Student Conference Proceedings
Vol. 1 No. 1 (2025): Stud Conf Proc
https://doi.org/10.18416/SCP.2025.1944

Medical Engineering Science, ID 1944

Combination of motion data and electromyography for threshold-based swallow onset detection

Main Article Content

Nils Lange (Study program Medical Engineering Science, Universität zu Lübeck, Lübeck, Germany), Maxim Fenko (SensorStim Neurotechnology GmbH, Berlin, Germany), Constantin Wiesener (SensorStim Neurotechnology GmbH, Berlin, Germany), Daniel Laidig (SensorStim Neurotechnology GmbH, Berlin, Germany), Benjamin Riebold (1) SensorStim Neurotechnology GmbH, Berlin, Germany; 2) Control Systems Group, Technische Universität Berlin, Berlin, Germany), Philipp Rostalski (Institute for Electrical Engineering in Medicine, Universität zu Lübeck, Lübeck, Germany), Thomas Schauer (1) SensorStim Neurotechnology GmbH, Berlin, Germany; 2) Control Systems Group, Technische Universität Berlin, Berlin, Germany)

Copyright (c) 2025 Nils Lange, Maxim Fenko, Constantin Wiesener, Daniel Laidig, Benjamin Riebold, Philipp Rostalski, Thomas Schauer

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Dysphagia – difficulty in swallowing – represents a restriction for patients. Approaches like biofeedback-based training and functional electrical stimulation (FES) arise as supporting methods. Both require online swallow onset detection for triggering, whereby non-invasive measurement methods should be preferred. In this pilot work, a threshold-based approach for the detection of swallow onsets is presented, which utilizes electromyography (EMG) and motion parameters of the larynx, measured by a wearable inertial measurement unit (IMU). For data collection, a pilot study with nine subjects was conducted, in which swallows with different volumes and consistencies, movement and speech were recorded. Evaluation of the detection approach is based on a cross-validation in combination with a grid search for finding appropriate thresholds. The detection approach results in a F1 score of 0.825 ± 0.074, rated as sufficient for a first feedback mechanism. This work supports the usage of the previously named signals as non-invasive measures for onset detection.

Article Details