{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T08:44:20Z","timestamp":1774860260496,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,17]],"date-time":"2018-02-17T00:00:00Z","timestamp":1518825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671197"],"award-info":[{"award-number":["61671197"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Public Welfare Technology Research,China","award":["LGF18F010006"],"award-info":[{"award-number":["LGF18F010006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation applications as they reflect effectively the motor intentions of users. However, real-time sEMG signals are non-stationary and vary to a large extent within the time frame of signals. Although previous studies have focused on the issues, their results have not been satisfactory. Therefore, we present a new method of conducting feature-level fusion to obtain a new feature space for sEMG signals. Eight activities of daily life (ADLs), including falls, were performed to obtain raw data from EMG signals from the lower limb. A feature set combining the time domain, time\u2013frequency domain, and entropy domain was applied to the raw data to establish an initial feature space. A new projection method, the weighting genetic algorithm for GCCA (WGA-GCCA), was introduced to obtain the final feature space. Different tests were carried out to evaluate the performance of the new feature space. The new feature space created with the WGA-GCCA effectively reduced the dimensions and selected the best feature vectors dynamically while improving monotonicity. The Davies\u2013Bouldin index (DBI) based on fuzzy c-means algorithms of the space obtained the lowest value compared with several fusion methods. It also achieved the highest accuracy when applied to support vector machine classifier.<\/jats:p>","DOI":"10.3390\/s18020614","type":"journal-article","created":{"date-parts":[[2018,2,20]],"date-time":"2018-02-20T03:54:22Z","timestamp":1519098862000},"page":"614","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Feature-Level Fusion of Surface Electromyography for Activity Monitoring"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9213-6313","authenticated-orcid":false,"given":"Xugang","family":"Xi","sequence":"first","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7311-0794","authenticated-orcid":false,"given":"Minyan","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Zhizeng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.ins.2014.10.036","article-title":"A multipurpose smart activity monitoring system for personalized health services","volume":"314","author":"Park","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Leone, A., Rescio, G., Caroppo, A., and Siciliano, P. (2016). An EMG-based system for pre-impact fall detection. IEEE Sens., 1\u20134.","DOI":"10.1109\/ICSENS.2015.7370314"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MWC.2013.6590048","article-title":"Context awareness in WBANs: A survey on medical and non-medical applications","volume":"20","author":"Falk","year":"2013","journal-title":"IEEE Wirel. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1109\/TNSRE.2009.2036615","article-title":"A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke","volume":"17","author":"Roy","year":"2009","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.resp.2011.03.011","article-title":"Chronic assessment of diaphragm muscle EMG activity across motor behaviors","volume":"177","author":"Mantilla","year":"2011","journal-title":"Respir. Physiol. Neurobiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.ergon.2013.10.010","article-title":"Effects of shoulder rotation combined with elbow flexion on discomfort and EMG activity of ECRB muscle","volume":"44","author":"Farooq","year":"2014","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chiauzzi, E., Rodarte, C., and Dasmahapatra, P. (2015). Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med., 13.","DOI":"10.1186\/s12916-015-0319-2"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, K.C., and Chan, C.T. (2017). Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors. Sensors, 17.","DOI":"10.3390\/s17010187"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"835","DOI":"10.5665\/sleep.1886","article-title":"Normative EMG Values during REM Sleep for the Diagnosis of REM Sleep Behavior Disorder","volume":"35","author":"Frauscher","year":"2012","journal-title":"Sleep"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s11517-010-0629-7","article-title":"Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders","volume":"48","author":"Istenic","year":"2010","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Buchner, H., Petersen, E., Eger, M., and Rostalski, P. (2016). Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control. Eng. Med. Biol. Soc. IEEE.","DOI":"10.1109\/EMBC.2016.7591513"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1002\/mus.10008","article-title":"Intermuscle differences in activation","volume":"25","author":"Behm","year":"2002","journal-title":"Muscle Nerve"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.medengphy.2013.11.010","article-title":"Monitoring human health behaviour in one\u2019s living environment: A technological review","volume":"36","author":"Lowe","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_14","unstructured":"Kuula, A.S. (2011). Energy expenditure and muscle activity in active and passive commute among elderly."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Muhammed, H.H., and Jammalamadaka, R. (2016, January 5\u20136). A new approach for rehabilitation and upper-limb prosthesis control using optomyography (OMG). Proceedings of the International Conference on Biomedical Engineering, Yogyakarta, Indonesia.","DOI":"10.1109\/IBIOMED.2016.7869814"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Betthauser, J.L., Hunt, C.L., Osborn, L.E., Masters, M.R., Levay, G., Kaliki, R.R., and Thakor, N.V. (2017). Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations from Extreme Learning. IEEE Trans. Biomed. Eng.","DOI":"10.1109\/TBME.2017.2719400"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hazarika, A., Dutta, L., Barthakur, M., and Bhuyan, M. (2016, January 16\u201318). Fusion of projected feature for classification of EMG patterns. Proceedings of the International Conference on Accessibility to Digital World, Guwahati, India.","DOI":"10.1109\/ICADW.2016.7942515"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ishii, A., Kondo, T., and Yano, S. (2016). Improvement of EMG Pattern Recognition by Eliminating Posture-Dependent Components. International Conference on Intelligent Autonomous Systems, Springer.","DOI":"10.1007\/978-3-319-48036-7_2"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1016\/j.patcog.2004.12.013","article-title":"A new method of feature fusion and its application in image recognition","volume":"38","author":"Sun","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1016\/j.medengphy.2014.09.011","article-title":"Feature dimensionality reduction for myoelectric pattern recognition: A comparison study of feature selection and feature projection methods","volume":"36","author":"Liu","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/978-3-540-75867-9_132","article-title":"Processing of Myoelectric Signals by Feature Selection and Dimensionality Reduction for the Control of Powered Upper-Limb Prostheses","volume":"Volume 4739","author":"Pichler","year":"2007","journal-title":"Computer Aided Systems Theory\u2014EUROCAST 2007"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bose, R., Samanta, K., and Chatterjee, S. (2016, January 21\u201323). Cross-correlation based feature extraction from EMG signals for classification of neuro-muscular diseases. Proceedings of the International Conference on Intelligent Control Power and Instrumentation, Kolkata, India.","DOI":"10.1109\/ICICPI.2016.7859710"},{"key":"ref_23","first-page":"2179","article-title":"Protein Structure Prediction by Fusion, Bayesian Methods","volume":"23","author":"Akkaladevi","year":"2009","journal-title":"Encycl. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/TMECH.2007.897262","article-title":"A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control","volume":"12","author":"Chu","year":"2007","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1109\/TNSRE.2014.2304470","article-title":"Correlation Analysis of Electromyogram Signals for Multiuser Myoelectric Interfaces","volume":"22","author":"Khushaba","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/JSTSP.2008.2008265","article-title":"Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia","volume":"2","author":"Correa","year":"2008","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_27","first-page":"134","article-title":"Multi-scale Image Fusion Based on Invariant Moments","volume":"11","author":"Mehdi","year":"2017","journal-title":"International Research J. Appl. Basic Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1743-0003-7-21","article-title":"Study of stability of time-domain features for electromyographic pattern recognition","volume":"7","author":"Tkach","year":"2010","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xi, X., Tang, M., Miran, S.M., and Luo, Z. (2017). Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors. Sensors, 17.","DOI":"10.3390\/s17061229"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.cag.2014.01.006","article-title":"Visual analysis of dimensionality reduction quality for parameterized projections","volume":"41","author":"Martins","year":"2014","journal-title":"Comput. Graph."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1109\/TASE.2015.2477283","article-title":"Design of a Gait Phase Recognition System That Can Cope with EMG Electrode Location Variation","volume":"14","author":"Sang","year":"2017","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.ins.2016.06.038","article-title":"Anomalous Query Access Detection in RBAC-Administered Databases with Random Forest and PCA","volume":"369","author":"Ronao","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/BF02823145","article-title":"An introduction to genetic algorithms","volume":"24","author":"Deb","year":"1999","journal-title":"Sadhana"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A Cluster Separation Measure","volume":"1","author":"Davies","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.patcog.2003.06.005","article-title":"Validity index for crisp and fuzzy clusters","volume":"37","author":"Pakhira","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_36","first-page":"1","article-title":"Fuzzy c-means algorithm-a review","volume":"2","author":"Suganya","year":"2012","journal-title":"Int. J. Sci. Res. Publ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.isatra.2010.06.005","article-title":"Application of the PSO\u2013SVM model for recognition of control chart patterns","volume":"49","author":"Ranaee","year":"2010","journal-title":"ISA Trans."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.compbiomed.2013.01.020","article-title":"Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders","volume":"43","author":"Subasi","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2017). Feature Selection. Encyclopedia of Machine Learning and Data Mining, Springer.","DOI":"10.1007\/978-1-4899-7687-1"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/614\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:55:28Z","timestamp":1760194528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/614"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,17]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["s18020614"],"URL":"https:\/\/doi.org\/10.3390\/s18020614","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,17]]}}}