Smartwatch-Based Prediction of Transdermal Alcohol Levels Using Hyperdimensional Computing

Pere Vergés, Manuel E. Segura, Ramesh Arangott, Angela Kristine Garcia, Laura Garcia Reynoso, Alexandru Nicolau, Tony Givargis, Sergio Gago Masagué

Abstract: Excessive alcohol consumption was responsible for 6% of global deaths in 2023. To encourage healthier drinking habits and enhance user awareness of their current condition, just-in-time interventions prove to be a suitable approach for informing users about their current state of intoxication. Current methods for determining blood alcohol content are intrusive and many also invasive, requiring users to use breathalizers or actively engage in urine or blood tests. In this study, we introduce an application utilizing Hyperdimensional Computing to predict if a user is under the influence of alcohol, achieving an accuracy of 93.5% on average. Furthermore, this application is designed to run on both smartphones and smartwatches, enabling full on device computation and online learning through a C implementation utilizing vectorial operations. The application has shown to be very efficient, having a training time per instance of 13.2 and 1.25ms on smartwatch and smartphone respectively and inference time of 6.8 and 1.1ms. Moreover the energy consumption of the running application is negligible compared to the energy usage of the idle device.

External IDs:dblp:conf/wf-iot/VergesSAGRNGM24