An automatic network structure search via channel pruning for accelerating human activity inference on mobile devices
During recent years, deep convolutional neural networks (CNNs) have become increasingly
popular in sensor-based human activity recognition (HAR), due to powerful feature …
popular in sensor-based human activity recognition (HAR), due to powerful feature …
Deep ensemble learning approach for lower limb movement recognition from multichannel sEMG signals
Walking is a complex task that requires consistent practice to master, and it involves the
synchronisation between the lower limbs and the brain, making it challenging. While bipedal …
synchronisation between the lower limbs and the brain, making it challenging. While bipedal …
An intelligent framework based on optimized variational mode decomposition and temporal convolutional network: Applications to stock index multi-step forecasting
Y Yu, D Dai, Q Yang, Q Zeng, Y Lin, Y Chen - Expert Systems with …, 2024 - Elsevier
The stock market is often subjected to external environment changes, which results in
significant characteristics of stock indices such as high volatility, nonlinearity, and complex …
significant characteristics of stock indices such as high volatility, nonlinearity, and complex …
A novel physical activity recognition approach using deep ensemble optimized transformers and reinforcement learning
In recent years, human physical activity recognition has increasingly attracted attention from
different research fields such as healthcare, computer-human interaction, lifestyle …
different research fields such as healthcare, computer-human interaction, lifestyle …
ASK-HAR: Attention-Based Multi-Core Selective Kernel Convolution Network for Human Activity Recognition
X Yu, MAA Al-qaness - Measurement, 2025 - Elsevier
Abstract Human Activity Recognition (HAR) is an increasingly popular field of study aimed at
automatically identifying and categorizing human movements and activities using various …
automatically identifying and categorizing human movements and activities using various …
[HTML][HTML] WISNet: A deep neural network based human activity recognition system
Abstract Nowadays, Human Activity Recognition (HAR) is a key research area with many
ubiquitous innovative solutions, where both accelerometer and gyroscope data provide …
ubiquitous innovative solutions, where both accelerometer and gyroscope data provide …
LMSFF: Lightweight multi-scale feature fusion network for image recognition under resource-constrained environments
Y Liu, H Liang, S Zhao - Expert Systems with Applications, 2025 - Elsevier
In many resource-constrained environments, recognition tasks often require efficient and fast
execution. Currently, many methods designed for this field adopt a combination of …
execution. Currently, many methods designed for this field adopt a combination of …
Deep multimodal fusion model for moisture content measurement of sand gravel using images, NIR spectra, and dielectric data
Q Yuan, J Wang, B Wu, M Zheng, X Wang, H Liang… - Measurement, 2024 - Elsevier
A fast and accurate moisture content (MC) measurement of sand gravel is essential for
hydraulic engineering project sites. Most existing measurement methods are unimodal …
hydraulic engineering project sites. Most existing measurement methods are unimodal …
A hybrid TCN-GRU model for classifying human activities using smartphone inertial signals
S Raja Sekaran, YH Pang, LZ You, OS Yin - Plos one, 2024 - journals.plos.org
Recognising human activities using smart devices has led to countless inventions in various
domains like healthcare, security, sports, etc. Sensor-based human activity recognition …
domains like healthcare, security, sports, etc. Sensor-based human activity recognition …
Weighted voting ensemble of hybrid CNN-LSTM Models for vision-based human activity recognition
S Aggarwal, G Bhola, DK Vishwakarma - Multimedia Tools and …, 2024 - Springer
This research work aims to propose an ensemble model of different pre-trained CNN
networks combined with LSTM to detect a set of routine human activities practiced by the …
networks combined with LSTM to detect a set of routine human activities practiced by the …