Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation.

ABSTRACT:

Background

For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on feature engineering and machine learning algorithms. Time-domain and frequency-domain features are extracted from original time series data collected by sensor nodes. For high-dimensional data, feature selection plays an important role in improving the performance of motion recognition. Existing feature selection methods can be categorized into filter and wrapper methods. Wrapper methods usually achieve better performance than filter methods; however, in most cases, they are computationally intensive, and the feature subset obtained is u

SUBMITTER: Li Q 

PROVIDER: S-EPMC8446846 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature