Prosthetics is the branch of surgery that “involves the use of artificial limbs to improve the function and lifestyle of people with limb loss” (“What is prosthetics”). These artificial limbs are called prosthetics (singular: prosthetics). Many people include in the definition of prosthetics devices that replace body parts that are not limbs, such as glass eyes or pacemakers. This article, however, will only focus on limb replacement. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay A prosthesis can be controlled in two ways: body-powered or electric. A body-powered limb is a totally manual limb, usually "[relying] on a system of cables or harnesses (along with manual controls, in many cases) to control the limb itself" ("Electric vs. body-powered" ). Body-powered prosthetics are generally more convenient and reliable than electric ones. An electric limb, sometimes called myoelectric (myo means muscle), "[works] by using existing muscles [in] the stump to control the functions of the prosthetic device itself" ("Electric vs. body-powered"). This results in more natural movements and more precise motor control in the limb. In the past, prosthetics were minimal replacements for a missing limb, like a metal rod connected to the remaining leg via a harness. Slowly, canes and harnesses transformed into elaborate designs that mimic the real limb. Functionality has been added so that the amputee can grasp objects or bend the knee with the help of hand controls and cables. Next, we developed artificial limbs that connect to muscles in the remaining limb that electronically control the limb. Today we have prosthetics that can be roughly controlled using brain signals. These huge improvements are promising when you 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, appear to be at the forefront of research into AI-controlled prosthetics. Their most notable design, the Rheo Knee 3, is said 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 be continuously learning, meaning it can adapt to new situations and environments without needing to be explicitly trained for this (Viejo). The idea of ​​a continuously learning prosthetic is discussed in depth in a TED talk by Dr. Patrick Pilarski, who is the Canada Research Chair in Machine Intelligence for Rehabilitation at the University of Alberta and leads the Amii Adaptive Prosthetics program, which focuses on creation of intelligent prosthetics. In his speech he highlights the importance of continually learning about prostheses, giving the example of someone who takes up cooking as a hobby. He explains that a continuously learning prosthetic arm will learn new chopping and stirring motions, making it easier for the user to cook. If the prosthesis was only given things to learn during its initial training, it would not be able to remember cutting and shaking movements, leaving control completely up to the user (Pilarski). There is confusion in the media about prosthetics and the science behind the advancement of artificially intelligent prosthetics. Many of the errors are attributed to the use of buzzwords such as “bionic,” “AI,” and “intelligent.” In general, many articles will use the word bionics to describe any electrical, controlled prosthesisfrom the brain, and will use it interchangeably with words like “cybernetics” and “intelligent.” Although these terms and concepts overlap with those of bionics, the articles do not use the word accurately. 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, as the goal is to observe and mimic the most efficient natural processes and functions (“Bionics”). In prosthetics, bionics can be used to simulate natural hand movement in bionic hands. Cybernetics, although similar, deals with the control systems found 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 present in humans, we can begin to create limbs with infinite movements, rather than simple pre-programmed movements, such as grasping. Another common misconception was around the actual implementation of AI. Even in academic articles it can be difficult to find information about artificial intelligence in prosthetics as it is often not explicitly stated that machine learning is used to train the prosthetic. In many cases, popular tech news sites describe the prosthetic as “smart,” which ended up not being a good indicator for the use of artificial intelligence. Some news sites even seemed to think that artificial intelligence was being used even though the prosthetic was controlled only by brain impulses or muscle movements, without the prosthetic learning or adapting in any way. Natural movement, reliable action predictions, and more In an ideal world, prosthetic limbs would work just 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. There are several important areas that would benefit from improvement, including natural motion, more accurate predictions, and costs. Natural Motion. Natural movement is difficult to master. To give some perspective, an able-bodied person has the advantage of their body and brain working in harmony to produce smooth, natural movements; however, this 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 with a prosthetic arm, for example, does not have all the benefits of having their limb controlled by the brain. Although some prostheses respond to electrical impulses from the brain, the variety of movement is often limited to a series of predetermined actions, such as grasping or pinching. On top of that, up until that point the user hadn't had a lifetime to practice with that particular prosthesis. Machine learning algorithms are significantly reducing the time it takes to learn how to use a new prosthesis correctly by having the user train it to learn about their individual gait, walking speed, environment, and so on. A prosthesis that uses artificial intelligence to learn its user's behavior can significantly improve an amputee's quality of life by making it easier to carry out everyday tasks, such as turning door knobs or climbing stairs. Predict movements. Learning how the user moves is essential to predict their next movements. Correct predictions are important because if the prediction is wrong, it could causedamage to the user. For example, if a prosthetic leg incorrectly predicts that it is about to go up the stairs and begins to lift