Sujittra Sarakon1, Wansuree Massagram1,2, Kreangsak Tamee1,3,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2109-2136, 2025, DOI:10.32604/cmc.2025.058906
- 17 February 2025
Abstract This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP More >
Login
Register
