# Recent Advances in Myoelectric Control for Finger Prostheses for Multiple Finger Loss
## 3 Signals and Finger Motor Function
[27] investigated the accuracy of individual and combined finger movements using surface EMG signals using Bayesian data fusion in postprocessing for the proper classification of EMG data belonging to different movements. Similarly, [PERSON] et al. [28] purposed a feedback interface for a myoelectric prosthesis with multiple degrees of freedom that allows proprioceptive and sensory information (i.e., the grasping force). In this method, nine subjects performed a sequential algorithm in a time comparable to that reported when performed by experts and found that amplitudes can be significantly streamlined to a quarter of initial time without reducing the outcome when using prior knowledge in setting the baseline stimulation. [PERSON] et al. [20] studied the natural control of robotic hands via sEMG using healthy subjects and amputees. A neural network was used to evaluate the effects of the pre-processing, layer architecture, data augmentation, and optimization. The results show that convolutional neural networks with simple architecture can produce precise results comparable to the average classical classification methods. They also show that several factors (pre-processing, net architecture, and parameters for optimization) are important for the analysis of sEMG data. More accuracy for computer vision and object recognition tasks is seen with larger networks. Recently, multimodal data acquisition has been applied for the EMG-based detection of hand/finger movement [20; 29; 30; 31]. Accelerometers can be combined with EMG electrodes to produce excellent capabilities to recognize hand movements [32]. Similarly, convolutional neural networks and deep learning have revolutionized machine learning, computer vision, and speech recognition. Deep learning is used in deep neural network applications and reinforcement learning to maximize the performance of a machine or software agent by itself making errors [33]. The neural networks have been applied to EMG hand movement recognition. The highest accuracy can be seen when combined convolutional neural networks with the retrained network. Table 1 shows the summary of developments in myoelectric finger prostheses and research outcomes. Furthermore, EMG-driven prosthesis performance is influenced by various factors, such as electrode position shifting, varying muscle contraction levels, forearm orientation, and limb position [16]. In addition, [PERSON] et al. [16] investigated the combined effect of forearm orientation and muscle contraction levels on the generalizability of EMG pattern recognition. They first verified whether the subjects could produce EMG activity in all different classes of movement at three muscular contraction levels (low, medium, and high) by analyzing the RMS of EMG activity across all channels, orientations, and movements. Figure 3 shows the representative radar plot of the EMG signals RMS values collected from one subject performing wrist flexion (C4) when the wrist was fully pronated. The RMS values from the different channels were then grouped in separate columns for each force level, thereby constructing an RMS matrix. They found a significant difference (\(p\) = 0.003) between the means of the different force levels. They also mentioned that multiple factors can influence the accuracy of EMG pattern recognition. There are various myoelectric prostheses available in the market. Some commonly used models are iLimb, Vincent Hand, iLimb Pulse, SensorHand, BeBionic, BeBionic V2,