{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:52Z","timestamp":1760146012553,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation (DFG)","doi-asserted-by":"publisher","award":["BE 5729\/16-1"],"award-info":[{"award-number":["BE 5729\/16-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device\u2019s functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device\u2019s functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching 92.3\u00b12.6% on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands.<\/jats:p>","DOI":"10.3390\/s24196214","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T08:20:52Z","timestamp":1727338852000},"page":"6214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multimodal Bracelet to Acquire Muscular Activity and Gyroscopic Data to Study Sensor Fusion for Intent Detection"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0266-6637","authenticated-orcid":false,"given":"Daniel","family":"Andreas","sequence":"first","affiliation":[{"name":"Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2746-7050","authenticated-orcid":false,"given":"Zhongshi","family":"Hou","sequence":"additional","affiliation":[{"name":"Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9158-9419","authenticated-orcid":false,"given":"Mohamad Obada","family":"Tabak","sequence":"additional","affiliation":[{"name":"Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3262-6676","authenticated-orcid":false,"given":"Anany","family":"Dwivedi","sequence":"additional","affiliation":[{"name":"Artificial Intelligence (AI) Institute, Division of Health, Engineering, Computing and Science, University of Waikato, Hamilton 3216, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5703-6029","authenticated-orcid":false,"given":"Philipp","family":"Beckerle","sequence":"additional","affiliation":[{"name":"Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"},{"name":"Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1486","DOI":"10.1002\/wcs.1486","article-title":"Robotic interfaces for cognitive psychology and embodiment research: A research roadmap","volume":"10","author":"Beckerle","year":"2019","journal-title":"Wiley Interdiscip. 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