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This paper introduces a neural network-based approach for estimating respiratory rate from ECG signals by leveraging Respiratory Sinus Arrhythmia. It evaluates different deep learning architectures to automatically extract features, offering a scalable solution for non-invasive monitoring.
This paper proposes a comparative system for classifying Parkinson's disease severity using triaxial IMU sensors and ensemble learning, with LightGBM achieving the best performance at around 97% accuracy across metrics.
MIT researchers have developed an ultrasound wristband that tracks hand movements in real-time using AI to control robotic hands or virtual environments. The technology, published in Nature Electronics, aims to improve dexterous robot training and VR interaction without the limitations of cameras or sensor gloves.