A Bottom-up method Towards the Automatic and Objective Monitoring of
Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors
- URL: http://arxiv.org/abs/2109.03475v1
- Date: Wed, 8 Sep 2021 07:50:47 GMT
- Title: A Bottom-up method Towards the Automatic and Objective Monitoring of
Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors
- Authors: Athanasios Kirmizis, Konstantinos Kyritsis and Anastasios Delopoulos
- Abstract summary: Tobacco consumption has reached global epidemic proportions and is characterized as the leading cause of death and illness.
We present a two-step, bottom-up algorithm towards the automatic and objective monitoring of cigarette-based, smoking behavior during the day.
In the first step, our algorithm performs the detection of individual smoking gestures (i.e., puffs) using an artificial neural network with both convolutional and recurrent layers.
In the second step, we make use of the detected puff density to achieve the temporal localization of smoking sessions that occur throughout the day.
- Score: 6.955421797534318
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The consumption of tobacco has reached global epidemic proportions and is
characterized as the leading cause of death and illness. Among the different
ways of consuming tobacco (e.g., smokeless, cigars), smoking cigarettes is the
most widespread. In this paper, we present a two-step, bottom-up algorithm
towards the automatic and objective monitoring of cigarette-based, smoking
behavior during the day, using the 3D acceleration and orientation velocity
measurements from a commercial smartwatch. In the first step, our algorithm
performs the detection of individual smoking gestures (i.e., puffs) using an
artificial neural network with both convolutional and recurrent layers. In the
second step, we make use of the detected puff density to achieve the temporal
localization of smoking sessions that occur throughout the day. In the
experimental section we provide extended evaluation regarding each step of the
proposed algorithm, using our publicly available, realistic Smoking Event
Detection (SED) and Free-living Smoking Event Detection (SED-FL) datasets
recorded under semi-controlled and free-living conditions, respectively. In
particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of
0.863 for the detection of puffs and an F1-score/Jaccard index equal to
0.878/0.604 towards the temporal localization of smoking sessions during the
day. Finally, to gain further insight, we also compare the puff detection part
of our algorithm with a similar approach found in the recent literature.
Related papers
- Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations.
In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
arXiv Detail & Related papers (2023-08-31T05:05:53Z) - On the Universal Adversarial Perturbations for Efficient Data-free
Adversarial Detection [55.73320979733527]
We propose a data-agnostic adversarial detection framework, which induces different responses between normal and adversarial samples to UAPs.
Experimental results show that our method achieves competitive detection performance on various text classification tasks.
arXiv Detail & Related papers (2023-06-27T02:54:07Z) - Application-Driven AI Paradigm for Hand-Held Action Detection [1.8531114735719274]
We propose an application-driven AI paradigm for hand-held action detection based on hierarchical object detection.
The proposed framework achieve higher detection rate with good adaptation and robustness in complex environments.
arXiv Detail & Related papers (2022-10-13T02:30:23Z) - Fake It Till You Make It: Near-Distribution Novelty Detection by
Score-Based Generative Models [54.182955830194445]
existing models either fail or face a dramatic drop under the so-called near-distribution" setting.
We propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data.
Our method improves the near-distribution novelty detection by 6% and passes the state-of-the-art by 1% to 5% across nine novelty detection benchmarks.
arXiv Detail & Related papers (2022-05-28T02:02:53Z) - Interpretable Feature Learning Framework for Smoking Behavior Detection [0.0]
Interpretable feature learning framework for smoking behavior detection utilizing a Deep Learning VGG-16 pretrained network.
Technology can also detect other smokeable drugs like weed, shisha, marijuana etc.
arXiv Detail & Related papers (2021-12-12T11:05:35Z) - Project Achoo: A Practical Model and Application for COVID-19 Detection
from Recordings of Breath, Voice, and Cough [55.45063681652457]
We propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices.
The approach combines signal processing methods with fine-tuned deep learning networks and provides methods for signal denoising, cough detection and classification.
We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection.
arXiv Detail & Related papers (2021-07-12T08:07:56Z) - STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection [52.648906951532155]
We propose a novel Spatio-Temporal Cross Network (STCNet) to recognize industrial smoke emissions.
The proposed STCNet involves a spatial to extract texture features and a temporal pathway to capture smoke motion information.
We show that our STCNet achieves clear improvements on the challenging RISE industrial smoke detection dataset against the best competitors by 6.2%.
arXiv Detail & Related papers (2020-11-10T02:28:47Z) - A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating
Behavior Using Smartwatches [8.257740966456172]
This paper presents a complete framework towards the automated i) modeling of in-meal eating behavior and ii) temporal localization of meals.
We present an end-to-end Neural Network which detects food intake events (i.e. bites)
We show how the distribution of the detected bites throughout the day can be used to estimate the start and end points of meals, using signal processing algorithms.
arXiv Detail & Related papers (2020-10-12T12:35:56Z) - Recognition of Smoking Gesture Using Smart Watch Technology [0.18472148461613155]
Early identification of smoking gestures can help to initiate the appropriate intervention method and prevent relapses in smoking.
Our experiments indicate 85%-95% success rates in identification of smoking gesture.
We have demonstrated the possibility of using smart watches to perform continuous monitoring of daily activities.
arXiv Detail & Related papers (2020-03-05T16:05:49Z) - A Robust Functional EM Algorithm for Incomplete Panel Count Data [66.07942227228014]
We propose a functional EM algorithm to estimate the counting process mean function under a missing completely at random assumption (MCAR)
The proposed algorithm wraps several popular panel count inference methods, seamlessly deals with incomplete counts and is robust to misspecification of the Poisson process assumption.
We illustrate the utility of the proposed algorithm through numerical experiments and an analysis of smoking cessation data.
arXiv Detail & Related papers (2020-03-02T20:04:38Z) - State Transition Modeling of the Smoking Behavior using LSTM Recurrent
Neural Networks [0.2294014185517203]
In this study, we focus on the use of smartwatch sensors to recognize smoking activity.
Our presented reformulation of the smoking gesture as a state-transition model has demonstrated improvement in detection rates nearing 100%.
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%.
arXiv Detail & Related papers (2020-01-07T15:06:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.