Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

ABSTRACT: Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied pa

SUBMITTER: Capela NA 

PROVIDER: S-EPMC4401457 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature