{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T04:25:07Z","timestamp":1729743907463,"version":"3.28.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031355950"},{"type":"electronic","value":"9783031355967"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-35596-7_36","type":"book-chapter","created":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T23:04:02Z","timestamp":1688857442000},"page":"563-572","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis and Considerations of the Controllability of EMG-Based Force Input"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0877-357X","authenticated-orcid":false,"given":"Hayato","family":"Nozaki","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6144-6477","authenticated-orcid":false,"given":"Yuta","family":"Kataoka","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Arzate Cruz","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6534-5742","authenticated-orcid":false,"given":"Fumihisa","family":"Shibata","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9859-9079","authenticated-orcid":false,"given":"Asako","family":"Kimura","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,9]]},"reference":[{"key":"36_CR1","first-page":"428","volume":"90","author":"S Beniczky","year":"2018","unstructured":"Beniczky, S., Conradsen, I., Henning, O., Fabricius, M., Wolf, P.: Automated real-time detection of tonic-clonic seizures using a wearable EMG device. J. Neurol. 90, 428\u2013434 (2018)","journal-title":"J. Neurol."},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Benalc\u00e1zar, M.E., Jaramillo, A.G., Jonathan, A.Z., P\u00e1ez, A., Andaluz, V.H.: Hand gesture recognition using machine learning and the Myo armband. In: Proceedings of European Signal Processing Conference (EUSIPCO), pp. 1040\u20131044 (2017)","DOI":"10.23919\/EUSIPCO.2017.8081366"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D.S., Morris, D., Balakrishnan, R., Turner, J., Landay, J.A.: Enabling always-available input with muscle-computer interfaces. In: Proceedings of ACM Symposium on User Interface Software and Technology (UIST), pp. 167\u2013176 (2009)","DOI":"10.1145\/1622176.1622208"},{"issue":"5","key":"36_CR4","doi-asserted-by":"publisher","first-page":"E67","DOI":"10.1111\/aor.13004","volume":"42","author":"P Xia","year":"2018","unstructured":"Xia, P., Hu, J., Peng, Y.: EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks: EMG-based estimation of limb movement. Artif. Organs 42(5), E67\u2013E77 (2018). https:\/\/doi.org\/10.1111\/aor.13004","journal-title":"Artif. Organs"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Hosoda, R., Venture, G.: Human joint motion estimation for electromyography (EMG)-based dynamic motion control. In: Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC), pp. 21\u201324 (2013)","DOI":"10.1109\/EMBC.2013.6609427"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Becker, V., Oldrati, P., Barrios, L., S\u00f6r\u00f6s, G.: Touchsense: classifying finger touches and measuring their force with an electromyography armband. In: Proceedings of ACM International Symposium on Wearable Computers (ISWC), pp. 1\u20138 (2018)","DOI":"10.1145\/3267242.3267250"},{"key":"36_CR7","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/B:JOCM.0000025279.27084.39","volume":"18","author":"B Hartmann","year":"2004","unstructured":"Hartmann, B., et al.: Computer keyboard and mouse as a reservoir of pathogens in an intensive care unit. J. Clin. Monitor. Comput. 18, 7\u201312 (2004)","journal-title":"J. Clin. Monitor. Comput."},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Greenberg, S., Fitchett, C.: Phidgets: easy development of physical interfaces through physical widgets. In: Proceedings of ACM symposium on User Interface Software and Technology (UIST), pp. 167\u2013176 (2001)","DOI":"10.1145\/502348.502388"},{"issue":"3","key":"36_CR9","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1109\/TMECH.2007.897262","volume":"12","author":"JU Chu","year":"2007","unstructured":"Chu, J.U., Moon, I., Lee, Y.J., Kim, S.K., Mun, M.S.: A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE\/ASME Trans. Mechatron. 12(3), 282\u2013290 (2007)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"issue":"1","key":"36_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1743-0003-11-122","volume":"11","author":"JG Ngeo","year":"2014","unstructured":"Ngeo, J.G., Tamei, T., Shibata, T.: Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. J. Neuroeng. Rehabil. 11(1), 1\u201314 (2014)","journal-title":"J. Neuroeng. Rehabil."},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Benko, H., Saponas, T.S., Morris, D., Tan, D.: Enhancing input on and above the interactive surface with muscle sensing. In: Proceedings of ACM Interactive Tabletops and Surfaces (ITS), pp. 93\u2013100 (2009)","DOI":"10.1145\/1731903.1731924"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Rosen, J., Brand, M., Fuchs, M., Arcan, M.: A myosignal-based powered exoskeleton system. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans. 31(3), 210\u2013222 (2001)","DOI":"10.1109\/3468.925661"},{"issue":"1","key":"36_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/1742-6596\/1450\/1\/012118","volume":"1450","author":"EPG Rakasena","year":"2020","unstructured":"Rakasena, E.P.G., Herdiman, L.: Electric wheelchair with forward-reverse control using Electromyography (EMG) control of arm muscle. J. Phys. Conf. Ser. 1450(1), 1\u20137 (2020)","journal-title":"J. Phys. Conf. Ser."