Towards a Modular Machine Learning Architecture for Human Activity Recognition
Author:
Affiliation:
1. FZI Research Center for Information Technology,Dept. of Embedded Systems and Sensors Engineering,Karlsruhe,Germany
2. Institute of Information Processing Technologies, Karlsruhe Institute of Technology KIT,Karlsruhe,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10636309/10636356/10636463.pdf?arnumber=10636463
Reference15 articles.
1. United Nations, Department of Economic and Social Affairs, Population Division;World Population Prospects 2022: Summary of Results. UN DESA/POP/2022/TRlNO. 3,2022
2. A Review of Human Activity Recognition Methods
3. Different approaches for human activity recognition: A survey;Zawar;arXiv preprint,2019
4. Human Activity Recognition Using Deep Learning: A Sur-vey;Marutirao Nanche;International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT),2023
5. Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks
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