PRE2017 3 Groep7
Problem Statement and Objectives
In the U.S., it is estimated that one in every 190 people have suffered from limb loss.[1] This shows the importance and opportunity of prosthetics. For all these people, part of their old functions can be regained with the use of a prosthetic limb. While technology evolves, the prosthetics are able to perform more functions and are easier to handle. Where old prosthetics are mostly body-powered, they are now myoelectric or even robotic. These robotic prosthetics should eventually be able to mimic all old functions of the lost limb.
One of the challenges in the field is the hand. Especially the wrist is difficult. This joint creates a lot of movement, working with the forearm. To make a joint with as much movement as the wrist and the same power, has proven to be a difficult task. How can one design a full flexible wrist, while also giving it enough strength to lift objects? That will be one of the main questions during this project.
Another big challenge is how to control the prosthetic. Some of the newer, robotic limbs can actually work with the nerve system or the brain. This still proves difficult, since the user will need to learn to use the prosthetic in a natural way and some limbs, like the arm, have many different degrees of freedom to take into account. However, machine learning might prove a way for the prosthetic and the user to meet halfway: the user has to adjust to the prosthetic, and the prosthetic will learn how the user behaves. How do these algorithms work and what could it mean for the industry? By making our own algorithm, we will try to find out whether this might be a breakthrough for prosthetic use.
USE
USE: It is important to look at the user aspects of wrist prostheses. For this there will be looked at the end users, society and enterprise.
Users: The users of a hand prothesis will be people who have somehow lost their hand or arm entirely. Their current way of living would be improved by giving them the possibility to use their prosthesis to pick up objects, which would also help them with basic uses of the hand. Further development could lead to even more possibilities such as catching objects or typing. This would greatly improve the quality of life for these people.
The preferences of the users would of course be a full-fuctioning arm and hand with perhaps even additional functions. The question is whether or not this is desirable, since if a prosthetic hand is more useful than u human one it could incentivise people to have a prostetic hand even if their current hand is still functioning.
Society: A hand prothesis will allow people who lose their arm or hand to more easily rehabilitate to their normal lives and jobs.
Enterprise: The enterprise will be able to sell prostheses.
Deliverables and Approach
The results of this project will be presented in the form of a 3D Functional Model (FuMo) together with an algorithm. The model will consist of a design for a prosthetic hand and wrist in a 3D environment (Probably NX10). Since the design will be virtual as of now, the two deliverables cannot be combined to give a single result. However the algorithm's functionality will still be proven in another way. If in the next couple weeks it is concluded that a physical model is possible to make within the given time frame, the option will be considered.
Planning
| Name | Week 1 | Week 2 | Week 3 | Week 4 |
|---|---|---|---|---|
| Eva | Search for useful sources and make a planning; do research on robot hands | Relate research on human hands to the research on robotic hands; start on the design (FuMo) | Work on the design | Finish draft |
| Jurre | Finding sources; do research on human hands | Relate research on human hands to the research on robotic hands | Aid in either the design or software | Finish draft/first algorithm; wiki up-to-date |
| Karsten | Write about deliverables; do research on control mechanism for prosthetic hand | Start on the design (FuMo) | Work on the design; implement stability/force feedback | Finish draft |
| Steven | Write about users; do research on machine learning for prostheses | Start on machine learning algorithm | Work on machine learning algorithm | Finish first version of algorithm |
| Thijs | Do research on machine learning for prostheses | Start on machine learning algorithm | Work on machine learning algorithm | Finish first version of algorithm |
| Name | Week 5 | Week 6 | Week 7 | Week 8 |
| Eva | Improve design | Finish design; work on presentation | Presentation | - |
| Jurre | Aid in either the design or software | Finish project; work on presentation | Presentation | - |
| Karsten | Improve design | Finish design; work on presentation | Presentation | - |
| Steven | Improvements in efficiency/running time of algorithm | Finish algorithm; work on presentation | Presentation | - |
| Thijs | Improvements in efficiency/running time of algorithm | Finish algorithm; work on presentation | Presentation | - |
The yellow fields are the milestones.
Literature study
State of the art
The human hand
The human hand can be separated into three main parts. The forearm, the wrist and the fingers. For clarity we will look at each part separately. The fingers consist of two hinge joints and condyloid joint at the base of the digit. While they are separate joints, they cannot work independently. A tendon connects each digit with the associated muscles in the forearm. For each digit there is a pair of muscles of which one extends and one curls the digit. There is a second group of muscles also situated in the forearm that spreads the fingers apart. .[2]
The thumb is the only digit that slightly differs from this. By allowing one more degree of freedom to the first joint and adding a muscle in the hand that allows movement in this direction.
