ArticleBiomedical engineering letters2020
A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors.
Article in Biomedical engineering letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular Rate Signals.Sensors (Basel, Switzerland) · 2025Article
- Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2024Article
- Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances.Sensors (Basel, Switzerland) · 2022Review
- CNN-based bi-directional and directional long-short term memory network for determination of face mask.Biomedical signal processing and control · 2022Article
- Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency.Sensors (Basel, Switzerland) · 2021Article
- Dynamic models of stress-smoking responses based on high-frequency sensor data.NPJ digital medicine · 2021Article
- OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
A detailed assessment of smoking behavior under free-living conditions is a key challenge for health behavior research. A number of methods using wearable sensors and puff topography devices have been developed for smoking and individual puff detection. In this paper, we propose a novel algorithm for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors. The detection of puffs was performed by using a deep network containing convolutional and recurrent neural networks. Convolutional neural networks (CNN) were utilized to automate feature learning from raw sensor streams. Long Short Term Memory (LSTM) network layers were utilized to obtain the temporal dynamics of sensor signals and classify sequence of time segmented sensor streams. An evaluation was performed by using a large, challenging dataset containing 467 smoking events from 40 participants under free-living conditions. The proposed approach achieved an F1-score of 78% in leave-one-subject-out cross-validation. The results suggest that CNN-LSTM based neural network architecture sufficiently detect puffing episodes in free-living condition. The proposed model be used as a detection tool for smoking cessation programs and scientific research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.