本田 雄一郎

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 (89.3 +/- 6.65%), 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.

© The Author(s) 2017. 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 (89:3±6:65%), 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.

This paper proposes a novel EMG-based MyoBock training system that consistently provides a variety of functions ranging from EMG signal control training to task training. Using the proposed training sytem, a trainee controls a virtual hand (VH) in a 3D virtual reality (VR) environment using EMG signals and position/posture information recorded from the trainee. The trainee can also perform tasks such as holding and moving virtual objects using the system. In the experiments of this study, virtual task training developed with reference to the Box and Block Test (BBT) used to evaluate myoelectric prostheses was conducted with two healthy subjects, who repeatedly performed 10 one-minute tasks involving grasping a ball in one box and transporting it to another. The BBT experiments were also conducted in a real environment before and after the virtual training, with results showing an improvement in the number of tasks successfully completed. It was therefore confirmed that the proposed system could be used for myoelectric prosthesis control training.

In this article a new pattern recognition system for hand gestures developed for the control of active hand prostheses is presented. The system uses muscle contraction for gesture recognition. Different hand gestures produce discrete tactile patterns in the prosthetic socket by muscle contractions. The tactile patterns from hand gestures are analyzed and classified by the recognition system. Thus, a user can control hand prosthesis muscle contraction of the arm from handgestures. This paper introduces the concept of force resistive resistor sensor based recognition system for hand gestures and then discusses the issues of stability of classification during reattaching of the prosthetic socket.

In this article a new pattern recognition system for hand gestures developed for the control of active hand prostheses is presented. The system uses muscle contraction for gesture recognition. Different hand gestures produce discrete tactile patterns in the prosthetic socket by muscle contractions. The tactile patterns from hand gestures are analyzed and classified by the recognition system. Thus, a user can control hand prosthesis muscle contraction of the arm from handgestures. This paper introduces the concept of force resistive resistor sensor based recognition system for hand gestures and then discusses the issues of stability of classification during reattaching of the prosthetic socket.

In this article a new recognition system for hand gestures developed for the purpose of controlling active hand prosthesis is presented. The recognition system allows for the measurement and classification of muscle contractions around the lower arm. The system associates muscle contractions to corresponding finger grips of an electrically controlled prosthesis. Thus, a user can control a hand prosthesis by contracting the muscles of his arm through making hand gestures. This contribution describes the setup of the system and a first analysis of the stability and precision for recognizing and differentiating discrete hand gestures.