Myoelectric forearm prostheses: State of the art from a user-centered perspective

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity

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

EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition

Myoelectric control is an area of electromyography of increasing interest nowadays, particularly in applications such as Hand Gesture Recognition (HGR) for bionic prostheses. Today's focus is on pattern recognition using Machine Learning and, more recently, Deep Learning methods. Despite achieving good results on sparse sEMG signals, the latter models typically require large datasets and training times. Furthermore, due to the nature of stochastic sEMG signals, traditional models fail to generalize samples for atypical or noisy values. In this paper, we propose the design of a Vision Transformer (ViT) based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform Hand Gesture Recognition from surface electromyography (sEMG) signals. The proposed EMGTFNet architecture can accurately classify a variety of hand gestures without any need for data augmentation techniques, transfer learning or a significant increase in the number of parameters in the network. The accuracy of the proposed model is tested using the publicly available NinaPro database consisting of 49 different hand gestures. Experiments yield an average test accuracy of 83.57\% \& 3.5\% using a 200 ms window size and only 56,793 trainable parameters. Our results outperform the ViT without FNB, thus demonstrating that including FNB improves its performance. Our proposal framework EMGTFNet reported the significant potential for its practical application for prosthetic control