The wrist consists of several bones that together function as a single condyloid joint. This allows for it to flex, extend and deviate to both sides. The degrees of motion for the joint are 60° for flexing and extending and 20-30° deviation to both sides. .[3]
The forearm acts like a pivot joint using the two bones there to rotate the wrist. This gives the wrist about 180° of rotation. The forearm is also the place where almost all of the muscles controlling the hand are situated. This allows for the muscles to become larger and therefore stronger than if they were confined within the hand.
Robot hands
In prosthetic and robot arms, the hand and wrist are difficult to develop. Many prosthetics do not even have a wrist motion and fingers cannot move separately, since these are difficult to mimic. Below, three state-of-the-art robots are discussed: first the SoftHand Pro-H, a dexterous hand and the wrist mechanism of the humanoid robot SARA.
Prosthetics can have different kinds of hands. The older ones have fingers, but they cannot move separately from each other. Some can, like the SoftHand Pro-H. This is one of the newer prosthetic devices, which had nineteen degrees of freedom. The fingers can grasp and make a fist, and are soft yet robust, which allows them to also hold pencils or irregular shapes. It is designed in a way that it can actually grip with a force of 40N. [4]
Also, a dexterous prosthetic hand has been developed. This hand works with micro servos and is connected to the brain. Electroencephalography controls the hand. All fingers can move separately, and it has many degrees of freedom. The finger bones are moved by servo motors, which are installed at the base of the fingers. The fingers cannot only bend, but also move sideways. The thumb is even controlled by two motors, giving it more degrees of freedom. It can even touch all the other fingers. This already comes very close to an actual human hand, perfect for amputees. [5]
Wrists in humanoid robots are not the same as in humans. In robots, there are typically two types. The first contains of one or more joints with one degree of freedom, while the second consists of one joint with either two degrees of freedom (a condylar joint) or three degrees of freedom (ball-and-socket joint). Wrists of the first type are often rigid and have high carrying capacity and reliability. They have high positioning accuracy, since they have low backlash. This is important for motion control. Wrists with only one joint and multiple degrees of freedom, are actually more human-like and are combined with artificial muscles. [6]
Control of prosthetics
Several ways to control prosthetics have been used in the past. Some were mechanic, but lately also electromyography and electroencephalography are used. Prosthetic arms working with the spinal cord have been developed, so the wearer only has to think about the movement to do it. Many existing prosthetics work on the twitching of remaining muscles. Other research is looking at gripping intuitively, so the user can grip something without consciously thinking about it. Working with the neural network could also allow sensory feedback, so the user can have ‘a sense of touch’. This way, the imposed pressure can be better regulated. It will give the user their feeling back. [7]
One of the methods used for prosthetics, is surface electromyography (sEMG). It can be used to control myoelectric prosthetics. In order to know which sEMG signal corresponds to which wrist movement, the sEMG of people making certain wrist movements was recorded. This movement was then classified by the system. It resulted in an accuracy of 84.93% in real-time classification, so the prosthetic would make the right movement in about 85% of the cases. [8]
Feedback control for prosthetics research
Sensors for the measurement of applied force can be used in prosthetic hands to control the strength of the hand when grabbing objects. This technology is already implemented in 'soft' grippers in the form of a pneumatic soft sensor (PSS), consisting of a silicon body and a flexible pressure sensor and has been tested succesfully on rubber balls.[9][10] This could be advantageous for picking up fragile objects, raw eggs or fruit for example, in order to not break or bruise the object.
Since interacting with humans and fragile (biological) objects is a requirement for hand prosthetics, soft and flexible sensors and actuators are vital. Soft actuators work through the integration of microscopic changes at molecular level into a macroscopic deformation. This allows them to be 3D-printed at a reasonable price. [11] This holds that these actuators can be embedded within the soft tissue of the skin-like silicon if necessary.
