Advancing Intoxication Detection: A Smartwatch-Based Approach
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909837209632768 |
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| author | Segura, Manuel Vergés, Pere Ky, Richard Arangott, Ramesh Garcia, Angela Kristine Trong, Thang Dihn Hyodo, Makoto Nicolau, Alexandru Givargis, Tony Gago-Masague, Sergio |
| author_facet | Segura, Manuel Vergés, Pere Ky, Richard Arangott, Ramesh Garcia, Angela Kristine Trong, Thang Dihn Hyodo, Makoto Nicolau, Alexandru Givargis, Tony Gago-Masague, Sergio |
| contents | Excess alcohol consumption leads to serious health risks and severe consequences for both individuals and their communities. To advocate for healthier drinking habits, we introduce a groundbreaking mobile smartwatch application approach to just-in-time interventions for intoxication warnings. In this work, we have created a dataset gathering TAC, accelerometer, gyroscope, and heart rate data from the participants during a period of three weeks. This is the first study to combine accelerometer, gyroscope, and heart rate smartwatch data collected over an extended monitoring period to classify intoxication levels. Previous research had used limited smartphone motion data and conventional machine learning (ML) algorithms to classify heavy drinking episodes; in this work, we use smartwatch data and perform a thorough evaluation of different state-of-the-art classifiers such as the Transformer, Bidirectional Long Short-Term Memory (bi-LSTM), Gated Recurrent Unit (GRU), One-Dimensional Convolutional Neural Networks (1D-CNN), and Hyperdimensional Computing (HDC). We have compared performance metrics for the algorithms and assessed their efficiency on resource-constrained environments like mobile hardware. The HDC model achieved the best balance between accuracy and efficiency, demonstrating its practicality for smartwatch-based applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Intoxication Detection: A Smartwatch-Based Approach Segura, Manuel Vergés, Pere Ky, Richard Arangott, Ramesh Garcia, Angela Kristine Trong, Thang Dihn Hyodo, Makoto Nicolau, Alexandru Givargis, Tony Gago-Masague, Sergio Machine Learning Excess alcohol consumption leads to serious health risks and severe consequences for both individuals and their communities. To advocate for healthier drinking habits, we introduce a groundbreaking mobile smartwatch application approach to just-in-time interventions for intoxication warnings. In this work, we have created a dataset gathering TAC, accelerometer, gyroscope, and heart rate data from the participants during a period of three weeks. This is the first study to combine accelerometer, gyroscope, and heart rate smartwatch data collected over an extended monitoring period to classify intoxication levels. Previous research had used limited smartphone motion data and conventional machine learning (ML) algorithms to classify heavy drinking episodes; in this work, we use smartwatch data and perform a thorough evaluation of different state-of-the-art classifiers such as the Transformer, Bidirectional Long Short-Term Memory (bi-LSTM), Gated Recurrent Unit (GRU), One-Dimensional Convolutional Neural Networks (1D-CNN), and Hyperdimensional Computing (HDC). We have compared performance metrics for the algorithms and assessed their efficiency on resource-constrained environments like mobile hardware. The HDC model achieved the best balance between accuracy and efficiency, demonstrating its practicality for smartwatch-based applications. |
| title | Advancing Intoxication Detection: A Smartwatch-Based Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.09916 |