Human Activity Recognition in Construction Industry Using Machine Learning Pose Estimation Technique

Author:

Manoj Kumar M.ORCID,Hegde Bhuvaneshwari,Veda Murthy S. P.,Akhila M. K.,Bhoomika A. S.

Publisher

Springer Nature Singapore

Reference11 articles.

1. Chakravarthi B, Patil AK, Ryu JY, Balasubramanyam A, Chai YH (2022) Scenario-based sensed human motion editing and validation through the motion-sphere. IEEE Access 10:28295–28307

2. Chakravarthi B, Prasad BP, Cethana B, Kumar BP (2022) Real-time human motion tracking and reconstruction using IMU sensors. In: 2022 ınternational conference on electrical, computer and energy technologies (ICECET). IEEE, pp 1–5

3. Maurya R, Teo TH, Chua SH, Chow H-C, Wey I-C (2022) Complex human activities recognition based on high performance 1D CNN model. In: IEEE 15th international symposium on embedded multicore/many-core systems-on-chip (MCSoC)

4. Chakravarthi B et al (2019) Quaternions based intuitive visualization for tracking weightlifting exercises. In: Korean society of information science and technology academic conference, pp 1052–1054

5. Sheu M-H, Salahuddhin Morsalin SM, Hsu C-C, Lai S-C, Wang S-H, Chang C-Y (2023) Improvement of human pose estimation and processing with the intensive feature consistency network. IEEE Access. 110-2221-E-224-052-MY2

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