{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:09:31Z","timestamp":1787029771737,"version":"build-2736575974"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study proposes a feature extraction scheme that fuses accelerometric (ACC) and electromyographic (EMG) data to improve shoulder movement identification in individuals with transhumeral amputation, in whom the clinical need for intuitive control strategies enabling reliable activation of full-arm prostheses is underinvestigated. A novel spatiotemporal warping feature extraction architecture was employed to realize EMG and ACC information fusion at the feature level. EMG and ACC data were collected from six participants with intact limbs and four participants with transhumeral amputation using an NI USB-6009 device at 1000 Hz to support the proposed feature extraction scheme. For each participant, a leave-one-trial-out (LOTO) training and testing approach was used for developing pattern recognition models for both the intact-limb (IL) and amputee (AMP) groups. The analysis revealed that the introduction of ACC information has a positive impact when using windows of length (WLs) lower than 150 ms. A linear discriminant analysis (LDA) classifier was able to exceed the accuracy of 90% in each WL condition and for each group. Similar results were observed for an extreme learning machine (ELM), whereas k-nearest neighbors (kNN) and an autonomous learning multi-model classifier showed a mean accuracy of less than 87% for both IL and AMP groups at different WLs, guaranteeing applicability over a large set of shallow pattern-recognition models that can be used in real scenarios. The present work lays the groundwork for future studies involving real-time validation of the proposed methodology on a larger population, acknowledging the current limitation of offline analysis.<\/jats:p>","DOI":"10.3390\/s25185920","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T13:04:44Z","timestamp":1758546284000},"page":"5920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Myoelectric and Inertial Data Fusion Through a Novel Attention-Based Spatiotemporal Feature Extraction for Transhumeral Prosthetic Control: An Offline Analysis"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1600-2137","authenticated-orcid":false,"given":"Andrea","family":"Tigrini","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6087-6763","authenticated-orcid":false,"given":"Alessandro","family":"Mengarelli","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2738-8896","authenticated-orcid":false,"given":"Ali H.","family":"Al-Timemy","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, Al- Khwarizmi College of Engineering, University of Baghdad, Baghdad 10071, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8528-8979","authenticated-orcid":false,"given":"Rami N.","family":"Khushaba","sequence":"additional","affiliation":[{"name":"Transport for NSW Alexandria, Haymarket, NSW 2008, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3929-084X","authenticated-orcid":false,"given":"Rami","family":"Mobarak","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2060-6942","authenticated-orcid":false,"given":"Mara","family":"Scattolini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4003-2619","authenticated-orcid":false,"given":"Gaith K.","family":"Sharba","sequence":"additional","affiliation":[{"name":"College of Dentistry, Ibn Sina University of Medical and Pharmaceutical Sciences, Baghdad 10018, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4252-3224","authenticated-orcid":false,"given":"Federica","family":"Verdini","sequence":"additional","affiliation":[{"name":"Department of Theoretical and Applied Science, Universit\u00e0 eCampus, 22060 Como, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6852-8483","authenticated-orcid":false,"given":"Ennio","family":"Gambi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9474-7046","authenticated-orcid":false,"given":"Laura","family":"Burattini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1038\/s41551-021-00732-x","article-title":"Toward higher-performance bionic limbs for wider clinical use","volume":"7","author":"Farina","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1049\/cit2.12058","article-title":"Shoulder girdle recognition using electrophysiological and low frequency anatomical contraction signals for prosthesis control","volume":"7","author":"Nsugbe","year":"2022","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1109\/TNSRE.2022.3159186","article-title":"Sensory feedback for upper-limb prostheses: Opportunities and barriers","volume":"30","author":"Jabban","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cordella, F., Ciancio, A.L., Sacchetti, R., Davalli, A., Cutti, A.G., Guglielmelli, E., and Zollo, L. (2016). Literature review on needs of upper limb prosthesis users. Front. Neurosci., 10.","DOI":"10.3389\/fnins.2016.00209"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TNSRE.2011.2108667","article-title":"Online myoelectric control of a dexterous hand prosthesis by transradial amputees","volume":"19","author":"Cipriani","year":"2011","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1109\/TNSRE.2011.2178039","article-title":"Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom","volume":"20","author":"Muceli","year":"2011","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Moradi, A., Rafiei, H., Daliri, M., Akbarzadeh-T, M.R., Akbarzadeh, A., Naddaf-Sh, A.M., and Naddaf-Sh, S. (2022). Clinical implementation of a bionic hand controlled with kineticomyographic signals. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-19128-1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.22153\/kej.2020.05.001","article-title":"Analysis of Different Hand and Finger Grip Patterns using Surface Electromyography and Hand Dynamometry","volume":"16","author":"Buniya","year":"2020","journal-title":"Al-Khwarizmi Eng. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107526","DOI":"10.1109\/ACCESS.2022.3212146","article-title":"Hand gesture recognition with acoustic myography and wavelet scattering transform","volume":"10","author":"Serrestou","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1177\/0309364619836353","article-title":"Fabrication and application of an adjustable myoelectric transhumeral prosthetic socket","volume":"43","author":"Schofield","year":"2019","journal-title":"Prosthetics Orthot. Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1109\/TMECH.2016.2552999","article-title":"Design of a myoelectric transhumeral prosthesis","volume":"21","author":"Bennett","year":"2016","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2974","DOI":"10.1152\/jn.00178.2007","article-title":"Decoding a new neural\u2013machine interface for control of artificial limbs","volume":"98","author":"Zhou","year":"2007","journal-title":"J. Neurophysiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hargrove, L.J., Miller, L.A., Turner, K., and Kuiken, T.A. (2017). Myoelectric pattern recognition outperforms direct control for transhumeral amputees with targeted muscle reinnervation: A randomized clinical trial. Sci. Rep., 7.","DOI":"10.1038\/s41598-017-14386-w"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/TNSRE.2022.3186266","article-title":"Simultaneous control of 2DOF upper-limb prosthesis with body compensations-based control: A multiple cases study","volume":"30","author":"Legrand","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TMRB.2020.3048251","article-title":"Closing the loop between body compensations and upper limb prosthetic movements: A feasibility study","volume":"3","author":"Legrand","year":"2020","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tigrini, A., Al-Timemy, A.H., Verdini, F., Fioretti, S., Morettini, M., Burattini, L., and Mengarelli, A. (2023). Decoding transient sEMG data for intent motion recognition in transhumeral amputees. Biomed. Signal Process. Control, 85.","DOI":"10.1016\/j.bspc.2023.104936"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/1743-0003-9-74","article-title":"Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees","volume":"9","author":"Geng","year":"2012","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1109\/TNSRE.2019.2955029","article-title":"Determining user intent of partly dynamic shoulder tasks in individuals with chronic stroke using pattern recognition","volume":"28","author":"Kopke","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_19","unstructured":"Campbell, E., Phinyomark, A., and Scheme, E. (2020). Differences in perspective on inertial measurement unit sensor integration in myoelectric control. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1109\/THMS.2022.3146053","article-title":"Myoelectric Control With Fixed Convolution-Based Time-Domain Feature Extraction: Exploring the Spatio\u2013Temporal Interaction","volume":"52","author":"Khushaba","year":"2022","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"066028","DOI":"10.1088\/1741-2552\/ac387f","article-title":"Spatio-temporal warping for myoelectric control: An offline, feasibility study","volume":"18","author":"Jabbari","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"114977","DOI":"10.1016\/j.eswa.2021.114977","article-title":"A long short-term recurrent spatial-temporal fusion for myoelectric pattern recognition","volume":"178","author":"Khushaba","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tigrini, A., Scattolini, M., Mengarelli, A., Fioretti, S., Morettini, M., Burattini, L., and Verdini, F. (2022, January 22\u201324). Role of the Window Length for Myoelectric Pattern Recognition in Detecting User Intent of Motion. Proceedings of the 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Messina, Italy.","DOI":"10.1109\/MeMeA54994.2022.9856454"},{"key":"ref_24","first-page":"609","article-title":"Wavelet-based feature extraction technique for classification of different shoulder girdle motions for high-level upper limb amputees","volume":"12","author":"Sharba","year":"2020","journal-title":"Int. J. Med. Eng. Inform."