Prosthetics is the branch of surgery that “involves the use of artificial limbs to improve the function and lifestyle of people who have lost a limb” (“What are prostheses"). These artificial limbs are called prostheses (singular: prosthesis). Many people include devices that replace body parts that are not limbs, such as glass eyes or pacemakers, in the definition of a prosthesis. However, this article will only focus on limb replacement. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayA prosthesis can be controlled in two ways: body-powered or electrical. A body-powered limb is a completely manual limb, usually "[relying] on a system of cables or harnesses (as well as hand controls, in many cases) to control the limb itself" ("Electric or powered by the body"). Body prosthetics are generally more affordable and more reliable than powered prosthetics. An electric limb, sometimes called myoelectric (myo meaning muscle), “[works] by using your existing muscles in [the] residual limb to control the functions of the prosthesis itself” (“Electric vs. Body Powered”) . This results in more natural movements and finer motor control of the limb. In the past, prosthetics were a simple substitute for a missing limb, such as a metal rod connected to the remaining leg by a harness. Gradually, the sticks and harnesses evolved into elaborate models imitating the real limb. Functionality has been added so that the amputee can grasp objects or bend the knee using hand controls and cables. Later, we developed artificial limbs that connect to the muscles of the remaining limb and electronically control the limb. Today we have prosthetics that can be roughly controlled using signals from the brain. These vast improvements are promising when we imagine the ultimate goal of prosthetics: creating artificial limbs that function exactly as well and as easily as a real limb. Current Efforts and MediaÖssur, an Icelandic company that develops prosthetics, appears to be at the forefront of research into AI-controlled prosthetics. Their most notable design, the Rheo Knee 3, claims to learn a user's gait in less than 15 seconds. After minimal training and practice, he can climb stairs naturally and reliably. The Rheo Knee 3 is said to learn continuously, meaning it can adapt to new situations and environments without needing to be explicitly trained to do so (Viejo). The idea of ​​a continuously learning prosthesis is discussed in depth in a TED talk by Dr. Patrick Pilarski, Canada Research Chair in Artificial Intelligence for Rehabilitation at the University of Alberta and lead of the program Amii Adaptive Prosthetics, focused on creating intelligent prosthetics. In his talk, he highlights the importance of lifelong learning about prosthetics, giving the example of someone who takes up cooking as a hobby. He explains that a continuously learning prosthetic arm will learn the new movements of chopping and stirring, making cooking easier for the user. If the prosthesis had only been given things to learn during its initial training, it would not be able to remember chopping and shaking movements, leaving control entirely to the user (Pilarski). In the media, there is aconfusion around prosthetics and science. behind the progress of artificially intelligent prosthetics. Many errors are attributed to the use of buzzwords like “bionic,” “AI,” and “intelligent.” In general, many articles use the word bionic to describe any prosthesis that is both electrical and controlled by the brain, and they use it interchangeably with words like "cybernetic" and "intelligent." Although these terms and concepts overlap with bionics, articles do not use the word precisely. To clarify, bionics is the study and practice of creating artificial systems that closely mimic the functions and capabilities of living things they are designed to replace. The field of bionics encompasses much more than just prosthetics, since the goal is to observe and imitate the most efficient natural processes and functions (“Bionics”). In prosthetics, bionics can be used to simulate the natural movement of bionic hands. Cybernetics, although similar, deals with the control systems in place in living creatures. In prosthetics, cybernetics can increase the functionality of a hand connected to nerves and muscles (“cybernetics”). Using the communication and control systems already in place in humans, we can begin to create limbs with infinite movements, rather than just pre-programmed movements, like grasping. Another common misunderstanding concerns the actual implementation of AI. Even in scientific articles, it can be difficult to find information about AI in prosthetics, because it is often not explicitly stated that machine learning is used to train the prosthetic. In several cases, popular technology news sites referred to the prosthesis as "smart," which ultimately was not a good indicator of AI use. Some news sites even seemed to think that AI was being used even if the prosthesis was controlled only by brain impulses or muscle movements, without the prosthesis learning or adapting in any way. Natural movement, reliable action predictions and much more. In an ideal world, prosthetic limbs would function as well, if not better, than healthy limbs. The goal of creating artificial limbs that rival real limbs is, while not impossible, very difficult to achieve. Several important areas could benefit from improvements, including natural motion, more accurate predictions, and the cost of natural motion. Natural movement is difficult to master. To give some perspective, an able-bodied person benefits from having their body and brain working in harmony to produce fluid, natural movements; however, it still requires years of practice and fine-tuning. Even after “perfecting” natural movement, the human body continually improves and adapts to new situations. On the other hand, a person wearing a prosthetic arm, for example, does not benefit from all the advantages of having their limb controlled by their brain. Although some prosthetics respond to electrical impulses from the brain, the variety of movements is often limited to a set of predetermined actions, such as grasping or pinching. Additionally, the user hasn't had their entire life so far to train with this particular prosthetic. Machine learning algorithms significantly reduce the time it takes to learn how to properly use a new prosthesis by having the user train it to learn about their individual gait, walking speed, surroundings, and more. A prosthesis that uses AI to learnIts user behavior can greatly improve an amputee's