This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.

A robust, real-time control scheme for multifunction myoelectric control

Tactile sensing in dexterous robot hands - Review

The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none of these methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.

/pdf/control-of-multifunctional-prosthetic-hands-by-processing-4g8afn3jgx.pdf

Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal

Dextrous artificial hand design and manipulation is an active research topic. A very interesting practical application is the field of upper limb prosthetics. This paper presents the mechanical design and manipulation aspects of the MANUS-HAND project to develop a multifunctional upper limb prosthesis. The kinematics of our design makes use of the so-called underactuated principle and leads to an innovative design that triples the performance of currently existing commercial hand prosthesis. In addition, the thumb design allows its positioning both in flexion and opposition. As a consequence, up to four grasping modes (cylindrical, precision, hook and lateral) are available with just two actuators.

The proposed impedance control approach allows the fingers to behave as virtual springs. Given the difficulty of including the user in the control loop, this approach is based on an autonomous coordination and control of the grasp. As a consequence, the requirements on the Human Machine interface are reduced. At the end of the paper, we briefly describe the clinical trials that were set up for evaluation purposes.

The MANUS-HAND Dextrous Robotics Upper Limb Prosthesis: Mechanical and Manipulation Aspects

Objective To assess the usefulness, compatibility, and long-term operability of a microelectrode array into the median nerve of the left arm of a healthy volunteer, including perception of feedback stimulation and operation of an instrumented prosthetic hand. Setting The study was carried out from March 14 through June 18, 2002, in England and the United States. Results The blindfolded subject received feedback information, obtained from force and slip sensors on the prosthetic hand, and subsequently used the implanted device to control the hand by applying an appropriate force to grip an unseen object. Operability was also demonstrated remotely via the Internet, with the subject in New York, NY, and the prosthetic hand in Reading, England. Finally, the subject was able to control an electric wheelchair, via decoded signals from the implant device, to select the direction of travel by opening and closing his hand. The implantation did not result in infection or any perceivable loss of hand sensation or motion control. The implant was finally extracted because of mechanical fatigue of the percutaneous connection. Further testing after extraction has not indicated any measurable long-term defects in the subject. Conclusions This implant may allow recipients to have abilities they would otherwise not possess. The response to stimulation improved considerably during the trial, suggesting that the subject learned to process the incoming information more effectively.

https://jamanetwork.com/journals/jamaneurology/articlepdf/784743/nbs20003.pdf

The application of implant technology for cybernetic systems.

The current designs of commercial artificial hands have a low level of innovation. As feedback to the user is difficult to achieve reliably, most devices are simple in design and operation, and limited in functional range. If information on the state of the hand, the forces and any slippage that is occurring is fed back to a microcontroller then more than one degree of freedom can be controlled and a greater and more natural functional range is possible. This paper describes the development of such a device. It outlines the design requirements, the methods of detection of the signals and the training required to operate the hand.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/613AF88D734005B49DF71D7738F654F5/S0263574798000691a.pdf/div-class-title-an-intelligent-anthropomorphic-hand-with-automatic-grasp-div.pdf

Stefan Te Winkel

Papers

An Intelligent Anthropomorphic Hand, with Automatic Grasp