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Advances and Trends in Intelligent Lower-Limb Prostheses: A Systematic Review of Mechanical Design, Sensing, and Control

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Systematic Review Open Access

Advances and Trends in Intelligent Lower-Limb Prostheses: A Systematic Review of Mechanical Design, Sensing, and Control

Xiaolong Shu 1,2,3 Shengli Luo 1,2,3 Xu Wang 1,2,3 Qiwen Liu 1,2,3 Yiyao Qin 1,2,3 Qiaoling Meng 1,2,3 Hongliu Yu 1,2,3,*

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1
Institute of Intelligent Rehabilitation Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2
Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China
3
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
*
Authors to whom correspondence should be addressed.

Received: 26 March 2026 Revised: 26 May 2026 Accepted: 29 June 2026 Published: 09 July 2026

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© 2026 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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Intell. Rehabil. Eng. 2026, 1(1), 10004; DOI: 10.70322/ire.2026.10004
ABSTRACT: Intelligent lower-limb prostheses are evolving from single-joint assistance toward coordinated, system-level control that supports cross-task adaptation, multimodal intent estimation, and verifiable safety. This systematic review surveys powered, semi-active, microprocessor-controlled, and related intelligent lower-limb prosthesis literature published between 1 January 2021 and 1 January 2026, spanning electromechanical design, sensing and human-machine interfaces, state/phase estimation, intent/terrain recognition, control and learning, evaluation endpoints, and translational considerations. Following a PRISMA-style workflow, 180 full-text reports were included and synthesized into a modular taxonomy covering clinical needs and endpoints; actuation and transmission; sensing and human-machine interfaces; phase/state estimation; intent/terrain recognition; impedance and trajectory control, including model predictive control; personalization with explicit safety constraints; real-world validation; and safety, reliability, and standardization. Emerging patterns include backdrivable low-impedance hardware, multimodal sensing with uncertainty-aware gating, and continuous phase-variable control, although the level of validation remains heterogeneous. Key gaps remain in endpoint consistency, external validity across users and contexts, and failure-mode reporting. We recommend benchmark protocols and system-level validation frameworks to support more reproducible evaluation and future clinical translation.
Keywords: Lower-limb prosthesis; Robotic prosthesis; Variable impedance; Gait phase; Intent recognition; Sensor fusion; Learning-based control; Real-world evaluation
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