Human activity classification using long short-term memory network

Human activity classification using long short-term memory network

Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural net...

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Bibliographic Details
Main Authors: Malshika Welhenge, Anuradhi, Taparugssanagorn, A.
Format: Journal Article
Language:English
Published: SPRINGER LONDON LTD 2019
Subjects:
Science & Technology
Technology
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Engineering
Deep learning
ADL
LSTM
RNN
TRIAXIAL ACCELEROMETER
FALL DETECTION
Online Access:http://hdl.handle.net/20.500.11937/90320
Description
Summary:Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural networks, is used. Data collected in MobiAct data set are used to train and test the network. An accuracy of 0.90 is achieved using LSTM network.

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