Revolutionizing Prosthetics: The Synergy of Artificial Intelligence and Brain-Computer Interfaces?
By 2050, there is expected to be a twofold¹ increase in amputation due to disease and trauma in the US. These disabilities severely limit mobility and social activity for millions of Americans, whose movements are slower, less stable, and less efficient. For these people, prosthetics equipped with human-like control can transform their lives. These prosthetics hold the power to restore a high degree of independence for these people and improve their quality of life.
There is a full spectrum of prosthetics today ranging from simple, unmovable prosthetics to prosthetics that offer some movement to modern advancements offering improved functionality and more natural movement. Some of the advancements include
- Bionic limbs with advanced sensors for natural motion and more control
- Myoelectric prosthetics controlled by muscle signals for precise control
- Neural interfaces for intuitive control
- 3D printing for customized devices and to reduce costs
- Sensory feedback enhancing interaction
It is important for prosthetics to be functional and cost effective. Ongoing research integrates artificial intelligence and brain-computer interfaces, promising a future where prosthetics offer a seamless and more natural experience. Imagine a prosthetic arm with human-like sensory capabilities or a prosthetic leg with human-like control and reflexes!
Artificial Intelligence is expected to transform Prosthetics
AI and machine learning are set to transform prosthetics, offering smoother control by decoding signals like brain waves and muscle activity. They adapt, learn, and personalize responses, ensuring a seamless and precise user experience and a closer approximation of natural limb function².
- Help read and react to the external environment — It is important for robotic prosthetics to have sensory feedback to and adapt. For example, lower limb prosthetics need to adapt based on the type of terrain to walk in a safer and more natural manner. Researchers at North Carolina State University³ are leveraging AI algorithms and are incorporating computer vision to predict terrain types, quantify uncertainties, and adjust behavior based on this information.
- Help read and react based on user’s intent — The Esper⁴ Hand is a smart prosthetic that learns and predicts user movements using brain/neural signals. The more it’s used, the faster and more accurate it becomes. Another example is the Utah Bionic Leg⁵, which leverages sensor input from the muscles in the residual limb and correlates those signals to the user’s intent.
- Help read and react to user’s state/personalization — AI-powered prosthetics, like smart sockets, seamlessly adapt to users over time. Equipped with sensors, they automatically adjust to changes in the residual limb, ensuring a secure and comfortable fit. These devices learn and respond to user preferences for a tailored experience.
- Predictive maintenance — AI can help monitor the prosthetic’s performance and predict when maintenance or repairs are needed, reducing downtime and improving reliability. This can help users avoid unexpected breakdowns and ensure that their prosthetic is always functioning at its best.
Integration of AI and BCI: The Next Frontier
Brain Computer Interface (BCI) technology enables direct communication between brain and prosthetic devices. BCI can record the brain’s activity, decode its meaning, and affect function. However, BCI alone cannot translate the user’s intent and match it to the action of a robotic arm. BCI in combination with AI in prosthetics is crucial because it makes controlling artificial limbs more natural through direct interpretation of neural signals and adapting over time to offer personalized experience, enhanced dexterity, and real-time feedback leading to increased independence and a better quality of life for those with limb loss.
How Does AI-powered Brain Computer Interface Work?
- The amputee thinks of an intent to move the prosthetic arm which produces signal in the brain
- The signals are acquired from motor area of the brain (motor cortex)
- The signals are then pre-processed to remove noise
- Signals are decoded by analyzing the patterns, identifying which signal was generated and mapping it to the specific action that the prosthetic arm needs to do
- 3D trajectory of the movement is computed
- The action signal is then sent to the prosthetic arm
- The prosthetic arm performs the action
- Sensors on the robotic arm detect information on touch and/or proprioception(via movement and position of the prosthetic limb)
- Output from sensors is converted to stimulus pulses
- Stimulus pulses are then delivered to somatosensory cortex
The constant feedback loop leverages AI to gain precision control of movements.
Challenges and Ethical Considerations
Using AI and BCI in prosthetics faces numerous challenges⁶.
- Accurate interpretation of complex neural signals is tough, impacting the precision of prosthetic movements
- Training periods for adaptation and learning individual preferences can be time-consuming
- Seamless integration with the user’s body poses complexity. Until now, successful BCIs have relied on invasive brain implants, limited by high costs, medical expertise, and potential risks. A major challenge is developing less or noninvasive technology. Non-invasive BCIs face challenges as the quality of the signal is lower than that of implanted devices resulting in less precise control of the prosthetics
- The cost of developing advanced AI and BCI technologies limits accessibility
- Ethical concerns, including privacy issues related to neural data, need careful consideration
- Users may find it challenging to adapt, requiring patience and support
- Regular maintenance and updates for AI-powered prosthetics are necessary
- Accessibility is restricted by factors like cost and technological infrastructure
Addressing these challenges requires ongoing research, technological advancements, user education, and ethical considerations for responsible integration.
The future of prosthetics holds immense potential with the convergence of AI and BCI technologies. Collaboration among researchers, engineers, and designers aims to provide amputees with limbs that emulate natural movement and seamlessly integrate into their lives. Beyond functionality and aesthetics, these advancements offer empowerment, independence, and hope, transforming the lives of individuals with limb loss.
References
[1] Estimating the prevalence of limb loss in the United States: 2005 to 2050, Ziegler — Graham K, MacKenzie EJ, Ephraim PL, Travison TG, Brookmeyer R., https://pubmed.ncbi.nlm.nih.gov/18295618/
[2] Four ways AI is making prosthetic tech smarter, https://livingwithamplitude.com/artificial-intelligence-prosthetic-technology/
[3] Researchers Incorporate Computer Vision, Uncertainty into AI for Robotic Prosthetics https://www.medicaldesignbriefs.com/component/content/article/37245-researchers-incorporate-computer-vision-uncertainty-into-ai-for-robotic-prosthetics?r=47337
[4] Esper Hand https://esperbionics.com
[5] Utah Bionic Leg https://www.mech.utah.edu/utah-bionic-leg-in-science-robotics/
[6] Overcoming Challenges and Innovations in Orthopedic Prosthesis Design: An Interdisciplinary Perspective https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10180679/
[7] AI’s Next Frontier: Are Brain-Computer Interfaces The Future Of Communication? https://www.forbes.com/sites/bernardmarr/2023/08/11/ais-next-frontier-are-brain-computer-interfaces-the-future-of-communication/?sh=134c64db51d9
[8] Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems https://www.mdpi.com/2076-3425/4/1/1
[9] DARPA-funded efforts in the development of novel brain-computer interface technologies
[10] Neuroengineering tools/applications for bidirectional interfaces, brain–computer interfaces, and neuroprosthetic implants — a review of recent progress https://www.frontiersin.org/files/Articles/2123/fneng-03-00112-HTML/image_m/fneng-03-00112-g001.jpg
