Strategic Development of Smart Prosthetic Lower Limb with Trainable Model for Balanced Movement

Dalia Nandi (), Arka Bairagi (), Subhradeep Mandal () and Oishila Bandyopadhyay ()
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Dalia Nandi: Indian Institute of Information Technology Kalyani, Electronics and Communication Engineering Department
Arka Bairagi: National Institute of Technology Rourkela, Department of Electronics and Communication Engineering
Subhradeep Mandal: Indian Institute of Information Technology Kalyani, Electronics and Communication Engineering Department
Oishila Bandyopadhyay: Indian Institute of Information Technology Kalyani, Computer Science and Engineering Department

A chapter in AI in Smart and Secure Healthcare, 2026, pp 143-166 from Springer

Abstract: Abstract People with unilateral transfemoral amputation suffer through difficulties in their daily activitifes. A prosthetic lower limb offers a solution to mitigate these difficulties. This chapter will focus on the low-cost design and development of a smart prosthetic lower limb prototype, which can provide balanced movements with gait synchronization of the normal leg. The main challenge for the development of the prototype of a prosthetic lower limb is finalizing the electronic components by several tests to reduce the size, cost, and weight, and to enhance the reliability and stability of the design. The motor torque computation is required for both knee and ankle joints based on weights and lengths of different parts of the normal limb to maintain proper body balancing. The shelf life, durability and usability of the designed prosthetic limb have been validated by cycle testing. To create a smooth, balanced gait pattern, the machine learning model can be applied for synchronizing the prosthetic lower limb movement with the normal limb. In real-time scenario, the stability of the prosthetic lower limb for different postures and transition activities can be detected from the sensor values placed at the ankle and knee joint angles. This type of customizable prosthetic lower limb improves mobility and functionality for those who have had a transfemoral amputation by allowing them to use it in a diverse environment and mimicking natural motions to help with rehabilitation protocols. It significantly improves the quality of life for users by ensuring a natural gait, reducing energy expenditure and promoting balanced, safe movement.

Keywords: Design upgradation; Original leg replication; Natural gait pattern; Machine learning; Cycle testing (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-15092-9_6

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