21 research outputs found
Discrete action control for prosthetic digits
We aim to develop a paradigm for simultaneous and independent control of multiple degrees of freedom (DOFs) for upper-limb prostheses. To that end, we introduce action control, a novel method to operate prosthetic digits with surface electromyography (EMG) based on multi-output, multi-class classification. At each time step, the decoder classifies movement intent for each controllable DOF into one of three categories: open, close, or stall (i.e., no movement). We implemented a real-time myoelectric control system using this method and evaluated it by running experiments with one unilateral and two bilateral amputees. Participants controlled a six-DOF bar interface on a computer display, with each DOF corresponding to a motor function available in multi-articulated prostheses. We show that action control can significantly and systematically outperform the state-of-the-art method of position control via multi-output regression in both task- and non-task-related measures. Using the action control paradigm, improvements in median task performance over regression-based control ranged from 20.14% to 62.32% for individual participants. Analysis of a post-experimental survey revealed that all participants rated action higher than position control in a series of qualitative questions and expressed an overall preference for the former. Action control has the potential to improve the dexterity of upper-limb prostheses. In comparison with regression-based systems, it only requires discrete instead of real-valued ground truth labels, typically collected with motion tracking systems. This feature makes the system both practical in a clinical setting and also suitable for bilateral amputation. This work is the first demonstration of myoelectric digit control in bilateral upper-limb amputees. Further investigation and pre-clinical evaluation are required to assess the translational potential of the method
Dimensionality reduction for EMG prediction of upper-limb activity in freely-behaving primates
Neural prosthetic systems aim to assist patients suffering from sensory, motor and
other disabilities by translating neural brain activity into control signals for assistive
devices, such as computers and robotics prostheses, or by restoring muscle contraction
through functional electrical stimulation (FES). In a neuro-motor prosthetic device, the
prediction of intended muscle activity is required for effective FES. It has been already
known that upper-limb electromyogram (EMG) signals in primates, can be accurately
predicted during repetitive tasks, by decoding the spiking-activity (SA) of single cells
and multi-unit activity (MUA) in the motor cortical areas. Recent work now suggests
that EMG signals can also be decoded by local field potential (LFP) recordings from
the same areas. In such decoding schemes, the number of input variables is usually
very large and no systematic way of performing effective variable selection has yet
been suggested. In this work, we demonstrated for the first time that muscle activity
decoding from SA and LFP signals in the primary motor cortex (M1) and the ventral
premotor cortex (PMv) areas is feasible during naturalistic free behaviour, and we
compared the decoding performance of spike-, LFP- and hybrid decoders. We also
tested the relative information in a number of LFP frequency bands, and found that
mid-high and high-frequency bands (70 - 244 Hz) conveyed the most EMG-related
information. Finally, we compared the decoding performance of a group of sparse
regression algorithms, and we showed that a method based on the variational bayes
(VB) outperformed the conventional Wiener cascade filter for LFP-decoders in the
case of limited amount of training data. For longer training datasets, the results from
all methods were comparable
Machine learning-based dexterous control of hand prostheses
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
CMOS Magnetic Sensors for Wearable Magnetomyography
Magnetomyography utilizes magnetic sensors to record small magnetic fields produced by the electrical activity of muscles, which also gives rise to the electromyogram (EMG) signal typically recorded with surface electrodes. Detection and recording of these small fields requires sensitive magnetic sensors possibly equipped with a CMOS readout system. This paper presents a highly sensitive Hall sensor fabricated in a standard 0.18 μm CMOS technology for future low-field MMG applications. Compared with previous works, our experimental results show that the proposed Hall sensor achieves a higher current mode sensitivity of approximately 2400 V/A/mT. Further refinement is required to enable measurement of MMG signals from muscles