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A framework for estimating muscle pennation angles from raw ultrasound data, demonstrating efficient implementation on low-power processors.

An extended version of EEGformer, demonstrating improved false alarm reduction and efficient deployment on various MCU platforms.

The paper presents a combined seizure and artifact detection framework based on Gradient Boosted Trees. The framework achieves high accuracy in detecting seizures and artifacts, reducing false alarms. The algorithms are optimized for a Parallel Ultra-Low Power platform, enabling extended monitoring with a long battery lifespan. The paper highlights the benefits of integrating artifact detection in wearable epilepsy monitoring devices.

A smart sensor system for real-time performance analysis and biofeedback in ski jumping, featuring energy-efficient design and ML-based predictions.

In this paper, we introduce EpiDeNet, a lightweight seizure detection network, and a novel loss function (SSWCE) to address imbalanced datasets, achieving high accuracy, reduced false positives, and energy-efficient performance on low-power embedded platforms.

EEGformer, a compact transformer model for seizure detection on raw EEG traces, demonstrating efficient execution on MCUs with competitive accuracy.

In this paper, we present implementations of energy-efficient artifact detection algorithms on a parallel ultra-low power platform.

In this paper, we present Clustering-Based REDuction (C-BRED), a new technique to reduce the size of NAS search spaces.

In this paper, we present implementations of seizure detection algorithms on a parallel ultra-low power platform.

In this paper, we propose ECG-TCN, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters.