Bionic hand: A brief review

The hand is one of the most crucial organs in the human body. Hand loss causes the loss of functionality in daily and work life and psychological disorders for the patients. Hand transplantation is best option to gain most of the hand function. However, the applicability of this option is limited since the side effects and the need for tissue compatibility. Electromechanical hand prosthesis also called bionic hand is an alternative option to hand transplantation. This study presents a quick review of bionic hand technology

End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands

Speech is one of the most common forms of communication in humans. Speech commands are essential parts of multimodal controlling of prosthetic hands. In the past decades, researchers used automatic speech recognition systems for controlling prosthetic hands by using speech commands. Automatic speech recognition systems learn how to map human speech to text. Then, they used natural language processing or a look-up table to map the estimated text to a trajectory. However, the performance of conventional speech-controlled prosthetic hands is still unsatisfactory. Recent advancements in general-purpose graphics processing units (GPGPUs) enable intelligent devices to run deep neural networks in real-time. Thus, architectures of intelligent systems have rapidly transformed from the paradigm of composite subsystems optimization to the paradigm of end-to-end optimization. In this paper, we propose an end-to-end convolutional neural network (CNN) that maps speech 2D features directly to trajectories for prosthetic hands. The proposed convolutional neural network is lightweight, and thus it runs in real-time in an embedded GPGPU. The proposed method can use any type of speech 2D feature that has local correlations in each dimension such as spectrogram, MFCC, or PNCC. We omit the speech to text step in controlling the prosthetic hand in this paper. The network is written in Python with Keras library that has a TensorFlow backend. We optimized the CNN for NVIDIA Jetson TX2 developer kit. Our experiment on this CNN demonstrates a root-mean-square error of 0.119 and 20ms running time to produce trajectory outputs corresponding to the voice input data. To achieve a lower error in real-time, we can optimize a similar CNN for a more powerful embedded GPGPU such as NVIDIA AGX Xavier

Diseño y control de un dedo robot subactuado mediante técnicas de control de ganancias autosintonizables

"Las destrezas de agarre y manipulación de la mano humana, son tareas básicas que empleamos día a día a lo largo de nuestras vidas. Por ser la mano, una de las partes más importantes y versátiles del cuerpo humano para interactuar con el medio circundante, desde hace décadas ha sido utilizada como fuente de inspiración para desarrollar modelos funcionales y adaptivos, que conciernen en teoría de control, hasta la implementación robótica y mecatrónica. Por ello, el antropomorfismo es la meta que se pretende alcanzar en el diseño de una mano robótica. La línea de investigación que se sigue en el presente documento, propicia el desarrollo de sistemas robóticos con elementos subactuados y pretende aportar innovación en cuestiones de diseño estructural y desempeño de movimiento, por medio de subsistemas asociados con complejos articulares que transmiten las fuerzas que requiere el robot para desplazarse adecuadamente. Asimismo, se propone la incorporación de un sistema de control de posición con ganancias autosintonizables, como solución ha aspectos de eficiencia y calibración de la mano robótica"