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Abstract Dysphagia or difficulty swallowing is caused by the failure of neurological pathways to properly activate swallowing muscles. Current electromyography (EMG) systems for dysphagia monitoring are bulky and rigid, limiting their potential for long‐term and unobtrusive use. To address this, a machine learning‐assisted wearable EMG system is presented, utilizing self‐adhesive, skin‐conformal, semi‐transparent, and robust ionic gel electrodes. The presented electrodes possess good conductivity, superior skin contact, and good transmittance, ensuring high‐fidelity EMG sensing without impeding daily activities. Moreover, the optimized material and structural designs ensure wearing comfort and conformable skin‐electrode contact, allowing for long‐term monitoring with high accuracy. Machine learning and mel‐frequency cepstral coefficient techniques are employed to classify swallowing events based on food types and volumes. Through an analysis of electrode placement on the chin and neck, the proposed system is able to effectively distinguish between different food types and water volumes using a small number of channels, making it suitable for continuous dysphagia monitoring. This work represents an advancement in machine learning assisted EMG systems for the classification and regression of swallowing events, paving the way for more efficient, unobtrusive, and long‐term dysphagia monitoring systems.
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Dynamic quantitative phase microscopy: a single-shot approach using geometric phase interferometry
Abstract There is a significant gap in cost-effective quantitative phase microscopy (QPM) systems for studying dynamic cellular processes while maintaining accuracy for long-term cellular monitoring. Current QPM systems often rely on complex and expensive voltage-controllable components like Spatial Light Modulators or two-beam interferometry. To address this, we introduce a QPM system optimized for time-varying phase samples using azobenzene liquid crystal as a Zernike filter with a polarization-sensing camera. This system operates without input voltage or moving components, reducing complexity and cost. Optimized for gentle illumination to minimize phototoxicity, it achieves a 1 Hz frame rate for prolonged monitoring. The system demonstrated accuracy with a maximum standard deviation of ±42 nm and low noise fluctuations of ±2.5 nm. Designed for simplicity and single-shot operations, our QPM system is efficient, robust, and precisely calibrated for reliable measurements. Using inexpensive optical components, it offers an economical solution for long-term, noninvasive biological monitoring and research applications.
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- Award ID(s):
- 2047592
- PAR ID:
- 10528966
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Physics
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2399-3650
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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