Upper-limb myoelectric prostheses are controlled by muscle activity information
recorded on the skin surface using electromyography (EMG). Intuitive prosthetic control
can be achieved by deploying statistical and machine learning (ML) tools to decipher
the user’s movement intent from EMG signals. This thesis proposes various
means of advancing the capabilities of non-invasive, ML-based control of myoelectric
hand prostheses. Two main directions are explored, namely classification-based
hand grip selection and proportional finger position control using regression methods.
Several practical aspects are considered with the aim of maximising the clinical
impact of the proposed methodologies, which are evaluated with offline analyses as
well as real-time experiments involving both able-bodied and transradial amputee
participants.
It has been generally accepted that the EMG signal may not always be a reliable
source of control information for prostheses, mainly due to its stochastic and non-stationary
properties. One particular issue associated with the use of surface EMG
signals for upper-extremity myoelectric control is the limb position effect, which is
related to the lack of decoding generalisation under novel arm postures. To address
this challenge, it is proposed to make concurrent use of EMG sensors and inertial
measurement units (IMUs). It is demonstrated this can lead to a significant improvement
in both classification accuracy (CA) and real-time prosthetic control performance.
Additionally, the relationship between surface EMG and inertial measurements is investigated
and it is found that these modalities are partially related due to reflecting
different manifestations of the same underlying phenomenon, that is, the muscular
activity.
In the field of upper-limb myoelectric control, the linear discriminant analysis (LDA)
classifier has arguably been the most popular choice for movement intent decoding.
This is mainly attributable to its ease of implementation, low computational requirements,
and acceptable decoding performance. Nevertheless, this particular method
makes a strong fundamental assumption, that is, data observations from different
classes share a common covariance structure. Although this assumption may often
be violated in practice, it has been found that the performance of the method is
comparable to that of more sophisticated algorithms. In this thesis, it is proposed to
remove this assumption by making use of general class-conditional Gaussian models
and appropriate regularisation to avoid overfitting issues. By performing an exhaustive analysis on benchmark datasets, it is demonstrated that the proposed approach
based on regularised discriminant analysis (RDA) can offer an impressive increase in decoding
accuracy. By combining the use of RDA classification with a novel confidence-based
rejection policy that intends to minimise the rate of unintended hand motions,
it is shown that it is feasible to attain robust myoelectric grip control of a prosthetic
hand by making use of a single pair of surface EMG-IMU sensors.
Most present-day commercial prosthetic hands offer the mechanical abilities to
support individual digit control; however, classification-based methods can only produce
pre-defined grip patterns, a feature which results in prosthesis under-actuation.
Although classification-based grip control can provide a great advantage over conventional
strategies, it is far from being intuitive and natural to the user. A potential
way of approaching the level of dexterity enjoyed by the human hand is via continuous
and individual control of multiple joints. To this end, an exhaustive analysis
is performed on the feasibility of reconstructing multidimensional hand joint angles
from surface EMG signals. A supervised method based on the eigenvalue formulation
of multiple linear regression (MLR) is then proposed to simultaneously reduce the
dimensionality of input and output variables and its performance is compared to that
of typically used unsupervised methods, which may produce suboptimal results in
this context. An experimental paradigm is finally designed to evaluate the efficacy of
the proposed finger position control scheme during real-time prosthesis use.
This thesis provides insight into the capacity of deploying a range of computational
methods for non-invasive myoelectric control. It contributes towards developing
intuitive interfaces for dexterous control of multi-articulated prosthetic hands by
transradial amputees
