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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 …
Publication date: 
27 Jun 2017
Biblio References: 
Pages: 1092-1095
2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)