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Article

Myoelectric and Inertial Data Fusion Through a Novel Attention-Based Spatiotemporal Feature Extraction for Transhumeral Prosthetic Control: An Offline Analysis

by
Andrea Tigrini
1,*,
Alessandro Mengarelli
1,
Ali H. Al-Timemy
2,
Rami N. Khushaba
3,
Rami Mobarak
1,
Mara Scattolini
1,
Gaith K. Sharba
4,
Federica Verdini
5,
Ennio Gambi
1 and
Laura Burattini
1
1
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
2
Biomedical Engineering Department, Al- Khwarizmi College of Engineering, University of Baghdad, Baghdad 10071, Iraq
3
Transport for NSW Alexandria, Haymarket, NSW 2008, Australia
4
College of Dentistry, Ibn Sina University of Medical and Pharmaceutical Sciences, Baghdad 10018, Iraq
5
Department of Theoretical and Applied Science, Università eCampus, 22060 Como, Italy
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(18), 5920; https://doi.org/10.3390/s25185920
Submission received: 17 July 2025 / Revised: 1 September 2025 / Accepted: 20 September 2025 / Published: 22 September 2025
(This article belongs to the Special Issue Advanced Sensors and AI Integration for Human–Robot Teaming)
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Abstract

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.
Keywords: myoelectric control; pattern recognition; shoulder joint; spatiotemporal feature extraction; transhumeral amputees myoelectric control; pattern recognition; shoulder joint; spatiotemporal feature extraction; transhumeral amputees

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MDPI and ACS Style

Tigrini, A.; Mengarelli, A.; Al-Timemy, A.H.; Khushaba, R.N.; Mobarak, R.; Scattolini, M.; Sharba, G.K.; Verdini, F.; Gambi, E.; Burattini, L. Myoelectric and Inertial Data Fusion Through a Novel Attention-Based Spatiotemporal Feature Extraction for Transhumeral Prosthetic Control: An Offline Analysis. Sensors 2025, 25, 5920. https://doi.org/10.3390/s25185920

AMA Style

Tigrini A, Mengarelli A, Al-Timemy AH, Khushaba RN, Mobarak R, Scattolini M, Sharba GK, Verdini F, Gambi E, Burattini L. Myoelectric and Inertial Data Fusion Through a Novel Attention-Based Spatiotemporal Feature Extraction for Transhumeral Prosthetic Control: An Offline Analysis. Sensors. 2025; 25(18):5920. https://doi.org/10.3390/s25185920

Chicago/Turabian Style

Tigrini, Andrea, Alessandro Mengarelli, Ali H. Al-Timemy, Rami N. Khushaba, Rami Mobarak, Mara Scattolini, Gaith K. Sharba, Federica Verdini, Ennio Gambi, and Laura Burattini. 2025. "Myoelectric and Inertial Data Fusion Through a Novel Attention-Based Spatiotemporal Feature Extraction for Transhumeral Prosthetic Control: An Offline Analysis" Sensors 25, no. 18: 5920. https://doi.org/10.3390/s25185920

APA Style

Tigrini, A., Mengarelli, A., Al-Timemy, A. H., Khushaba, R. N., Mobarak, R., Scattolini, M., Sharba, G. K., Verdini, F., Gambi, E., & Burattini, L. (2025). Myoelectric and Inertial Data Fusion Through a Novel Attention-Based Spatiotemporal Feature Extraction for Transhumeral Prosthetic Control: An Offline Analysis. Sensors, 25(18), 5920. https://doi.org/10.3390/s25185920

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