This study investigates the performance of an updated version of our pre-impact detection algorithm while parsing out hip kinematics in order to identify unexpected tripping-like perturbations during walking. This approach grounds on the hypothesis that due to unexpected gait disturbances, the cyclic features of hip kinematics are suddenly altered thus promptly highlighting that the balance is challenged. To achieve our goal, hip angles of eight healthy young subjects were recorded while they were managing unexpected tripping trials delivered during the steady locomotion. Results showed that the updated version of our pre-impact detection algorithm allows for identifying a lack of balance due to tripping-like perturbations, after a suitable tuning of the algorithm parameters. The best performance is represented by a mean detection time ranging within 0.8-0.9 s with a low percentage of false alarms (i.e., lower than 10%). Accordingly, we can conclude that the proposed strategy is able to detect lack of balance due to different kinds of gait disturbances (e.g., slippages, tripping) and that it could be easily implemented in lower limb orthoses/prostheses since it only relies on joint angles.

Pre-impact detection algorithm to identify lack of balance due to tripping-like perturbations

Aprigliano F.
;
Guaitolini M.;Sabatini A. M.;Micera S.;Monaco V.
2019

Abstract

This study investigates the performance of an updated version of our pre-impact detection algorithm while parsing out hip kinematics in order to identify unexpected tripping-like perturbations during walking. This approach grounds on the hypothesis that due to unexpected gait disturbances, the cyclic features of hip kinematics are suddenly altered thus promptly highlighting that the balance is challenged. To achieve our goal, hip angles of eight healthy young subjects were recorded while they were managing unexpected tripping trials delivered during the steady locomotion. Results showed that the updated version of our pre-impact detection algorithm allows for identifying a lack of balance due to tripping-like perturbations, after a suitable tuning of the algorithm parameters. The best performance is represented by a mean detection time ranging within 0.8-0.9 s with a low percentage of false alarms (i.e., lower than 10%). Accordingly, we can conclude that the proposed strategy is able to detect lack of balance due to different kinds of gait disturbances (e.g., slippages, tripping) and that it could be easily implemented in lower limb orthoses/prostheses since it only relies on joint angles.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11382/532523
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