Smartphone-Based Ubiquitous Data Sensing and Analysis for Personalized Preventive Care: A Conceptual Framework

Thakur, Saurabh; Roy, Ram Babu

doi:10.1007/978-981-13-1132-1_10

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Keeping in mind the general psychology of smoking behavior, a solution is envisaged in which a near real-time intervention could help in smoking cessation [55,60] by motivating users for behavior change [8,37,45,61]. Body wearables like smartwatches, bands, etc.…”

Section: Background and Motivationmentioning

confidence: 99%

“…It opens doors for the physicians and the patients to exchange information quickly and transparently, crossing geographical limits. Effective use of technology can cater to alleviating the hurdles in delivering effective treatment to the patients [58,61]. Recently, a significant number of studies conducted on quit smoking, which uses mobile applications or wearable devices.…”

Section: Review Of Smartphone-based Applications For Promoting Smokin...mentioning

confidence: 99%

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Real-time prediction of smoking activity using machine learning based multi-class classification model

Thakur

Poddar

Roy

2022

Multimed Tools Appl

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Smoking cessation efforts can be greatly influenced by providing just-in-time intervention to individuals who are trying to quit smoking. Detecting smoking activity accurately among the confounding activities of daily living (ADLs) being monitored by the wearable device is a challenging and intriguing research problem. This study aims to develop a machine learning based modeling framework to identify the smoking activity among the confounding ADLs in real-time using the streaming data from the wrist-wearable IMU (6axis inertial measurement unit) sensor. A low-cost wrist-wearable device has been designed and developed to collect raw sensor data from subjects for the activities. A sliding window mechanism has been used to process the streaming raw sensor data and extract several time-domain, frequency-domain, and descriptive features. Hyperparameter tuning and feature selection have been done to identify best hyperparameters and features respectively. Subsequently, multi-class classification models are developed and validated using in-sample and out-of-sample testing. The developed models obtained predictive accuracy (area under receiver operating curve) up to 98.7% for predicting the smoking activity. The findings of this study will lead to a novel application of wearable devices to accurately detect smoking activity in real-time. It will further help the healthcare professionals in monitoring their patients who are smokers by providing just-in-time intervention to help them quit smoking. The application of this framework can be extended to more preventive healthcare use-cases and detection of other activities of interest.

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