Abstract
The hand of the human being is a very sophisticated and useful instrument, being essential for all types of tasks, from delicate manipulations and of high precision, to tasks that require a lot of force. For a long time researchers have been studying the biomechanics of the human hand, to reproduce it in robotic hands to be used as a prosthesis in humans, in the replacement of limbs lost or used in robots. In this study, we present the implementation (electronics project, acquisition, treatment, processing and control) of different sensors in the control of prostheses. The sensors studied and implemented are: inertial, electromyography (EMG), force and slip. The tests showed reasonable results due to sliding and dropping of some objects. These sensors will be used in a more complex system that will approach the fusion of sensors through Artificial Neural Networks (ANNs) and new tests should be performed for different scenarios.
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de Oliveira de Souza, J.O., dos Santos, J.V.C., de Figueiredo, R.M., Pessin, G. (2018). Real-Time Hand Prosthesis Biomimetic Movement Based on Electromyography Sensory Signals Treatment and Sensors Fusion. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_15
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DOI: https://doi.org/10.1007/978-3-030-01424-7_15
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