Abstract
Human activity recognition (HAR) is an important research field with a variety of applications in healthcare monitoring, fitness tracking and in user-adaptive systems in smart environments. The performance of the activity recognition system is highly dependent on the features extracted from the sensor data which makes the selection of appropriate features a very important part of HAR. A 1D CNN model trained on accelerometer data is suggested in the paper for automatic feature extraction in a HAR system. A semi-automatic approach is used that effectively and efficiently determines the number of convolutional layers in the network, the number of kernels and the size of the kernels. The experimental results show that the suggested model outperforms several existing recognition approaches that use the same data set.
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: IOP Conference Series: Materials Science and Engineering
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.
