Motion Artifact Detection and Classification for Unobtrusive Cardiorespiratory Signals Using Machine Learning.
Onno LinschmannCarl RevanderSteffen LeonhardtMarkus J. LükenPublished in: CinC (2022)
Keyphrases
- machine learning
- pattern recognition
- text classification
- decision trees
- support vector machine
- feature selection
- machine learning methods
- machine learning algorithms
- supervised machine learning
- supervised learning
- classification accuracy
- image classification
- detection rate
- unsupervised learning
- motion analysis
- machine learning approaches
- optical flow
- motion model
- classification algorithm
- training set
- motion detection
- supervised classification
- learning algorithm
- false positives
- class labels
- low signal to noise ratio
- detection method
- detection algorithm
- model selection
- mobile robot
- active learning
- moving objects
- support vector
- training data
- phase locked
- weak signal
- shot detection
- computer vision
- image sequences
- eeg signals
- feature extraction
- knn
- classification method
- object detection
- signal processing
- support vector machine svm