The low-cost manufacturing and maintenance of prostheses is of vital importance to their successful deployment in developing countries. Low-cost prosthesis actuation is generally achieved by combining pre-programmed control strategies, with surface-electromyographic measurements taken from the residual limb. In a standard setting, these signals are measured with disposable gel electrodes. However, this limit on electrode reuse requires that prosthesis users have a stable supply of electrodes. Alternatively, the textile electrodes sewn from conductive thread are studied in the context of hand gesture recognition to consider their future use with low-cost prostheses. In this paper, it is demonstrated that textile electrodes can be applied for gesture recognition. To do so, surface electromyography (sEMG) experiments are run in South Africa on three amputees where they were asked to perform gestures with their phantom limb (i.e., the missing limb segment). A gesture recognition method is implemented, and the classification accuracy with data recorded from textile electrodes is compared to that from gel electrodes. Further analysis examining the relationship between classifier performance and physiological parameters are performed. Results show that textile electrodes can be used to perform accurate gesture recognition, and are comparable to disposable gel electrodes. This demonstrates that low-cost sensory systems are not barrier to myoelectric control in developing countries.

Cayley graph is used in representing the complex augmentation of autonomie EEG neural network with bipartite, trivalent and Erdos-Renyi models. The augmentation was used in determining an efficient communication, data and information transmission in EEG neural network. The geometric properties of EEG neural network augmented in autonomie Cayley neural network is used in the processing and transmission of EEG data. The correlation between directed communication path and optimum information transfer path ensured that EEG data and information were transmitted effortlessly to the end effector and end user. EEG network centrality revealed the geometric property of the neural network. The paper proposed the use of Cayley diagrams and graphs in the representation of autonomie EEG neural networks.