{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:52:50Z","timestamp":1767117170417,"version":"3.37.3"},"reference-count":40,"publisher":"IOP Publishing","issue":"6","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"The Hefei Institutes of Physical Science (HFIPS) Director\u2019s Fund","award":["YZJJKX202401"],"award-info":[{"award-number":["YZJJKX202401"]}]},{"name":"Anhui Province Key Research and Development Program","award":["2022i01020021"],"award-info":[{"award-number":["2022i01020021"]}]},{"name":"Research Funds of Centre for Leading Medicine and Advanced Technologies of Institute of Health and Medicine, Hefei Comprehensive National Science Center","award":["2023IHM01080"],"award-info":[{"award-number":["2023IHM01080"]}]},{"name":"University of Science and Technology of China Innovation and Entrepreneurship Fund","award":["SC2023G28"],"award-info":[{"award-number":["SC2023G28"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain\u2013computer interfaces. <jats:italic>Approach.<\/jats:italic> We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user\u2019s operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks. <jats:italic>Main results.<\/jats:italic> The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment. <jats:italic>Significance.<\/jats:italic> The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.<\/jats:p>","DOI":"10.1088\/1741-2552\/ad8b6e","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T22:51:47Z","timestamp":1729896707000},"page":"066007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands"],"prefix":"10.1088","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8159-5816","authenticated-orcid":true,"given":"Rongkai","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7236-6349","authenticated-orcid":true,"given":"Quanjun","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongqing","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"jnead8b6ebib1","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1109\/JBHI.2015.2412125","article-title":"Facial position and expression-based human\u2013computer interface for persons with tetraplegia","volume":"20","author":"Bian","year":"2016","journal-title":"IEEE J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-04-02","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-10-25","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-11-08","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}