Modeling long-term human activeness using recurrent neural networks for biometric data.

Kim, Zae Myung; Oh, Hyungrai; Kim, Han-Gyu; Lim, Chae-Gyun; Oh, Kyo-Joong; Choi, Ho-Jin

Kim, Zae Myung; Oh, Hyungrai; Kim, Han-Gyu; Lim, Chae-Gyun; Oh, Kyo-Joong; Choi, Ho-Jin.

Affiliation

Kim ZM; School of Computing, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.
Oh H; Samsung Seoul R&D Campus, Samsung Electronics, 33 Seongchon-gil, Seocho-gu, Seoul, 06765, South Korea.
Kim HG; School of Computing, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.
Lim CG; School of Computing, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.
Oh KJ; School of Computing, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.
Choi HJ; School of Computing, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea. hojinc@kaist.ac.kr.

BMC Med Inform Decis Mak ; 17(Suppl 1): 57, 2017 May 18.

Article in En | MEDLINE | ID: mdl-28539116

Subject(s)

Biometry; Exercise; Fitness Trackers; Neural Networks, Computer; Adult; Algorithms; Computer Simulation; Datasets as Topic; Feasibility Studies; Forecasting; Humans; Time Factors; Young Adult

Key words

Activeness prediction; Calorie; Footstep; Heart rate; Recurrent neural network; Time series modeling

Fulltext

Add to My VHL

XML

PubMed Links

Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Exercise / Biometry / Neural Networks, Computer / Fitness Trackers Type of study: Prognostic_studies Limits: Adult / Humans Language: En Journal: BMC Med Inform Decis Mak Journal subject: INFORMATICA MEDICA Year: 2017 Document type: Article Affiliation country: South Korea

Fulltext

Add to My VHL

XML

PubMed Links

Search on Google