Hybrid Simulated Annealing and Genetic Algorithm for Optimization of a Rule-based Algorithm for Detection of Gait Events in Impaired Subjects

Accurate identification of gait phases is a necessary step for control of robotic devices during gait therapy or automatic diagnosis of gait impairments. Most of the existing algorithms use a rule-based approach that takes advantage of the consistency of the gait cycle among healthy subjects. Since...

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Vydané v:IEEE/ASME International Conference on Advanced Intelligent Mechatronics s. 1167 - 1171
Hlavní autori: Perez-Ibarra, Juan C., Siqueira, Adriano A. G., Terra, Marco H., Krebs, Hermano I.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2020
Predmet:
Angular velocity
Artificial Intelligence
Foot
Gait Analysis
Genetic algorithms
Intelligent Sensors
Legged locomotion
Optimization
Performance evaluation
Protocols
Rehabilitation Robots
Wearable Sensors
ISSN:2159-6255
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Shrnutí:Accurate identification of gait phases is a necessary step for control of robotic devices during gait therapy or automatic diagnosis of gait impairments. Most of the existing algorithms use a rule-based approach that takes advantage of the consistency of the gait cycle among healthy subjects. Since impaired gait patterns lack of that inter-subject consistency, most of those algorithms have limited performance when detecting phases in impaired subjects. In this paper, we propose a new algorithm for real-time detection of four gait events (heel-strike, toe-strike, heel-off and toe-off). The proposed algorithm uses a set of threshold-based rules and to compute the adequate values for the thresholds, maximizing the performance of the algorithm, we use a hybrid meta-heuristic approach that integrates Simulated Annealing and a Genetic Algorithm. Using data collected during overground and treadmill walking trials with a wearable device equipped with an inertial sensor, we report experimental results for three subjects: one healthy, one hemiparetic, and one myelopathic. F 1 -scores for the three subjects were 0.98, 0.99, and 0.91, respectively.
ISSN:2159-6255
DOI:10.1109/AIM43001.2020.9158938