<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Plantar pressure is the main mechanical parameter to understand human lower limb movement, which can fully show the health status of human spine, foot, leg and other regions.At present, researchers have carried out empirical analysis on the rehabilitation training process of lower limbs, but due to the defects of guidance methods and too vague evaluation indicators, it is impossible to conduct comprehensive monitoring and feedback on the rehabilitation process of patients. Therefore, on the basis of the modeling method based on the understanding of skeletal muscle set, the rest of the lower limbs dyskinesia patients rehabilitation training, puts forward the plantar pressure changes as the core of human lower limb rehabilitation training monitoring and feedback methods, at the same time using autoregressive model to construct the algorithm model of lower limb exoskeleton, updating method of the weighted coefficient of contrast research. The final results show that the least mean square error (LMS) algorithm can provide effective driving force for the rehabilitation training model of lower extremity exoskeleton disease, and feedback evaluation of the whole rehabilitation training process</p>
</abstract>

Plantar pressure is the main mechanical parameter to understand human lower limb movement, which can fully show the health status of human spine, foot, leg and other regions.At present, researchers have carried out empirical analysis on the rehabilitation training process of lower limbs, but due to the defects of guidance methods and too vague evaluation indicators, it is impossible to conduct comprehensive monitoring and feedback on the rehabilitation process of patients. Therefore, on the basis of the modeling method based on the understanding of skeletal muscle set, the rest of the lower limbs dyskinesia patients rehabilitation training, puts forward the plantar pressure changes as the core of human lower limb rehabilitation training monitoring and feedback methods, at the same time using autoregressive model to construct the algorithm model of lower limb exoskeleton, updating method of the weighted coefficient of contrast research. The final results show that the least mean square error (LMS) algorithm can provide effective driving force for the rehabilitation training model of lower extremity exoskeleton disease, and feedback evaluation of the whole rehabilitation training process

Study on the rehabilitation training effect of exoskeletal diseases based on the skeletal muscle geometry modeling method

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Plantar pressure is the main mechanical parameter to understand human lower limb movement, which can fully show the health status of human spine, foot, leg and...

Study on the rehabilitation training effect of exoskeletal diseases based on the skeletal muscle geometry modeling method

Pubblicato online: 10 lug 2023

Pagine: 3139 - 3156

Ricevuto: 01 set 2022

Accettato: 16 gen 2023

DOI: https://doi.org/10.2478/amns.2023.2.00026

Parole chiave
Plantar pressure, Skeletal muscle, Set modeling, Exoskeleton. Disease rehabilitation training

© 2023 Ning Gong, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Study on the rehabilitation training effect of exoskeletal diseases based on the skeletal muscle geometry modeling method

Pubblicato online: 10 lug 2023

Pagine: 3139 - 3156

Ricevuto: 01 set 2022

Accettato: 16 gen 2023

DOI: https://doi.org/10.2478/amns.2023.2.00026

Parole chiavePlantar pressure, Skeletal muscle, Set modeling, Exoskeleton. Disease rehabilitation training

© 2023 Ning Gong, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Plantar pressure, Skeletal muscle, Set modeling, Exoskeleton. Disease rehabilitation training