ArticleResearch (Washington, D.C.)2024
A Flexible, Large-Scale Sensing Array with Low-Power In-Sensor Intelligence.
Article in Research (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Bio-Inspired Gradient Heterogeneous Architectures for Ultrasensitive and Robust Strain Sensors via Programmable Crack Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Laser-induced graphene/Cu-based fully-porous flexible capacitive pressure sensor with ultra-fast response and wide measurement range.Microsystems & nanoengineering · 2026Article
- One-Dimensional Position Detection Using a Cable Piezoelectric Sensor.Sensors (Basel, Switzerland) · 2026Article
- Flexible, super-resolution skin with in-sensor hyperdimensional computing for real-time pressure field perception.Science advances · 2026Article
- AI-embodied multi-modal flexible electronic robots with programmable sensing, actuating and self-learning.Nature communications · 2025Article
- Fabric-Based Flexible Pressure Sensor Arrays with Ultra-Wide Pressure Range for Lower Limb Motion Capture System.Research (Washington, D.C.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings. However, current intelligent monitoring systems always rely on an external computer with the capability of machine learning rather than integrating it into the sensing device. The computer-assisted intelligent system is hampered by energy inefficiencies, privacy issues, and bandwidth restrictions. Here, a flexible, large-scale sensing array with the capability of low-power in-sensor intelligence based on a compression hypervector encoder is proposed for real-time recognition. The system with in-sensor intelligence can accommodate different individuals and learn new postures without additional computer processing. Both the communication bandwidth requirement and energy consumption of this system are significantly reduced by 1,024 and 500 times, respectively. The capability for in-sensor inference and learning eliminates the necessity to transmit raw data externally, thereby effectively addressing privacy concerns. Furthermore, the system possesses a rapid recognition speed (a few hundred milliseconds) and a high recognition accuracy (about 99%), comparing with support vector machine and other hyperdimensional computing methods. The research holds marked potential for applications in the integration of artificial intelligence of things and flexible electronics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.