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

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 cycli...

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Published in:Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Vol. 2019; pp. 2430 - 2433
Main Authors: Aprigliano, F., Guaitolini, M., Sabatini, A. M., Micera, S., Monaco, V.
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.07.2019
Subjects:
Accidental Falls
Algorithms
Biomechanical Phenomena
Detection algorithms
Foot
Gait
Healthy Volunteers
Hip
Hip - physiology
Humans
Kinematics
Legged locomotion
Perturbation methods
Postural Balance
Tuning
Walking
ISSN:1557-170X, 2694-0604, 1558-4615, 2694-0604
Online Access:Get full text
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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.
AbstractList 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.
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.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.
Author Micera, S.
Aprigliano, F.
Sabatini, A. M.
Monaco, V.
Guaitolini, M.
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Snippet This study investigates the performance of an updated version of our pre-impact detection algorithm while parsing out hip kinematics in order to identify...
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SubjectTerms Accidental Falls
Algorithms
Biomechanical Phenomena
Detection algorithms
Foot
Gait
Healthy Volunteers
Hip
Hip - physiology
Humans
Kinematics
Legged locomotion
Perturbation methods
Postural Balance
Tuning
Walking
Title Pre-impact detection algorithm to identify lack of balance due to tripping-like perturbations
URI https://ieeexplore.ieee.org/document/8856383
https://www.ncbi.nlm.nih.gov/pubmed/31946389
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Volume 2019
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