Automatic Feature Extraction for Human Activity Recognitionon the Edge

Automatic Feature Extraction for Human Activity Recognitionon the Edge

This thesis evaluates two methods for automatic feature extraction to classify the accelerometer data of periodic and sporadic human activities. The first method selects features using individual hypothesis tests and the second one is using a random forest classifier as an embedded feature selector....

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Bibliographic Details
Main Authors: Cleve, Oscar, Gustafsson, Sara
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2019
Subjects:
Human Activity Recognition
Automatic Feature Extraction
Automatic Feature Selection
Automated Machine Learning
Random Forest Classifier
Hypothesis Test
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260247

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260247

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