The paper presents a preliminary study on the feasibility of a pre-impact fall detector, able to evaluate the risk of fall real-time, allowing the fast adoption of properly strategies of intervention for reducing injuries (e.g. by activating an impact reduction system). A wearable, wireless and minimally invasive surface Electromyography-based system (EMG) has been used to measure four lower-limb muscles activities. This work deals with the identification of highly discriminative features extracted within the EMG signals for the automatic detection of people instability. The framework prototype uses a threshold-based approach assuring real-time functioning and allowing the detection of a typical imbalance condition about 200ms after the stimulus perturbation, in simulated and controlled fall conditions.
1 Nov 2015
2015 IEEE SENSORS