Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data.

ABSTRACT:

Background

Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity.

Objective

We aimed to create an HAR framework adapted to variab

SUBMITTER: Li K 

PROVIDER: S-EPMC6386646 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature