TL;DR
These results demonstrate the models’ respective strengths in capturing long-range temporal dependencies, bidirectional temporal patterns, and short-term temporal dynamics with cross-channel interactions in complex motion patterns.
An attention-based CNN–LSTM–Attention architecture for smartphone-based HAR using accelerometer signals is proposed, confirming that convolutional feature extraction, recurrent temporal modeling, and attention-based weighting improve the reliability of accelerometer-based HAR while maintaining practical computational e...
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.
Human Activity Recognition through smartphones plays a crucial role in several medical states of affairs such as patient monitoring, elder-care, and post-surgery recovery. Most of these works require precisely labelled smartphone accelerometer data for training supervised learning classifiers. However, precise data ann...
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 a crucial subject in the field of intelligent healthcare, smart environments, assisted living systems and context-aware decision-making applications. Radio-enabled smartphones and wearable devices have made it possible to gather multimodal sensor data continuously, offering novel pos...
The findings show that accelerometers, gyroscopes, inertial measurement units, smartphones, smartwatches, and wearable sensors are the dominant data sources for HAR at the edge and a gradual shift from accuracy-centered evaluation toward deployment-aware assessment, incorporating latency, inference time, energy consump...
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