Development of Wrist Separated Exoskeleton Socket of Myoelectric Prosthesis Hand for Symbrachydactyly.

In recent years, the functionality of myoelectric prosthetic hands has improved as motors have become smaller and controls have become more advanced. Attempts have been made to reproduce the rotation and flexion of the wrist by adding degrees of freedom to the wrist joint. However, it is still difficult to fully reproduce the functionality of the wrist joint owing to the weight of the prosthesis and size limitations. In this study, we developed a new socket and prosthetic hand control system that does not interfere with the wrist joint motion. This allows individuals with hand defects who previously used prosthetic hands with fixed wrist joints to freely use their remaining wrist functionality. In the pick-and-place experiment, where blocks were moved from higher to lower locations, we confirmed that the proposed system resulted in a lower elbow position compared with the traditional prosthesis, and the number of blocks transported increased. This significantly reduced the compensatory motion of the elbow and improved the user's performance compared with the use of a conventional prosthetic hand. This study demonstrates the usefulness of a new myoelectric prosthetic hand that utilizes the residual functions of people with hand deficiencies, which have not been utilized in the past, and the direction of its development.

Novel prosthesis with proximity sensor for upper extremity phocomelia.

We herein report on the application of a novel motorized prosthetic hand in a child with upper extremity phocomelia.

Coevolution of Myoelectric Hand Control under the Tactile Interaction among Fingers and Objects.

The usability of a prosthetic hand differs significantly from that of a real hand. Moreover, the complexity of manipulation increases as the number of degrees of freedom to be controlled increases, making manipulation with biological signals extremely difficult. To overcome this problem, users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from falling. In previous studies, these have been left to the operating skills of the user, which is extremely difficult to achieve. In this study, we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user's operation technique. The required grasping technique is achieved by determining the correlation between the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli, such as myoelectricity, sense of touch, and grasping objects. The agents of the 16-DOF robot hand were trained with the myoelectric signals of six participants, including one child with a congenital forearm deficiency. Consequently, each agent could open and close the hand in response to the myoelectric stimuli and could accomplish the object pickup task. For the tasks, the agents successfully identified grasping patterns suitable for practical and stable positioning of the objects. In addition, the agents were able to pick up the object in a similar posture regardless of the participant, suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.

Learning Data Correction for Myoelectric Hand Based on "Survival of the Fittest".

In recent years, myoelectric hands have become multi-degree-of-freedom (DOF) devices, which are controlled via machine learning methods. However, currently, learning data for myoelectric hands are gathered manually and thus tend to be of low quality. Moreover, in the case of infants, gathering accurate learning data is nearly impossible because of the difficulty of communicating with them. Therefore, a method that automatically corrects errors in the learning data is necessary. Myoelectric hands are wearable robots and thus have volumetric and weight constraints that make it infeasible to store large amounts of data or apply complex processing methods. Compared with general machine learning methods such as image processing, those for myoelectric hands have limitations on the data storage, although the amount of data to be processed is quite large. If we can use this huge amount of processing data to correct the learning data without storing the processing data, the machine learning performance is expected to improve. We then propose a method for correcting the learning data through utilisation of the signals acquired during the use of the myoelectric hand. The proposed method is inspired by "survival of the fittest." The effectiveness of the method was verified through offline analysis. The method reduced the amount of learning data and learning time by approximately a factor of 10 while maintaining classification rates. The classification rates improved for one participant but slightly deteriorated on average among all participants. To solve this problem, verifying the method via interactive learning will be necessary in the future.

Selective motor fascicle transfer and neural-machine interface: case report.

An amputated nerve transferred to a nearby muscle produces a transcutaneously detectable electromyographic signal corresponding to the transferred nerve; this technique is known as targeted muscle reinnervation (TMR). There are 2 issues to overcome to improve this technique: the caliber and the selectivity of the transferred nerve. It is optimal to select and transfer each motor fascicle to achieve highly developed myoelectric arms with multiple degrees-of-freedom motion. The authors report on a case in which they first identified the remnant stumps of the amputated median and radial nerves and then identified the sensory fascicles using somatosensory evoked potentials. Each median nerve fascicle was transferred to the long head branch of the biceps or the brachialis branch, while the short head branch of the biceps was retained for elbow flexion. Each radial nerve fascicle was transferred to the medial or lateral head branch of the triceps, while the long head branch of the triceps was retained for elbow extension. Electrophysiological and functional tests were conducted in the reinnervated muscles. Functional and electrophysiological improvement was noted, with marked improvement in the identification rate for each digit, forearm, and elbow motion after the selective nerve transfers. The authors note that more selective nerve transfers may be required for the development of prostheses with multiple degrees of freedom.

Selective Linear-Regression Model for hand posture discrimination and grip force estimation using surface electromyogram signals.

This paper proposes the method of hand posture discrimination and grip force estimation by means of Selective Linear-Regression Model. Generally, myoelectric hands which discriminate hand posture and estimate grip force at the same time result in unsatisfying results because of complication of EMG signals. Therefore, most of myoelectric hands can control either the force or the posture. However, the proposed method is able to discriminate hand posture and to estimate grip force simultaneously while the accuracy results are achieved. In experiments, EMG signals were measured while hand posture and grip force were changing. As a result, it appears that EMG features increase monotonically with grip force. In addition, increasing forms of EMG features are different on each posture. Based on these experimental results, the authors propose the method for both discriminating hand posture and estimating grip force by means of several linear-regression models which utilize the relationship between the grip force and EMG features on each posture. To evaluate the effectiveness of this method, the failure rates of discrimination and the estimation errors of the proposed method were employed. The results indicate that failure rates and estimation errors are improved significantly.