SENSOR-BASED HUMAN ACTIVITY RECOGNITION USING BIDIRECTIONAL LSTM FOR CLOSELY RELATED ACTIVITIES

SENSOR-BASED HUMAN ACTIVITY RECOGNITION USING BIDIRECTIONAL LSTM FOR CLOSELY RELATED ACTIVITIES

Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Sh...

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Bibliographic Details
Main Author: Pavai, Arumugam Thendramil
Format: Others
Published: CSUSB ScholarWorks 2018
Subjects:
deep learning
inertial sensors
human activity recognition
bidirectional long short-term memory
Artificial Intelligence and Robotics
Computer Engineering
Computer Sciences
Online Access:https://scholarworks.lib.csusb.edu/etd/776
https://scholarworks.lib.csusb.edu/cgi/viewcontent.cgi?article=1864&context=etd

Internet

https://scholarworks.lib.csusb.edu/etd/776
https://scholarworks.lib.csusb.edu/cgi/viewcontent.cgi?article=1864&context=etd

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