Random forest algorithms for recognizing daily life activities using plantar pressure information: a smart-shoe study.

ABSTRACT:

Background

Wearable activity trackers are regarded as a new opportunity to deliver health promotion interventions. Indeed, while the prediction of active behaviors is currently primarily relying on the processing of accelerometer sensor data, the emergence of smart clothes with multi-sensing capacities is offering new possibilities. Algorithms able to process data from a variety of smart devices and classify daily life activities could therefore be of particular importance to achieve a more accurate evaluation of physical behaviors. This study aims to (1) develop an activity recognition algorithm based on the processing of plantar pressure information provided by a smart-shoe prototype and (2) to determine the optimal hardware and software configurations.

Method

Seventeen su

SUBMITTER: Ren D 

PROVIDER: S-EPMC7602692 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature