TL;DR
A Correlation-Centroid Reduction (CCR) method to reduce feature complexity and noise, and ARCL-Net, a hybrid deep learning model integrating convolutional, residual, LSTM, and attention layers for improved HAR performance are introduced.
This work proposes MSCALNet, a Multi-Scale Convolutional Attention LSTM Network, a Multi-Scale Convolutional Attention LSTM Network that employs a multi-branch differential encoding strategy to fuse heterogeneous sensor information, and efficiently models multi-timescale dynamics through a dilated convolutional pyramid...
Findings provide a leakage-resistant but selection-sensitive benchmark for subject-independent DeepConvLSTM evaluation on WISDM and showed an observed class-level trade-off relative to last-timestep pooling in the evaluated comparison rather than a general architectural advantage.
A resource-constrained CNN-GRU hybrid model for HAR on the WISDM dataset that uses convolutional layers for spatial learning and gated recurrent units (GRU) for sequence learning, enabling real-time HAR on edge devices.
A three-stage Adaptive CNN-Enhanced Scale-fusion Network (ACESNet) for six-channel accelerometer–gyroscope activity recognition supports a favorable performance–efficiency trade-off relative to the selected baselines under the stated protocol.
Human activity recognition (HAR) is increasingly important for healthcare, well-being, and daily monitoring ap- plications, for which detecting alimentary activities such as eating and drinking can provide actionable insight into dietary habits and chronic disease management. HAR systems, however, often underperform on...
A novel action recognition method, named MICA-Net, which combines data from multiple sensors to improve the efficiency of the HAR model, and a new compact version of a wrist-worn sensor device with Wi-Fi connectivity to an edge device, enhancing usability in human-machine interaction applications.
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