While hypoglycemia has been widely studied in medical research, studies assessing vocal changes associated with this state are limited. This study aims at collecting a data set labelled with the gold standard (blood glucose) to provide a solid basis for the identification of vocal biomarkers using machine learning. Additionally, physiological data are collected using wearable sensors to assess whether additional integration of vital signs (e.g. heart rate) enhances the performance of hypoglycemia detection.
The HypoVoice study aims at identifying potential vocal biomarkers associated with hypoglycemia to pave the way towards a voice-based hypoglycemia detection approach.