Supervised machine learning scheme for electromyography-based pre-fall detection system

Data di pubblicazione: 31 Gen 2018

RivistaFonte dati: OPENALEXTipo OpenAlex: articleAccesso chiuso

Falls are the leading cause of disability and death among the elderly. Over the years, several inertial-based wearable devices for automatic fall and pre-fall detection have been devised. Under controlled condition, these systems show a high performance for unbalance detection (up to 100% of specificity and sensitivity), however the mean lead time before the impact is about 200–400 ms. Although this period of time is enough to active an impact reduction system (i.e wearable airbag) to minimize injury, it is necessary to increase it so as to improve the system efficiency and reliability. A user's muscle behavior analysis could be more strategic than that of a kinematic evaluation one, permitting a rapid recognition of an imbalance event. This also holds true for several research studies on muscles response during a state of imbalance, whereas a limit number of them deal with the development of wearable …

Fonte
Expert Systems with Applications
Volume
100
Pagine
95-105
Citazioni
76
ID archivio
f1814c04bedc1ed1dda1dcf366042297
Riferimenti
Volume: 100 Pages: 95-105