Subject: activity recognition; LSTM, smartphone; wearable
Year: 2018
Type: Proceeding article
Title: Real time human activity recognition on smartphones using LSTM networks
Author: Milenkoski, Martin
Author: Trivodaliev, Kire
Author: Kalajdziski, Slobodan
Author: Jovanov, Mile
Author: Risteska Stojkoska, Biljana
Abstract: Activity detection is becoming an integral part of many mobile applications. Therefore, the algorithms for this purpose should be lightweight to operate on mobile or other wearable device, but accurate at the same time. In this paper, we develop a new lightweight algorithm for activity detection based on Long Short Term Memory networks, which is able to learn features from raw accelerometer data, completely bypassing the process of generating hand-crafted features. We evaluate our algorithm on data collected in controlled setting, as well as on data collected under field conditions, and we show that our algorithm is robust and performs almost equally good for both scenarios, while outperforming other approaches from the literature.
Publisher: IEEE
Relation: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
Identifier: oai:repository.ukim.mk:20.500.12188/20415
Identifier: http://hdl.handle.net/20.500.12188/20415
