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

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

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the author(s) used ChatGPT (OpenAI) in order to improve the clarity, grammar, and readability of the text through language polishing and stylistic refinement. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Acknowledgments

The authors thank their colleagues for constructive discussions that improved the manuscript.

Author Contributions

X.S.: Conceptualization, Methodology, Investigation, Data curation, Writing—original draft, Visualization. S.L.: Investigation, Data curation, Writing—review & editing. X.W.: Data curation, Formal analysis, Writing—review & editing. Q.L.: Data curation, Writing—review & editing. Y.Q.: Visualization, Writing—review & editing. Q.M.: Supervision, Writing—review & editing. H.Y.: Conceptualization, Supervision, Project administration, Writing—review & editing. All authors reviewed and approved the final manuscript.

Ethics Statement

Not applicable. This article is a systematic review of previously published literature and did not involve any new studies with human participants or animals performed by any of the authors.

Informed Consent Statement

Not applicable. This study did not involve the recruitment of participants or the collection of identifiable personal data.

Data Availability Statement

No new data were generated or analyzed in this study. All information supporting the findings of this review is available within the article.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 62473263).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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