Bioinspired Low-Power Mechanical Sensing Technologies.

Authors

Xueping Zhang, Xiangxiang Zhang, Xingkai Huang, Bo Li, Changchao Zhang, Xiancun Meng, Guangjun Chen, You Chen, Junqiu Zhang, Shichao Niu, Zhiwu Han, Luquan Ren

Published in

Advanced materials (Deerfield Beach, Fla.). Pages e75090. Sep 22, 2026. Epub Sep 22, 2026.

Abstract

Bioinspired low-power mechanical sensing technology offers a promising solution to address the escalating energy consumption challenges faced by conventional sensing systems. Traditional mechanical sensing architectures, relying on dense sensor arrays and continuous data acquisition, suffer from substantial power consumption and limited long-term deployability. In contrast, biological mechanosensory systems achieve high sensitivity, robustness, and adaptive intelligence with minimal energy expenditure through structure-enabled signal acquisition, efficient neural processing, and closed-loop actuation. Accordingly, this article evaluates recent advances across the entire bioinspired mechanical sensing pipeline, encompassing signal acquisition, processing, and actuation. At the acquisition frontier, bioinspired sensing strategies based on web-like, slit-organ, lever-based, and skin-inspired topologies are reviewed, illustrating how structural amplification, intrinsic filtering, and directional selectivity enable efficient signal capture with ultralow energy overhead. Building upon these front-end mechanisms, low-power signal processing strategies inspired by biological nervous systems are discussed, emphasizing efficient signal transduction and event-driven, neuromorphic computation to mitigate redundant data handling. Subsequently, biological actuation mechanisms and their engineered counterparts are summarized, highlighting sensorimotor coupling and closed-loop feedback as critical routes to further energy optimization. Finally, representative applications in healthcare and environmental monitoring are presented, alongside current challenges and future trajectories, providing a comprehensive framework for developing next-generation mechanical sensing platforms.

PMID:
42770641
Bibliographic data and abstract were imported from PubMed on 22 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.