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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.12323 |
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| _version_ | 1866914719565086720 |
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| author | Segura, Manuel E. Verges, Pere Chen, Justin Tian Jin Arangott, Ramesh Garcia, Angela Kristine Reynoso, Laura Garcia Nicolau, Alexandru Givargis, Tony Gago-Masague, Sergio |
| author_facet | Segura, Manuel E. Verges, Pere Chen, Justin Tian Jin Arangott, Ramesh Garcia, Angela Kristine Reynoso, Laura Garcia Nicolau, Alexandru Givargis, Tony Gago-Masague, Sergio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12323 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional Computing on Embedded Devices Segura, Manuel E. Verges, Pere Chen, Justin Tian Jin Arangott, Ramesh Garcia, Angela Kristine Reynoso, Laura Garcia Nicolau, Alexandru Givargis, Tony Gago-Masague, Sergio Machine Learning 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. |
| title | Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional Computing on Embedded Devices |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.12323 |