Detecting Slipping-Like Perturbations by Using Adaptive Oscillators
This study introduces a novel algorithm to detect unexpected slipping-like perturbations based on the comparison between actual leg joint angles and those predicted by a pool of adaptive oscillators. The approach grounds on the hypothesis that during postural transitions, the difference between these datasets diverges and can early signal that the dynamic balance is challenged. To test this hypothesis, leg joint angles of twelve healthy young participants were recorded while undergoing four different perturbations delivered during steady locomotion. Joint angles were estimated after spanning the whole domain of the adaptive oscillator dynamics. Results confirmed that the implemented strategy allows to early detect a postural transition induced by a slipping-like perturbation: the best performance is represented by a mean detection time ranging between 150 and 250 ms and a low rate (lower than 10%) of false alarms. On the whole, the proposed approach is efficient even if it is based on a quite simple threshold-based algorithm. Moreover, it does not need any falling-based training before being implemented, is not computationally heavy, and is not subject dependent. Finally, since it is based on leg joint angles, it appears well suited to be implemented in lower-limb orthoses/prostheses already equipped with joint position sensors.