Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition.

ABSTRACT: Participatory exposure research, which tracks behaviour and assesses exposure to stressors like air pollution, traditionally relies on time-activity diaries. This study introduces a novel approach, employing machine learning (ML) to empower laypersons in human activity recognition (HAR), aiming to reduce dependence on manual recording by leveraging data from wearable sensors. Recognising complex activities such as smoking and cooking presents unique challenges due to specific environmental conditions. In this research, we combined wearable environment/ambient and wrist-worn activity/biometric sensors for complex activity recognition in an urban stressor exposure study, measuring parameters like particulate matter concentrations, temperature, and humidity. Two groups, Group H (88 individual

SUBMITTER: Novak R 

PROVIDER: S-EPMC10747712 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature