Algorithms for robust human-machine interfacing via surface electromyography

Julkaisun otsikon käännös: Algorithms for robust human-machine interfacing via surface electromyography

Dennis Yeung

Tutkimustuotos: Doctoral ThesisCollection of Articles

Abstrakti

Surface electromyographic (sEMG) signals are an information-rich medium from which motor intention can be decoded. This property has led to their use as the primary interface between amputees and robotic prostheses for decades. Since the inception of such prosthetic systems, their mechatronics design and onboard processing power have advanced significantly. Similarly, the algorithms that facilitate the estimation of user commands have evolved from simple heuristics to data-driven models. However, the increased complexity of the interfacing algorithms has also incurred greater sensitivity to signal non-stationarities that can manifest during device operation and deteriorate control performance. Perspiration, fatigue, and electrode displacement are common sources of such signal perturbations and these issues extend to any wearable human-machine interface (HMI) that utilizes sEMG. To address this, adaptive control models that compensate for such signal perturbations and restore control performance have been proposed. Moreover, the emergence of surface decomposition algorithms that extract motor unit (MU) firing times from high-density sEMG recordings has also spurred the investigation of HMI driven by MU spike trains. This alternate means of extracting neural information from surface recordings offers certain advantages over traditional features derived from the global sEMG signal. Since MU spike times constitute the most basic bit of neural information responsible for force generation, more intuitive and dexterous interfacing may be derived. In this doctoral thesis comprising four journal articles and two conference articles, contributions in the domain of non-adaptive, supervised adaptive, and unsupervised adaptive control models are made. Furthermore, methods that advance the integration of MU decomposition techniques to HMI applications are proposed. Publication I proposes a directional forgetting extension to existing supervised adaptation schemes for improved co-adaptive stability. Publication II investigates the effects of imposing sparsity constraints to a muscle synergy-inspired control model in terms of robustness against electrode shifting. Publication III presents an unsupervised learning scheme to facilitate co-adaptive control using the muscle synergy-inspired model. Publication IV investigates the incorporation of feature selection methods to the initialization of MU-driven HMI. Publication V presents a semi-automatic method for generating benchmark decompositions referenced against intramuscular EMG recordings. Finally, Publication VI presents an adaptive algorithm for online decomposition capable of compensating for changes in joint angle and contraction intensities.
Julkaisun otsikon käännösAlgorithms for robust human-machine interfacing via surface electromyography
AlkuperäiskieliEnglanti
PätevyysTohtorintutkinto
Myöntävä instituutio
  • Aalto-yliopisto
Valvoja/neuvonantaja
  • Vujaklija, Ivan, Vastuuprofessori
  • Vujaklija, Ivan, Ohjaaja
Kustantaja
Painoksen ISBN978-952-64-2181-0
Sähköinen ISBN978-952-64-2182-7
TilaJulkaistu - 2024
OKM-julkaisutyyppiG5 Artikkeliväitöskirja

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