Neural Interfaces for Bidirectional Sensory Restoration in Limb Prostheses: Current Evidence and Clinical Translation

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Abstract
Background: Restoration of sensory feedback has become one of the major goals of modern bidirectional neuroprosthetic rehabilitation, enabling the transition from conventional prosthetic replacement toward bidirectional neuroprosthetic systems capable of restoring physiological sensorimotor communication. This narrative review aims to provide a comprehensive and clinically oriented overview of current neural and related sensorimotor interfaces for upper- and lower-limb prostheses, focusing on their mechanisms of sensory restoration, clinical performance, and translational potential. Methods: A targeted literature search was conducted in PubMed, Scopus, and Web of Science to identify clinical, translational, and landmark studies published between January 2010 and June 30, 2026. Sources were selected according to prespecified relevance criteria consistent with the narrative design of the review. Owing to the heterogeneity of the included technologies, study designs, populations, and outcomes, the evidence was synthesized narratively. Earlier seminal publications were included when relevant to the physiological basis of sensory restoration and the historical evolution of neural interfaces. The selected literature was analyzed thematically according to neural interface technology, sensory restoration strategies, functional outcomes, and clinical translation. Results: Evidence from small pilot studies and experimental evaluations suggests that direct peripheral and central neural interfaces, biological or regenerative interfaces, neuromuscular signal interfaces, non-invasive sensory-feedback systems, and integrated prosthetic platforms may contribute to the restoration or substitution of tactile, proprioceptive, thermal, and multimodal sensory information. Emerging technologies, including regenerative peripheral nerve interfaces, fully implantable wireless systems, biomimetic sensory encoding, adaptive closed-loop control, and artificial intelligence-assisted decoding, may support further clinical translation, although most remain investigational. However, the available evidence remains limited by small patient cohorts, heterogeneous methodologies, and a lack of standardized long-term outcome measures. Conclusions: Neural and related sensorimotor interfaces have shown the feasibility of providing sensory feedback in selected experimental and early clinical settings. Although no single technology currently fulfils all clinical requirements, the integration of complementary biological, neural, and computational approaches appears to represent the most promising pathway toward personalized neuroprosthetic systems with the potential to improve sensorimotor function, although long-term clinical and quality-of-life benefits remain uncertain.

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