},{"key":"ref_25","unstructured":"Criswell, E. (2010). Cram\u2019s Introduction to Surface Electromyography, Jones & Bartlett Publishers."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s12984-019-0512-1","article-title":"Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks","volume":"16","author":"Trigili","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.1109\/JSEN.2018.2813434","article-title":"Analysis and comparison of features and algorithms to classify shoulder movements from sEMG signals","volume":"18","author":"Rivela","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Khushaba, R.N., Al-Ani, A., Al-Timemy, A., and Al-Jumaily, A. (2016, January 6\u20139). A fusion of time-domain descriptors for improved myoelectric hand control. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.","DOI":"10.1109\/SSCI.2016.7850064"},{"key":"ref_29","first-page":"650","article-title":"Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees","volume":"24","author":"Khushaba","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_30","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TNSRE.2020.3029873","article-title":"Exploring the relationship between EMG feature space characteristics and control performance in machine learning myoelectric control","volume":"29","author":"Franzke","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neunet.2014.03.010","article-title":"Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features","volume":"55","author":"Khushaba","year":"2014","journal-title":"Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Angelov, P., and Gu, X. (June, January 31). Autonomous learning multi-model classifier of 0-order (ALMMo-0). Proceedings of the 2017 Evolving and Adaptive Intelligent Systems (EAIS), Ljubljana, Slovenia.","DOI":"10.1109\/EAIS.2017.7954832"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Angelov, P.P., and Gu, X. (2019). Empirical Approach to Machine Learning, Springer.","DOI":"10.1007\/978-3-030-02384-3"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6001904","DOI":"10.1109\/LSENS.2021.3100607","article-title":"Shoulder motion intention detection through myoelectric pattern recognition","volume":"5","author":"Tigrini","year":"2021","journal-title":"IEEE Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1007\/s13042-022-01687-4","article-title":"Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals","volume":"14","author":"Fang","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8569","DOI":"10.1109\/LRA.2021.3111850","article-title":"Decoding HD-EMG Signals for Myoelectric Control-How Small Can the Analysis Window Size be?","volume":"6","author":"Khushaba","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cai, D., He, X., and Han, J. (2007, January 28\u201331). Efficient kernel discriminant analysis via spectral regression. Proceedings of the Seventh IEEE International Conference on Data Mining (ICDM 2007), Omaha, NE, USA.","DOI":"10.1109\/ICDM.2007.88"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TNSRE.2010.2100828","article-title":"Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay","volume":"19","author":"Smith","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5629","DOI":"10.1109\/TIM.2019.2962295","article-title":"Single IMU displacement and orientation estimation of human center of mass: A magnetometer-free approach","volume":"69","author":"Cardarelli","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cardarelli, S., di Florio, P., Mengarelli, A., Tigrini, A., Fioretti, S., and Verdini, F. (2019, January 19\u201321). Magnetometer-free sensor fusion applied to pedestrian tracking: A feasibility study. Proceedings of the 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), Ancona, Italy.","DOI":"10.1109\/ISCE.2019.8901014"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chadwell, A., Kenney, L., Thies, S., Head, J., Galpin, A., and Baker, R. (2021). Addressing unpredictability may be the key to improving performance with current clinically prescribed myoelectric prostheses. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-82764-6"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"016029","DOI":"10.1088\/1741-2552\/adada0","article-title":"Plug-and-play myoelectric control via a self-calibrating random forest common model","volume":"22","author":"Jiang","year":"2025","journal-title":"J. Neural Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kulwa, F., Tai, P., Sarwatt, D.S., Asogbon, M.G., Khushaba, R., Oyemakinde, T.T., Aboyeji, S.T., Li, G., Samuel, O.W., and Li, Y. (2025). A NMF-based non-Euclidean Adaptive Feature Extraction Scheme for Limb Motion Pattern Decoding in Pattern Recognition System. IEEE Trans. Biomed. Eng.","DOI":"10.1109\/TBME.2025.3592183"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5920\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:47:06Z","timestamp":1760035626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,22]]},"references-count":44,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25185920"],"URL":"https:\/\/doi.org\/10.3390\/s25185920","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,22]]}}}