},{"key":"36_CR14","doi-asserted-by":"publisher","first-page":"2713","DOI":"10.1109\/TSP.2020.2985299","volume":"68","author":"SA Raurale","year":"2020","unstructured":"Raurale, S.A., McAllister, J., del Rincon, J.M.: Real-time embedded EMG signal analysis for wrist-hand pose identification. IEEE Trans Signal Process. 68, 2713\u20132723 (2020)","journal-title":"IEEE Trans Signal Process."},{"key":"36_CR15","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1111\/j.1469-8986.1984.tb00249.x","volume":"21","author":"J Fridlund","year":"1984","unstructured":"Fridlund, J., Schwartz, G.E., Fowler, S.C.: Pattern recognition of self-reported emotional state from multiple-site facial EMG activity during affective imagery. J. Psychophysiol. 21, 622\u2013637 (1984)","journal-title":"J. Psychophysiol."},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Winter, D.A., Yack, H.J.: EMG profiles during normal human walking: stride-to-stride and inter-subject variability. J. Electroencephalogr. Clin. Neurophysiol. 402\u2013411 (1987)","DOI":"10.1016\/0013-4694(87)90003-4"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Yamagami, M., Steele, K.M., Burden, S.A.: Decoding intent with control theory: comparing muscle versus manual interface performance. In: Proceedings of CHI Conference on Human Factors in Computing Systems (CHI), pp. 1\u201312 (2020)","DOI":"10.1145\/3313831.3376224"},{"key":"36_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1743-0003-11-68","volume":"11","author":"J Lobo-Prat","year":"2014","unstructured":"Lobo-Prat, J., Keemink, A.Q., Stienen, A.H., Schouten, A.C., Veltink, P.H., Koopman, B.F.: Evaluation of EMG, force and joystick as control interfaces for active arm supports. J. Neuroeng. Rehabil. 11, 1\u201313 (2014)","journal-title":"J. Neuroeng. Rehabil."},{"key":"36_CR19","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1682\/JRRD.2010.03.0028","volume":"48","author":"EA Corbett","year":"2011","unstructured":"Corbett, E.A., Perreault, E.J., Kuiken, T.A.: Comparison of electromyography and force as interfaces for prosthetic control. J. Rehabil. Res. Dev. 48, 629\u2013641 (2011)","journal-title":"J. Rehabil. Res. Dev."},{"key":"36_CR20","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/S0166-4115(08)62386-9","volume":"52","author":"SG Hart","year":"1988","unstructured":"Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. J. Adv. Psychol. 52, 139\u2013183 (1988)","journal-title":"J. Adv. Psychol."},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Ishikawa, K., Akita, J., Toda, M., Kondo, K., Sakurazawa, S., Nakamura, Y.: Robust finger motion classification using frequency characteristics of surface electromyogram signals. In: Proceedings of International Conference on Biomedical Engineering (ICoBE), pp. 362\u2013367 (2012)","DOI":"10.1109\/ICoBE.2012.6179039"},{"key":"36_CR22","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/BF00864227","volume":"67","author":"C Jensen","year":"1993","unstructured":"Jensen, C., Vasseljen, O., Westgaard, R.H.: The influence of electrode position on bipolar surface electromyogram recordings of the upper trapezius muscle. J. Appl. Physiol. Occup. Physiol. 67, 266\u2013273 (1993)","journal-title":"J. Appl. Physiol. Occup. Physiol."},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Ozdemir, M.A., Kisa, D.H., Guren, O., Onan, A., Akan, A.: EMG based hand gesture recognition using deep learning. In: Proceedings of Medical Technologies Congress, pp. 1\u20134 (2020)","DOI":"10.1109\/TIPTEKNO50054.2020.9299264"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D.S., Morris, D., Turner, J., Landay, J.A.: Making muscle-computer interfaces more practical. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 851\u2013854 (2010)","DOI":"10.1145\/1753326.1753451"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Rautaray, S.S., Agrawal, A.: A novel human computer interface based on hand gesture recognition using computer vision techniques. In: Proceedings of Intelligent Interactive Technologies and Multimedia (IITM), pp. 292\u2013296 (2010)","DOI":"10.1145\/1963564.1963615"},{"key":"36_CR26","unstructured":"Byers, J.C., Bittner, A., Hill, S.: Traditional and raw Task Load Index (TLX) correlations: are paired comparisons necessary? Advances in Industrial Ergonomics and Safety l, pp. 481\u2013485. Taylor and Francis (1989)"}],"container-title":["Lecture Notes in Computer Science","Human-Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35596-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T21:27:39Z","timestamp":1729718859000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35596-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031355950","9783031355967"],"references-count":26,"URL":"http:\/\/dx.doi.org\/10.1007\/978-3-031-35596-7_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Copenhagen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7472","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1578","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"396","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}
