Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional Computing on Embedded Devices

TL;DR

This paper introduces a hyperdimensional computing approach for real-time transdermal alcohol level detection on mobile devices, achieving high accuracy with low power and latency, suitable for just-in-time interventions.

Contribution

It presents a novel application of hyperdimensional computing for alcohol detection, improving accuracy and efficiency over traditional machine learning methods on embedded devices.

Findings

89% detection accuracy achieved

12% improvement over state-of-the-art

Low latency and power consumption

Abstract

Alcohol consumption has a significant impact on individuals' health, with even more pronounced consequences when consumption becomes excessive. One approach to promoting healthier drinking habits is implementing just-in-time interventions, where timely notifications indicating intoxication are sent during heavy drinking episodes. However, the complexity or invasiveness of an intervention mechanism may deter an individual from using them in practice. Previous research tackled this challenge using collected motion data and conventional Machine Learning (ML) algorithms to classify heavy drinking episodes, but with impractical accuracy and computational efficiency for mobile devices. Consequently, we have elected to use Hyperdimensional Computing (HDC) to design a just-in-time intervention approach that is practical for smartphones, smart wearables, and IoT deployment. HDC is a framework that has proven results in processing real-time sensor data efficiently. This approach offers several advantages, including low latency, minimal power consumption, and high parallelism. We explore various HDC encoding designs and combine them with various HDC learning models to create an optimal and feasible approach for mobile devices. Our findings indicate an accuracy rate of 89\%, which represents a substantial 12\% improvement over the current state-of-the-art.