On the Decoding of Shoulder Joint Intent of Motion From Transient EMG: Feature Evaluation and Classification
Open accessAbstract
Motion intent detection for shoulder actions may allow the early decoding of upper limb motions, thus enhancing the real-time usability of rehabilitative devices and prosthetics. In this study we faced a motion intent detection problem involving four shoulder movements by using transient epochs of surface electromyographic (EMG) signals. Reliability of time and frequency domain features was investigated through clusters separability properties and classification performances. Those features able to provide accuracy greater than 90% were selected and further investigated by a holdout scheme, i.e. decreasing the amount of data for training the learning models (60%, 50%, 40%, and 30%). Key findings of the study are as follows. Firstly, single-feature approach appeared suitable for early decoding shoulder movements, thus supporting reduced recording setup. Time domain features related to the instantaneous variations of signal amplitude produced the best results but frequency domain features showed comparable performances, suggesting no favored domain for feature extraction. Eventually, autoregressive coefficients suffered from a reduced amount of data used for training. Outcomes of this study can support the design of myoelectric control schemes, based on transient EMG data, for driving shoulder joint assistive devices.
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Logistic Regression is a linear probabilistic model outputting class probabilities, has strong interpretability and probabilistic decision support, and is used for binary motion intent prediction.
“| Logistic Regression | Linear probabilistic model, outputting class | Strong interpretability, probabilistic | Binary motion intent prediction 265,266 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [265]
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Research works printed but not linked: 1
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Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
- Authors
- Tigrini, A., Verdini, F., Fioretti, S., & Mengarelli, A.
- Affiliations
- Marche Polytechnic University (IT)
- Venue
- IEEE Transactions on Medical Robotics and Bionics
- Published
- 2023-11
- Volume
- 5
- Issue
- 4
- Pages
- 1037-1044
- ISSN
- 2576-3202 (ISSN-L)
- OpenAlex
- W4387129509
- Cited by
- 27
- References
- 39
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 14
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Medical Robotics and Bionics (publishedVersion, cc-by)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, cc-by)
- IRIS eCampus Telematic University (Università degli Studi eCampus) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- surface electromyography, myoelectric control, feature selection, time-domain features, frequency-domain features, autoregressive coefficients, classification accuracy, limited training data, assistive devices, upper limb rehabilitation, prosthetics
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