Abstract
Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. An accelerometer was popular sensors to recognize the activity, as well as a gyroscope, which can be embedded in a smartphone. Signal was generated from the accelerometer as a time-series data is an actual approach like a human actifvity pattern. Motion data have acquired in 30 volunteers. Dynamic actives (walking, walking upstairs, walking downstairs) as DA and static actives (laying, standing, sitting) as SA were collected from volunteers. SA and DA it's a challenging problem with the different signal patterns, SA signals coincide between activities but with a clear threshold, otherwise the DA signal is clearly distributed but with an adjacent upper threshold. The proposed network structure achieves a significant performance with the best overall accuracy of 97% . The result indicated the ability of the model for human activity recognition purposes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: TELKOMNIKA (Telecommunication Computing Electronics and Control)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
