Background: Application of machine learning for classifying human behavior is increasingly
common as access to raw accelerometer data improves. The aims of this scoping review are …

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that
were recorded during free living suffer from non-fixed sensor placement, the usage of only …

Activity monitoring combined with machine learning (ML) methods can contribute to detailed
knowledge about daily physical behavior in older adults. The current study (1) evaluated the …

Long-form audio recordings have had a transformational effect on the study of infant
language acquisition by using mobile, unobtrusive devices to gather full-day, real-time data …

Physical activity in children and youth is associated with physiological, physical, and mental
health benefits with research suggesting that the more physical activity one accumulates, the …

With the promotion of physical activity in young people, an established global health priority
[1], it is critically important for governments and public health agencies to have a clear …

Background The time that children spend in physical activity, sedentary behaviour, and
sleep each day (ie, 24-h time-use behaviours), is related to physical and mental health …

Introduction: Accelerometer-based measurements of physical activity types are commonly
used to replace self-reports. To advance the field, it is desirable that such measurements …

How can researchers best measure infants' motor experiences in the home? Body position—
whether infants are held, supine, prone, sitting, or upright—is an important developmental …

Aim and objectives To assess intensive care nurses' resilience and identify associated
personal factors and physical activity behaviours using a job demands–recovery framework …