Low-cost and open-source anthropomorphic prosthetics hand using linear actuators

A robust, low cost, open-source, and low power consumption in the research of prosthetics hand is essential. The purpose of this study is to develop a low-cost, open-source anthropomorphic prosthetics hand using linear actuator based on electromyography (EMG) signal control. The main advantages of this proposed method are the low-cost, lightweight and simplicity of controlling the prosthetic hand using only single channel. This is achieved by evaluating the DC motor and exploring number of locations of the EMG signal. The development of prosthetics hand consists of 3D anthropomorphic hand design, active electrodes, microcontroller, and linear actuator. The active electrodes recorded the EMG signal from extensor carpi radialis longus. The built-in EMG amplifier on the electrode amplified the EMG signal. Further, the A/D converter in the Arduino microcontroller converted the analog signal into digital. A filtering process consisted of bandpass and notch filter was performed before it used as a control signal. The linear actuator controlled each finger for flexion and extension motion. In the assessment of the design, the prosthetic hand capable of grasping ten objects. In this study, the cost and weight of the prosthetics hand are 471.99 US$ and 0.531 kg, respectively. This study has demonstrated the design of low cost and open-source of prosthetics hand with reasonable cost and lightweight. Furthermore, this development could be applied to amputee subjects

Grasp : Design and developement of a 6DOF, 3D printable, open source bionic hand

There are 3 millions of upper limb amputees worldwide, and this represents a minority of 5% of the total number of amputees. For this reason, the costs of production of an advanced bionic limb are so elevated. This impacts on the acquisition prices, setting them between 25.000 and 75.000 €. The main motivation of the present work has been to create a design of a bionic hand with 6 degrees of freedom (DOF), which can be 3D printed with competitive features respect to the best commercial models available in the market in terms of flexion-extension speed and force. Each of the aims of the project has been a different technological challenge. The main objectives can be summarised as follows: (1) the bionic hand must be able to reproduce the 6 most important types of grips required in order to carry out activities of daily life; (2) all the components: electronics and actuators should be included into the palm of the hand to reduce the possible space occupied in the forearm; (3) the hand mass should be less than 400g, which the weight critical for the user to be considered too heavy; (4) the bionic hand should be easily assembled and programmed by the same user once you have downloaded the open source files; (5) the total cost of the hand must be below 500 € including all components. Finally, the greatest challenge is to achieve all these objectives and at the same time be competitive with respect to existing commercial projects. The project includes the development of various designs and 3D printable prototypes that have been carried out during the previous 4 years. All the experience and knowledge acquired with these prior designs culminated in Grasp: the latest design, presented in this work. Grasp sets out to achieve all the objectives that have emerged from the needs that must be covered to provide a product with the optimal quality and functionality

Implantable Neural Interfaces for Direct Control of Hand Prostheses

State-of-the art robotic hands can mimic many functions of the human hand. These devices are capable of actuating individual finger and multi-joint movements while providing adequate gripping force for daily activities. However, for patients with spinal cord injuries or amputations, there are few options to control these functions seamlessly or intuitively. A common barrier to restoring hand function to both populations is a lack of high-fidelity control signals. Non-invasive electrophysiological techniques record global summations of activity and lack the spatial or temporal resolution to extract or “decode” precise movement commands. The ability to decode finger movements from the motor system would allow patients to directly control hand functions and provide intuitive and scalable prosthetic solutions. This thesis investigates the capabilities of implantable devices to provide finger-specific commands for prosthetic hands. We adapt existing reasoning algorithms to two different sensing technologies. The first is intracortical electrode arrays implanted into primary motor cortex of two non-human primates. Both subjects controlled a virtual hand with a regression algorithm that decoded brain activity into finger kinematics. Performance was evaluated with single degree of freedom target matching tasks. Bit rate is a throughput metric that accounts for task difficulty and movement precision. A state-of-the-art re-calibration approach improved throughputs by an average of 33.1%. Notably, decoding performance was not dependent on subjects moving their intact hands. In future research, this approach can improve grasp precision for patients with spinal cord injuries. The second sensing technology is intramuscular electrodes implanted into residual muscles and Regenerative Peripheral Nerve Interfaces of two patients with transradial amputations. Both participants used a high-speed pattern recognition system to switch between 10 individual finger and wrist postures in a virtual environment with an average completion rate of 96.3% and a movement delay of 0.26 seconds. When the set was reduced to five grasp postures, average metrics improved to 100% completion and a 0.14 second delay. These results are a significant improvement over previous studies which report average completion rates ranging from 53.9% to 86.9% and delays of 0.45 to 0.86 seconds. Furthermore, grasp performance remained reliable across arm positions and both participants used this controller to complete a functional assessment with robotic prostheses. For a more dexterous solution, we combined the high-speed pattern recognition system with a regression algorithm that enabled simultaneous position control of both the index finger and middle-ring-small finger group. Both patients used this system to complete a virtual two degree of freedom target matching task with throughputs of 1.79 and 1.15 bits per second each. The controllers in this study used only four and five differentiated inputs, which can likely be processed with portable or implantable hardware. These results demonstrate that implantable sensors can provide patients with fluid and precise control of hand prostheses. However, clinically translatable implantable electronics need to be developed to realize the potential of these sensing and reasoning approaches. Further advancement of this technology will likely increase the utility and demand of robotic prostheses.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169798/1/akvaskov_1.pd