A prosthesic hand behaves similar to an exoskeleton when controlling forces and movement. There has been alot of research on the topic of controlling exoskeletons. The mechanism can be controlled by: a model, hierarchy, parameter or usage based control system. Most exoskeletons nowadays use a combination of mentioned control systems to optimise performance. [12] The sensory feedback can also be interpreted by the human carrier of the exoskeleton / prosthesis. Through the use of discrete (event-driven) over continuous feedback, the traditional limitation of neural delays can be circumvented. [13]
Force feedback turns out to be difficult for haptic devices, since they have limited force accuracy. New research is conducted to use a variable motion mapping method. Now the motion mapping coefficient could be regulated according to the object stiffness. This method could lead to identifying objects without having to see them. However, the stiffness of the object has to be estimated beforehand. It could still be more efficient than current methods. [14]
Machine learning for prostheses
Machine learning has been studied as a way to make prostheses react to electric pulses in the brain. Research has been done on this subject with rhesus monkeys. In this research, rhesus monkeys were first trained to perform a 3D outreaching task, and later implanted with chronic electrode arrays. After that, Principal Component Analysis (PCA) was used to reduce the amount of neurons being used in the analysis. Then a machine learning algorithm was employed to map the neurons being used to the direction of movement. The model made in this way was tested, and could correctly predict when the hand was in motion 81% of the time, and the motion ended less than 3 cm from the correct endpoint in less than 50% of the cases, so there's room for improvement in this algorithm. [15]
Coaching questions
Bibliography
- ↑ K. Ziegler-Graham, E. J. MacKenzie, P.L. Epharim, T. G. Travinson, and R. Brookmeyer. Estimating the prevalence of limb loss in the united stated: 2005 to 2050. Archives of Physical Medicine and Rehabilitation, 89:422-429, March 2008
- ↑ C. L. Taylor, R. J. Schwarz. The anatomy and mechanics of the human hand. "Artificial limbs", 1955.
- ↑ C. Donna, B. A. Boone, P. Azen. Normal Range of Motion of Joints in Male Subjects. "The Journal of Bone and Joint Surgery: incorparated", 1979 .
- ↑ C. Piazza, M.G. Catalano, S.B. Godfrey, M. Rossi, G. Grioli, M. Bianchi, K. Zhao, A. Bicchi. The SoftHand Pro-H. IEEE Robotics & Automation magazine, 2017, p.87-101
- ↑ M. Owen, C. Au, A. Fowke. Development of a Dexterous Prosthetic Hand. Journal of Computing and Information Science in Engineering, 2018,
- ↑ M. Penčić, M. Rackov, M. Čavić, I. Kiss, V. G. Cioată. Social humanoid robot SARA: development of the wrist mechanism. IOP Conf. Series: Materials Science and Engineering 294, 2017
- ↑ J. Edwards. Signal Processing Powers Next-Generation Prosthetics. IEEE Signal Processing Magazine, 2018, p.13-16
- ↑ G.D. Eisenberg, K.G.H.M. Fyvie, A. Mohamed. Real-Time Segmentation and Feature Extraction of Electromyography: Towards Control of a Prosthetic Hand. IFAC PapersOnLine 50-2, 2017, p.151–156
- ↑ H.Yang, Y.Chen, Y.Sun, L.Hao. A novel pneumatic soft sensor for measuring contact force and curvature of a soft gripper. Sensors and Actuators A: Physical, 2017, p.318-327
- ↑ Y. Zhu, J. Li, H. Cai, Y. Wu, H. Ding, N. Pan, X. Wang. Highly sensitive and skin-like pressure sensor based on asymmetric double-layered structures of reduced graphite oxide. Sensors and Actuators B: Chemical, 2018, p.1262-1267
- ↑ A. Zolfagharian, A.Z. Kouzani, S. Y. Khoo, A. A. A. Moghadam, I.Gibson, A. Kaynak. Evolution of 3D printed soft actuators. Sensors and Actuators A: Physical, 2016, p.258-272
- ↑ K.Anam,A.A.Al-Jumaily. Active Exoskeleton Control Systems: State of the Art.Procedia Engineering, 2012,p.988-994
- ↑ C. Cipriani, J.L. Segil, F. Clemente, R. F. ff. Weir, B. Edin. Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand. Exp Brain Res, 2014, p.3421-3429
- ↑ L. Liu, Y. Zhang, G. Liu, W. Xu. Variable motion mapping to enhance stiffness discrimination and identification in robot hand teleoperation. Robotics and Computer–Integrated Manufacturing 51, 2018, p.202–208
- ↑ Isaacs RE, e. (2018). Work toward real-time control of a cortical neural prothesis. - PubMed - NCBI . Ncbi.nlm.nih.gov. Retrieved 18 February 2018, from https://www.ncbi.nlm.nih.gov/pubmed/10896185