering the lower limb surface electromyography. The user's muscle behavior was chosen because it may allow a faster recognition of an imbalance event than the user's kinematic evaluation. Moreover, a machine learning scheme was adopted to overcome the drawbacks of well-known threshold approaches, in which the algorithm parameters have to be set according to the users' specific physical characteristics. Ten kinds of time-domain features, commonly used in the analysis of the lower-limb muscle activity, were investigated and the Markov Random Field based Fisher-Markov selector was used to reduce the signal processing complexity. The supervised classification phase was obtained through a low computational cost and a high classification accuracy Linear Discriminant Analysis. The developed system showed high performance in terms of sensitivity and specificity (about 90%) in controlled conditions, with a mean lead-time before the impact of about 775 ms.