Home blood glucose monitoring devices (i.e., glucometers) have been an essential tool in managing diabetes mellitus as well as hypo- and hyper- glycemia events since the 1980s. These traditional glucometers allowed individuals to measure their blood glucose levels at home through a quick and simple procedure of extracting blood via a finger prick. In recent years, newer commercial devices have evolved to provide continuous glucose monitoring throughout the day in a less invasive method than older glucometers. In this contribution, we investigate the feasibility of developing a non-invasive method for continuous glucose monitoring by integrating wearable sensors and learning-based algorithms.
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