SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
A review of wearable sensors and systems with application in rehabilitation,
2 Pith papers cite this work. Polarity classification is still indexing.
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A-Live detects human liveness passively using IMU signals from commodity devices by identifying neuromuscular micro-motion signatures, achieving over 99.5% accuracy.
citing papers explorer
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SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
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A-Live: Passive Liveness Detection via Neuromuscular Micro-Motion Signatures on Commodity Sensors
A-Live detects human liveness passively using IMU signals from commodity devices by identifying neuromuscular micro-motion signatures, achieving over 99.5% accuracy.