[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121406-en":3,"doc-seo-121406-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121406,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Length Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning","McKibben artificial muscle is a pneumatic soft actuator whose flexible structure and high power-to-weight ratio suit human-oriented applications. A smart version is built by integrating an optical fiber into the muscle sleeve so that macrobending loss can be used for length sensing. Experiments show the sensor response depends on length, load, and applied air pressure due to stress-induced microbending loss. An LSTM-based machine learning model estimates muscle length with reduced error, demonstrating feasibility for improving measurement accuracy.","Article  \nLength Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning  \nYilei Ni, Shuichi Wakimoto *, Weihang Tian, Yuichiro Toda , Takefumi Kanda  and Daisuke Yamaguchi   \nAcademic Editors: Flavio Esposito, Stefania Campopiano and Agostino Iadicicco  \nReceived: 22 January 2025  \nRevised: 21 March 2025  \nAccepted: 27 March 2025  \nPublished: 1 April 2025  \nCitation: Ni, Y.; Wakimoto, S.; Tian, W.; Toda, Y.; Kanda, T.; Yamaguchi, D. Length Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning. Sensors 2025, 25, 2221. [https://](https://)[ ](https://)[doi.org/10.3390/s25072221](doi.org/10.3390/s25072221)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nGraduate School of Environmental, Life, Natural Science and Technology, Okayama University,  \nOkayama 700-8530, Japan; [ni22@s.okayama-u.ac.jp](ni22@s.okayama-u.ac.jp) (Y.N.); [den_19@s.okayama-u.ac.jp](den_19@s.okayama-u.ac.jp) (W.T.);  \n[ytoda@okayama-u.ac.jp](ytoda@okayama-u.ac.jp) (Y.T.); [kanda-t@okayama-u.ac.jp](kanda-t@okayama-u.ac.jp) (T.K.); [yamaguchi20@okayama-u.ac.jp](yamaguchi20@okayama-u.ac.jp) (D.Y.)  \n* [Correspondence: wakimoto@okayama-u.ac.jp](Correspondence: wakimoto@okayama-u.ac.jp)  \nAbstract: A McKibben artificial muscle is a soft actuator driven by air pressure, characterized by its flexibility, lightweight design, and high power-to-weight ratio. We have developed a smart artificial muscle that is capable of sensing its motion. To enable this sensing function, an optical fiber was integrated into the sleeve consisting of multiple fibers and serving as a component of the McKibben artificial muscle. By measuring the macrobending loss of the optical fiber, the length of the smart artificial muscle is expected to be estimated. However, experimental results indicated that the sensor’s characteristics depend not only on the length but also on the load and the applied air pressure. This dependency arises because the stress applied to the optical fiber increases, causing microbending loss. In this study, we employed a machine learning model, primarily composed of Long Short-Term Memory (LSTM) neural networks, to estimate the length of the smart artificial muscle. The experimental results demonstrate that the length estimation obtained through machine learning exhibits a smaller error. This suggests that machine learning is a feasible approach to enhancing the length measurement accuracy of the smart artificial muscle.  \nKeywords: McKibben artificial muscle; machine learning; optical fiber; motion estimation  \n1. Introduction  \nThe McKibben artificial muscle [1,2], a typical pneumatic soft actuator, contracts axially and expands radially, with its contraction displacement serving as the mechanical output. This actuator has a simple structure, consisting of a rubber tube and sleeve fibers, and is known for its excellent compatibility with human applications and high power-toweight ratio. In recent years, McKibben artificial muscles have been incorporated into general-purpose for power-assist and rehabilitation devices, which are now commercially available [3,4] .  \nDespite its effectiveness as an actuator, the McKibben artificial muscle faces challenges in control due to the mechanical nonlinearity of its rubber materials and the anisotropy of the generated force, which results from friction between the rubber and the fibers. These factors make it more difficult to accurately sense the state of artificial muscles. As a result, additional sensors, such as laser displacement sensors and linear encoders, are often required for precise measurement. However, the integration of these additional sensors frequently l","cbCaioAIxx6Wvu2z","https://ap.wps.com/l/cbCaioAIxx6Wvu2z","pdf",5121148,1,15,"English","en",105,"# Introduction\n## McKibben artificial muscle and sensing challenges\n## Optical fiber sensing and smart artificial muscle concept","[{\"question\":\"How does the optical fiber sensor estimate the smart pneumatic artificial muscle length?\",\"answer\":\"The optical fiber measures macrobending loss, which changes as the muscle deformation alters the fiber’s radius of curvature, enabling length estimation from the light attenuation.\"},{\"question\":\"Why does the sensor characteristics depend on factors besides length?\",\"answer\":\"Because stress applied to the optical fiber increases with load and air pressure, leading to microbending loss that alters the sensor response.\"},{\"question\":\"What machine learning method is used for length estimation and what is the benefit?\",\"answer\":\"An LSTM neural network-based model is used to estimate length, producing smaller estimation error than experimental baselines, improving measurement accuracy.\"}]","Length Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning | 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does the optical fiber sensor estimate the smart pneumatic artificial muscle length?","Question",{"text":75,"@type":76},"The optical fiber measures macrobending loss, which changes as the muscle deformation alters the fiber’s radius of curvature, enabling length estimation from the light attenuation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why does the sensor characteristics depend on factors besides length?",{"text":80,"@type":76},"Because stress applied to the optical fiber increases with load and air pressure, leading to microbending loss that alters the sensor response.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning method is used for length estimation and what is the benefit?",{"text":84,"@type":76},"An LSTM neural network-based model is used to estimate length, producing smaller estimation error than experimental baselines, improving measurement 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