Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network

Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities. Howev...

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Bibliographic Details
Main Authors: Odongo Steven Eyobu, Dong Seog Han
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Sensors
Subjects:
human activity recognition
data augmentation
feature representation
deep learning
long short term memory
inertial measurement unit sensor
Online Access:http://www.mdpi.com/1424-8220/18/9/2892

Internet

http://www.mdpi.com/1424-8220/18/9/2892

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