A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
Ramanujam, Thinagaran Perumal, and S
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Multilevel CNN-LSTM architectures using both late and intermediate feature fusion achieve higher accuracy in human activity recognition than late fusion alone on two benchmark datasets.
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Multilevel neural networks with dual-stage feature fusion for human activity recognition
Multilevel CNN-LSTM architectures using both late and intermediate feature fusion achieve higher accuracy in human activity recognition than late fusion alone on two benchmark datasets.