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.

Six new peptides with serine protease inhibitory activity have been isolated from the marine sponge Theonella swinhoei. Their structures including absolute stereochemistry were unambiguously established by interpretation of spectral data and chemical degradation. Pseudotheonamides A1 (1), A2 (2), and B2 (3) are linear pentapeptides embracing the rare piperazinone and piperidinoiminoimidazolone ring systems. Pseudotheonamide C (4) contains v-Tyr (vinylogous tyrosine) instead of a piperazinone ring. Pseudotheonamide D (5) is a tetrapeptide which lacks a C-terminal k-Arg (α-ketohomoarginine) unit. Dihydrocyclotheonamide A (6) is a reduction product of the known cyclotheonamide A (7).

We report a choline ester-grafted turn-on fluorescence probe to detect acetylcholinesterase (AChE) in living cells. AChE-mediated hydrolysis of the choline ester moiety producing carboxylate initiates the activation of Cy5 fluorophore...

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 a reaching movement model for the generation of desired trajectories within a myoelectric prosthesis training system. First, an experiment was performed to observe reaching movements with a non-impaired subject and a myoelectric prosthesis user. Reaching movements made by the prosthesis user were then adopted to construct a model based on a logistic function. The proposed model can be used to generate three trajectory types with a bell-shaped speed profile with the adjustment of only a few parameters.