Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data.

ABSTRACT:

Background

There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing.

Methods

The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobili

SUBMITTER: Giri S 

PROVIDER: S-EPMC9667683 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature