Vijayakumar, Sethu

Nazarpour, Kianoush

Krasoulis, Agamemnon

Engineering and Physical Sciences Research Council (EPSRC)

2018-06-19T12:15:43Z

2018-06-19T12:15:43Z

2018-07-02

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.

http://hdl.handle.net/1842/31213

en

The University of Edinburgh

A. Krasoulis, I. Kyranou, M. S. Erden, K. Nazarpour, and S. Vijayakumar (2017). “Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements.” In: Journal of NeuroEngineering and Rehabilitation 14.1, p. 71

A. Krasoulis, K. Nazarpour, and S. Vijayakumar (2017b). “Use of regularized discriminant analysis improves myoelectric hand movement classification.” In: 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 395– 398

I. Kyranou, A. Krasoulis, M. S. Erden, K. Nazarpour, and S. Vijayakumar (2016). “Real-time classification of multi-modal sensory data for prosthetic hand control.” In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, pp. 536–541

A. Krasoulis, K. Nazarpour, and S. Vijayakumar (2015). “Towards low-dimensionsal proportional myoelectric control.” In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 7155– 7158

A. Krasoulis, S. Vijayakumar, and K. Nazarpour (2015). “Evaluation of regression methods for the continuous decoding of finger movement from surface EMG and accelerometry.” In: 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, pp. 631–634

A. Krasoulis, K. Nazarpour, and S. Vijayakumar (2017a). “Real-time classification of five grip patterns with only two sensors.” In: Proceedings of the 2017 Myoelectric Controls Symposium (MEC), pp. 142–142

A. Krasoulis, S. Vijayakumar, and K. Nazarpour (2017). “Real-time proportional myoelectric control of digits.” In: Proceedings of the 2017 Myoelectric Controls Symposium (MEC), pp. 141–141

biomedical engineering

neural engineering

myoelectric control

upper-limb prostheses

machine learning

Machine learning-based dexterous control of hand prostheses

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy