Behavioural Smoking Identification via Hand-Movement Dynamics
| Autor: | Alex Barret-Chapman, Maryam Abo-Tabik, Yael Benn, Nicholas Costen, Mohamed Benouis, Olivia Salmon |
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| Rok vydání: | 2019 |
| Předmět: |
050101 languages & linguistics
Artificial neural network business.industry Computer science Local binary patterns 05 social sciences Probabilistic logic Wearable computer Gyroscope 02 engineering and technology Machine learning computer.software_genre Accelerometer law.invention Identification (information) Dynamics (music) law 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business computer |
| Zdroj: | SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI |
| DOI: | 10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00309 |
| Popis: | Smoking is a commonly observed habit worldwide, and is a major cause of disease leading to death. Many techniques have been established in medical and psychological science to help people quit smoking. However, the existing systems are complex, and usually expensive. Recently, wearable sensors and mobile application have become an alternative method of improving health. We propose a human behavioural classification based on the use of a one-dimensional local binary pattern (LBP), combined with a Probabilistic Neural Net (PNN) to differentiate smoking from other movements as measured from a wearable device. Human activity signals were recorded from two sets of 6 and 11 participants, using a smart phones equipped with an accelerometer and gyroscope mounted on a wrist module. The data combined structured and naturalistic scenarios. The pro- posed architecture was compared to previously studied machine learning algorithms and found to out-perform them, exhibiting ceiling level performance. |
| Databáze: | OpenAIRE |
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