the leg higher, this can cause the user to lose balance and fall unexpectedly. Since the user relies on the prosthesis to perform the correct action, the risk of incorrect prediction should be low. A lot of research effort is being put into (Zhang) what types of incorrect predictions are safer and more convenient to make versus which can cause serious injury or significant inconvenience. In general, research shows that for a prosthetic leg, any mistake made while the foot is in the air is usually safe and at most slightly uncomfortable, while a mistake made while the leg is supporting a load (the weight of the body) is often dangerous or especially uncomfortable. Less effort. With AI predictions and electric limbs, amputees will expend less effort when performing simple or repetitive tasks. Instead of having to swing a body-powered prosthesis to walk, the leg will “walk itself” by applying force to the ground and bending at the knee. This greatly reduces strain on the amputated limb and allows the user to focus on things other than balancing while walking. Better balance. Balancing on a prosthetic leg can be difficult, especially for older amputees. A prosthesis that uses artificial intelligence to help detect changes in weight distribution can balance more easily and reliably without special input from the user. This helps people walk correctly and safely on uneven terrain and stand without a balance aid. While automatic balancing is beneficial for everyone, it is especially beneficial for people who are at increased risk of falling, such as those with weakened muscles at the end of their limbs, the elderly, subway riders, 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 will cover most of the cost of a prosthesis if it is deemed medically necessary. However, there are many ways to make cheaper prosthetics that use artificial intelligence. 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 grip them properly (O'Reilly). Sirosh says this prosthetic costs only a few hundred dollars without insurance. Many of these goals are attempted by teaching machine learning algorithms with reinforcement learning in a simulated environment. Łukasz Kidziński of Stanford University created a physiology-based human model with a prosthetic leg in a simulator called OpenSim. This human model is a musculoskeletal model, meaning it has contracting muscles and rigid bones that simulate the different stresses on a human leg as it moves. This is a huge improvement over the typical "stick man" model that is commonly used when teaching an AI to walk or run, which lacks muscles and results in an erratic walk or run. By including a prosthesis in the model, the AI ​​can find a more applicable solution for walking and running. Kidziński's model is available on crowdAI as open source, attached to a challenge called AI for Prosthetics Challenge in which the goal is to create artificial intelligence that adapts tochanges in speed, direction and environmental conditions as quickly as possible (Kidziński). While training the AI ​​in a simulation will not perfectly match the needs of an amputee, it is a good start to learning to walk, run and climb without the need to physically train it, and the AI ​​should be allowed to continue learning on its own user to improve. functionality over time. Prosthetic hands that see and “feel” In robotics, it is common to use computer vision to control the movements of a robotic arm. Research has already been conducted in the field of “object recognition, arm positioning, grasp estimation and visual feedback control” (Martin). This concept, however, is new to prosthetics. Adapting this research to a prosthetic is not challenging, considering that the prosthetic is quite similar to a robotic arm. A team of students spread across universities in Florida and Louisiana have created a working prototype of an arm that detects and grasps objects with the help of “eye gaze” data. Essentially, the user will look at an object, the arm will recognize that the object is within reach, and then the arm will move and grab the object, avoiding any obstacles. Their prototype was successful, although it is not ready for widespread use as the user must wear an eye-tracking helmet and connect the arm to an external computer (Martin). A team of students from Newcastle University have improved this concept with a prosthetic hand that recognizes different objects and adjusts grip strength accordingly, and can accurately predict the force needed to grasp and hold an object it has never seen before (" Seeing Hand"). “Sensitive” artificial hands must be surgically implanted. This is because electrodes need to be placed on as many nerve endings as possible in order to stimulate the nerves and provide feedback to the brain. Using these prosthetics with haptic feedback, one man, named Igor Spetic, can pry cherries from their stems with a 93% success rate, compared to a 43% success rate using the same prosthetic with haptic feedback turned off. 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 incredibly important for amputees. When researchers from the DARPA HAPTIX program ask amputees, "they universally say they want to hold a loved one's hand and really feel it" (Tyler). Problems for Athletes Special athletic prosthetics, such as running blades, are popular among many athletic amputees. The use of these prosthetics in competition is controversial, as some see the artificial limb as an enhancement, while others see it as a handicap. This puts amputee athletes in the strange situation of having an advantage in a sport despite having a disability. After Oscar Pistorius from South Africa competed in the Olympics with a blade prosthesis on each leg, another Olympian, named Markus Rehm, was denied permission to compete because he failed to prove that his prosthesis did not give him an advantage. This led to studies on the topic, which led to the conclusion that a runner using blade prosthetics would use 17% less energy to run than an able-bodied competitor, while also taking 21% less time to swing the leg forward while running. These findings led to a ban on Olympic athletes wearing this type of prosthesis (Greenemeier). As AI-powered power prostheses become more widespread, more efficient and lightweight, many athletes may transition to a prosthetic.”).