Multiclass Detection and Tracking of Transient Motor Activation based on Decomposed Myoelectric Signals

Martyna Stachaczyk, Seyed Farokh Atashzar, Sigrid Dupan, Ivan Vujaklija, Dario Farina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Performance and efficacy of neuroprosthetic devices depend critically on the ability to detect the users motor intent with high temporal resolution. Delayed and incorrect responses significantly reduce usability, controllability and intuitiveness of prosthetic systems. Substantial efforts have been conducted to detect the steady-state phase of motor intention. However, detection, classification, and tracking of transient phases for one complete muscle contraction is still not possible. Clinically-established control systems rely mainly on surface electromyography (sEMG) signals in stationary, steady-state contractions, that have limited temporal resolution. Characterization of neural activities during different stages of a dynamic, transient contraction would allow for the development of a clinically-viable myoelectric system with a high temporal resolution that can significantly enhance the level of intuitiveness and usability of prosthetic devices. This could increase the response bandwidth and realize natural and dexterous control while avoiding exaggerated compensatory movements. For this purpose, in this paper, we explore the use of motor unit action potential trains (MUAPTs) for designing a movement intention detection technique. The goal is to classify and track the transient phases of muscle activation. Data collected from three subjects, during flexion tasks with four individual digits, is used to evaluate the algorithm. The performance is compared with that of the standard sEMG-based approach. Results showed a substantial advantage of the MUAPT-based phase detection algorithm over the conventional sEMG-based technique. It is confirmed that decoding, classification, and tracking of all stages of a dynamic, transient contraction is feasible using the proposed MUAPT-based approach, as a robust and efficient alternative for conventional sEMG-based algorithms.

Original languageEnglish (US)
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages1080-1083
Number of pages4
ISBN (Electronic)9781538679210
DOIs
StatePublished - May 16 2019
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: Mar 20 2019Mar 23 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
CountryUnited States
CitySan Francisco
Period3/20/193/23/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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  • Cite this

    Stachaczyk, M., Atashzar, S. F., Dupan, S., Vujaklija, I., & Farina, D. (2019). Multiclass Detection and Tracking of Transient Motor Activation based on Decomposed Myoelectric Signals. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 (pp. 1080-1083). [8717077] (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March). IEEE Computer Society. https://doi.org/10.1109/NER.2019.8717077