Falls are very dangerous events among elderly people. Several automatic fall detectors have been developed to reduce the time of the medical intervention, but they cannot avoid the injures due to the fall. In this paper a study about the feasibility of a wearable system for the detection of the fall risk is presented. The aim is to detect the fall before to the impact on the floor, allowing the intervention of an impact reduction system. An electromyography-based system has been chosen because it can recognize the risk of fall faster than other more commonly used inertial sensors based system. The logical framework implements the feature extraction procedure, demonstrating a higher discriminative power of Co-contraction Indices and Integrated EMG for which at least of 70% of specificity and sensitivity are achieved in the classification process.
19 Nov 2015
2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL)