Despite the association between tobacco use and the harmful effects on general health as
well as male fertility parameters, smoking remains globally prevalent. The main content of …

Objective Identify and review the body of tobacco research literature that self-identified as
using machine learning (ML) in the analysis. Data sources MEDLINE, EMABSE, PubMed …

Wearable technology has advanced significantly and is now used in various entertainment
and business contexts. Authentication methods could be trustworthy, transparent, and non …

Smoking is a major cause of cancer, heart disease and other afflictions that lead to early
mortality. An effective smoking classification mechanism that provides insights into individual …

Fitness trackers are increasingly popular. The data they collect provides substantial benefits
to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker …

Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data
from activity classes that are not represented in the training data of a Machine Learning (ML) …

Mobile health technologies are being developed for personal lifestyle and medical
healthcare support, of which a growing number are designed to assist smokers to quit. The …

Continuous monitoring of complex activities is valuable for understanding human behavior
and providing activity-aware services. At the same time, recognizing these activities requires …

Smoking is associated with cancer, cardiovascular disease and premature death and can
cause severe fire hazards. To assist with smoking cessation, this paper presents a wireless …

Abstract Background Human activity Recognition (HAR) using smartphone sensors suffers
from two major problems: sensor orientation and placement. Sensor orientation and sensor …