Human activity classification using long short-term memory network
Citation
Malshika Welhenge, A. and Taparugssanagorn, A. 2019. Human activity classification using long short-term memory network. Signal, Image and Video Processing. 13 (4): pp. 651-656.
Source Title
Signal, Image and Video Processing
ISSN
Collection
Abstract
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.
Related items
Showing items related by title, author, creator and subject.
-
Chai, Pey San Nancy (2011)Backhaul networks are used to interconnect access points and further connect them to gateway nodes which are located in regional or metropolitan centres. Conventionally, these backhaul networks are established using ...
-
Ma, Victor Kee Kin (2009)Most small new firms face problems in surviving the gestation process and achieving a viable performance thereafter because of the very fact of their smallness and newness. Due to a lack of internal resources, entrepreneurs ...
-
Mostafa, Fahed. (2011)Market risk refers to the potential loss that can be incurred as a result of movements inmarket factors. Capturing and measuring these factors are crucial in understanding andevaluating the risk exposure associated with ...