quality of life by making it easier to perform daily tasks, such as turning doorknobs or climbing stairs. Predict movements. Learning how the user moves is key to predicting their next moves. Proper predictions are important because if the prediction is wrong, it could harm the user. For example, if a prosthetic leg incorrectly predicts that it is about to climb stairs and begins to lift the leg higher, the user may lose balance and fall unexpectedly. Since the user relies on the prosthesis to perform the correct action, the risk of an incorrect prediction should be low. Much effort is devoted to research (Zhang) on ​​what types of erroneous predictions are safer and more practical to make compared to erroneous predictions. which may cause serious injury or major inconvenience. In general, research shows that for a prosthetic leg, any error made while the foot is in the air is generally safe and at most mildly inconvenient, while an error made while the leg is supporting a load (the weight of the body) is often dangerous or very annoying. Less effort. Thanks to AI predictions and electric limbs, amputees will exert less effort while performing simple or repetitive tasks. Rather than having to swing a body-powered prosthesis to walk, the leg will “walk on its own” by applying force to the ground and bending the knee. This significantly reduces the strain on the amputated limb and allows the user to focus on things other than balance while walking. Better balance. Balancing on a prosthetic leg can be difficult, especially for older amputees. A prosthesis that uses AI to help detect changes in weight distribution can balance more easily and reliably without any special user intervention. This helps people walk correctly and safely on uneven ground and stand without balance aids. While self-balancing benefits everyone, it particularly benefits people who are at higher risk of falling, such as those with weakened extremity muscles, the elderly, those who take the subway, and hikers. Cost and accessibility. The cost of an electric prosthesis can range from $3,000 to $50,000. For reference, the Ossur Rheo Knee 3 costs around $45,000 without insurance. In the United States, medical insurance covers most of the cost of a prosthesis if it is deemed medically necessary. However, there are many ways to make less expensive prosthetics using AI. Many people and companies have started 3D printing body-powered prosthetic arms at minimal cost. Very recently, Microsoft's Joseph Sirosh developed a prosthetic arm that connects to the cloud and uses computer vision to recognize objects and grasp them in the right way (O'Reilly). Sirosh says this prosthesis only costs a few hundred dollars without insurance. Many of these goals are achieved by teaching machine learning algorithms with reinforcement learning in a simulated environment. Łukasz Kidziński from Stanford University created a human model physiologically based on a prosthetic leg in a simulator called OpenSim. This human model is a musculoskeletal model, meaning it has contracted muscles and rigid bones that simulate the different stresses exerted on a human leg when it moves. This is an improvementconsiderable compared to the typical "stick man" model commonly used to teach an AI to walk or run, which lacks muscle and results in abnormal walking or running. By including a prosthetic on the model, the AI ​​can find a solution more applicable to walking and running. Kidziński's model is available on crowdAI as open source, combined with a challenge called AI for Prosthetics Challenge in which the goal is to create AI that adapts as quickly as possible to changes in speed, direction and environmental conditions (Kidziński). Although training AI in a simulation may not perfectly match the needs of an amputee, it is a good start to learning to walk, run, and climb without needing to physically train it, and AI should be allowed to continue learning more about its user to improve. functionality over time. Prosthetic hands that see and “feel” In robotics, it is common to use computer vision to help control the movements of a robotic arm. Research has already been carried out on “object recognition, arm positioning, grip estimation and vision feedback control” (Martin). However, this concept is new in prosthetics. Adapting this research to a prosthetic is not a challenge, given that the prosthesis is quite similar to a robotic arm. A team of students across universities in Florida and Louisiana has created a working prototype of an arm that detects and grasps objects using “gaze” data. Essentially, the user will look at an object, the arm will recognize that the object is within range, and then the arm will move and grab the object, thereby avoiding any obstacles. Their prototype was a success, although it is not ready for large-scale use since the user must wear a headset for eye tracking and connect the arm to an external computer (Martin). A team of Newcastle University students have improved this concept with a prosthetic hand that recognizes different objects and adjusts grip force accordingly and can accurately predict the force needed to grasp and hold an object it does not have. never seen before (“Hand that sees”). “Sensitive” artificial hands must be surgically implanted. This is because the electrodes need to be placed on as many nerve endings as possible to be able to stimulate the nerves and provide feedback to the brain. Using these haptic feedback prosthetics, a man, named Igor Spetic, can pluck cherries from their stems with a 93% success rate, compared to 43% using the same prosthesis with the haptic feedback disabled. The importance of tactile sensations in prosthetic limbs is enormous, as being able to restore "one of the most basic forms of human contact" is extremely important for amputees. When DARPA HAPTIX program researchers survey amputees, “they universally respond that they want to hold the hand of a loved one and really feel it” (Tyler). Problems for athletes Special sports prosthetics, such as running blades, are popular among many amputee athletes. . The use of these prostheses in competition is controversial, since some see the artificial limb as an improvement, while others see it as a handicap. This puts amputee athletes in the strange situation of having an advantage in a sport while having a disability. After South Africa's Oscar Pistorius competed in the Olympics with a prosthetic blade in each leg, another Olympian, Markus Rehm, was refused permission to compete after failing to prove that his prosthesis did not give him an advantage. This led to.”).