Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator’s electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy–based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant.

This article proposes a virtual hand and a virtual training system for controlling the MyoBock-the most commonly used myoelectric prosthetic hand worldwide. As the virtual hand is controlled using the method also adopted for the MyoBock hand, the proposed system provides upper-limb amputees with operation sensibilities similar to those experienced in MyoBock control. It can also display an additional virtual hand for the provision of instructions on hand operation, such as the recommended posture for object grasping and the trajectory desirable to reach a target. In virtual hand control experiments conducted with an amputee to evaluate the proposed virtual hand's operability, the subject successfully performed stable opening and closing with high discrimination rates ð89:3+6:65%Þ, thanks to the virtual hand's incorporation of the MyoBock's operational characteristics. A training experiment using the proposed system was also conducted with eight healthy participants over a period of 5 days. The participants were asked to perform the box and block test using the MyoBock hand in a real environment on the first and final days. The results showed that the number of blocks transported in 1 min significantly increased and that the participants using the instruction virtual hand changed the orientation of the hand approaching blocks from vertical to lateral. The outcomes of the experiment indicate that the proposed system can be used to improve MyoBock hand control operation both quantitatively and qualitatively.

This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance. This facilitates representation of artificial EMG signals with signal-dependent noise superimposed according to the muscle activation levels. The frequency characteristics of the EMG signals are also simulated via a shaping filter with parameters determined by an autoregressive model. An estimation method to determine EMG variance distribution using rectified and smoothed EMG signals, thereby allowing model parameter estimation with a small number of samples, is also incorporated in the proposed model. Moreover, the prediction of variance distribution with strong muscle contraction from EMG signals with low muscle contraction and related artificial EMG generation are also described. The results of experiments conducted, in which the reproduction capability of the proposed model was evaluated through comparison with measured EMG signals in terms of amplitude, frequency content, and EMG distribution demonstrate that the proposed model can reproduce the features of measured EMG signals. Further, utilizing the generated EMG signals as training data for a neural network resulted in the classification of upper limb motion with a higher precision than by learning from only measured EMG signals. This indicates that the proposed model is also applicable to motion classification.

Pro-opiomelanocortin (POMC)-expressing neurons in the hypothalamic arcuate nucleus (ARC) play key roles in feeding and energy homoeostasis, hence their development is of great research interest. As the process of neurogenesis is accompanied by changes in adhesion, polarity, and migration that resemble aspects of epithelial-to-mesenchymal transitions (EMTs), we have characterised the expression and regulation within the prospective ARC of transcription factors with context-dependent abilities to regulate aspects of EMT. Informed by pseudotime meta-analysis of recent scRNA-seq data, we use immunohistochemistry and multiplex in situ hybridisation to show that SOX2, SRY-Box transcription factor 9 (SOX9), PROX1, Islet1 (ISL1), and SOX11 are sequentially expressed over the course of POMC neurogenesis in the embryonic chick. Through pharmacological studies ex vivo, we demonstrate that while inhibiting either sonic hedgehog (SHH) or Notch signalling reduces the number of SOX9+ neural progenitor cells, these treatments lead, respectively, to lesser and greater numbers of differentiating ISL1+/POMC+ neurons. These results are consistent with a model in which SHH promotes the formation of SOX9+ progenitors, and Notch acts to limit their differentiation. Both pathways are also required to maintain normal levels of proliferation and to suppress apoptosis. Together our findings demonstrate that hypothalamic neurogenesis is accompanied by dynamic expression of transcription factors (TFs) that mediate EMTs, and that SHH and Notch signalling converge to regulate hypothalamic cellular homoeostasis.