Motion Path Design for Specific Muscle Training Using Neural Network | Zendy

Kenta Itokazu | Zendy; Takanori Miyoshi | Zendy; Kazuhiko Terashima | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Motion Path Design for Specific Muscle Training Using Neural Network

Author(s) -

Kenta Itokazu,

Takanori Miyoshi,

Kazuhiko Terashima

Publication year - 2013

Publication title -

journal of robotics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.303

H-Index - 14

eISSN - 1687-9619

pISSN - 1687-9600

DOI - 10.1155/2013/810909

Subject(s) - computer science , training (meteorology) , path (computing) , signal (programming language) , motion (physics) , artificial neural network , rehabilitation , physical medicine and rehabilitation , artificial intelligence , simulation , physical therapy , medicine , computer network , physics , meteorology , programming language

Specific muscle training is expected to be used for efficient rehabilitation and care prevention. In this paper, we propose algorithms for designing a motion path capable of strengthening specific muscles. By using the proposed algorithms, it is possible to design a motion path maximizing the activity of an agonist muscle and minimizing that of other muscles. For training, the load is applied by using a 2-link arm. EMG signal is measured during a training experiment, and the degree of muscular revitalization is evaluated by the amplitude of EMG signal. Finally, the effectiveness of the proposed approach is demonstrated through experiments.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research