ArticleScientific reports2025
Quantum-classical deep learning hybrid architecture with graphene-printed low-cost capacitive sensor for essential tremor detection.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study presents a novel hardware and software architecture combining capacitive sensors, quantum-inspired algorithms, and deep learning applied to the detection of Essential Tremor. At the core of this architecture are graphene-printed capacitive sensors, which provide a cost-effective and efficient solution for tremor data acquisition. These sensors, known for their flexibility and precision, are specifically calibrated to monitor tremor movements across various fingers. A distinctive feature of this study is the incorporation of quantum-inspired computational filters-namely, Quantvolution and QuantClass-into the deep learning framework. This integration offers improved processing capabilities, facilitating a more nuanced analysis of tremor patterns. Initial findings indicate greater stability in loss variability; however, further research is necessary to confirm these effects across broader datasets and clinical environments. The approach highlights a promising application of quantum-inspired methods within healthcare diagnostics.
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