Leveraging Artificial Intelligence in the Bioengineering of Prosthetics: Enhancing Athletic Performance and Accessibility for Disabled Athletes Through Adaptive, Smart Prosthetic Technologies

Hayden Ko; Shyun Kim; Eugene Lee; Erin Kim

Authors

Hayden Ko Amplify Teens Research Scholars Programme, New York, NY, USA
Shyun Kim Amplify Teens Research Scholars Programme, New York, NY, USA
Eugene Lee Amplify Teens Research Scholars Programme, New York, NY, USA
Erin Kim Amplify Teens Research Scholars Programme, New York, NY, USA

Keywords:

artificial intelligence, prosthetics, machine learning, computer vision, adaptive technology, athletic performance, bioengineering, accessibility, inclusivity, real-time adaptability

Abstract

The integration of artificial intelligence (AI) in the bioengineering of prosthetics represents a transformative advancement in both the functionality and accessibility of prosthetic devices, particularly for athletes with disabilities. This paper delves into the utilization of AI-driven technologies—such as machine learning, computer vision, and adaptive algorithms—in the development of smart prosthetic systems designed to enhance athletic performance and improve accessibility for disabled athletes. By examining recent advancements and innovations in AI, this study highlights how these technologies contribute to the optimization of prosthetic function through real-time adaptability and enhanced user experience.

The paper begins with a comprehensive overview of the fundamental principles of AI and its application in prosthetics. It then transitions to a detailed analysis of how machine learning algorithms facilitate the customization of prosthetic devices to the unique biomechanical needs of athletes. Emphasis is placed on how data-driven approaches enable prosthetics to learn and adapt to various athletic activities, thereby enhancing performance and reducing the physical and cognitive load on the user.

Additionally, the paper explores the role of computer vision systems in prosthetic technology. These systems provide real-time feedback and adjust prosthetic functions based on environmental and situational inputs, thus significantly improving the precision and efficiency of athletic movements. The synergy between computer vision and machine learning is examined, demonstrating how these technologies collectively contribute to the development of adaptive prosthetics that respond dynamically to the user's intentions and external conditions.

A critical aspect of this research is the discussion of the broader implications of AI-enhanced prosthetics for inclusivity and accessibility in sports. The paper assesses how these technologies can bridge the gap between disabled and able-bodied athletes, fostering greater participation and competition in athletic events. Ethical considerations related to the equitable distribution of advanced prosthetic technologies and the potential for exacerbating disparities are also addressed, providing a balanced view of the socio-economic impact of these innovations.

The study further investigates the technical challenges encountered in the integration of AI with bioengineering prosthetics. These challenges include the complexities of designing algorithms that accurately interpret diverse movement patterns, ensuring the robustness and reliability of adaptive systems, and addressing the energy and computational constraints associated with real-time processing. Solutions to these challenges are proposed, including advancements in hardware design, optimization techniques, and interdisciplinary collaboration.

Future directions for research in AI-driven prosthetics are outlined, focusing on emerging trends such as the incorporation of neuroprosthetics, advancements in user interface design, and the potential for integrating AI with advanced materials and manufacturing techniques. The paper concludes by summarizing the transformative potential of AI in enhancing the capabilities of prosthetic devices and its broader impact on the field of bioengineering and sports inclusivity.

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