State Transition Modeling of the Smoking Behavior using LSTM Recurrent Neural Networks

The use of sensors has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch sensors to recognize smoking activity. More specifically, we have reformulated the previous work in detection o...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-01
Main Authors: Odhiambo, Chrisogonas O, Cole, Casey A, Torkjazi, Alaleh, Valafar, Homayoun
Format: Article
Language:English
Subjects:
Activity recognition
Neural networks
Recurrent neural networks
Sensors
Smoking
Social networks
Transportation networks
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The use of sensors has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch sensors to recognize smoking activity. More specifically, we have reformulated the previous work in detection of smoking to include in-context recognition of smoking. Our presented reformulation of the smoking gesture as a state-transition model that consists of the mini-gestures hand-to-lip, hand-on-lip, and hand-off-lip, has demonstrated improvement in detection rates nearing 100% using conventional neural networks. In addition, we have begun the utilization of Long-Short-Term Memory (LSTM) neural networks to allow for in-context detection of gestures with accuracy nearing 97%.
ISSN:2331-8422