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Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges
This paper firstly introduces common wearable sensors, smart wearable devices and the key
application areas. Since multi-sensor is defined by the presence of more than one model or …
application areas. Since multi-sensor is defined by the presence of more than one model or …
Deep learning in human activity recognition with wearable sensors: A review on advances
Mobile and wearable devices have enabled numerous applications, including activity
tracking, wellness monitoring, and human–computer interaction, that measure and improve …
tracking, wellness monitoring, and human–computer interaction, that measure and improve …
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
Z Zhao, S Yun, L Jia, J Guo, Y Meng, N He, X Li… - … Applications of Artificial …, 2023 - Elsevier
Accurate and reliable short-term forecasting of wind power is vital for balancing energy and
integrating wind power into a grid. A novel hybrid deep learning model is designed in this …
integrating wind power into a grid. A novel hybrid deep learning model is designed in this …
[HTML][HTML] A systematic review of data fusion techniques for optimized structural health monitoring
Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing
Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving,
ubiquitous human-sensing applications, enabled by signal processing and deep-learning …
ubiquitous human-sensing applications, enabled by signal processing and deep-learning …
Inception inspired CNN-GRU hybrid network for human activity recognition
Abstract Human Activity Recognition (HAR) involves the recognition of human activities
using sensor data. Most of the techniques for HAR involve hand-crafted features and hence …
using sensor data. Most of the techniques for HAR involve hand-crafted features and hence …
[HTML][HTML] Human activity recognition based on residual network and BiLSTM
Y Li, L Wang - Sensors, 2022 - mdpi.com
Due to the wide application of human activity recognition (HAR) in sports and health, a large
number of HAR models based on deep learning have been proposed. However, many …
number of HAR models based on deep learning have been proposed. However, many …
Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor
A bipedal walking robot is a kind of humanoid robot. It is suppose to mimics human behavior
and designed to perform human specific tasks. Currently, humanoid robots are not capable …
and designed to perform human specific tasks. Currently, humanoid robots are not capable …
[HTML][HTML] Wearable sensors for activity monitoring and motion control: A review
Wearable sensors for activity monitoring currently are being designed and developed,
driven by an increasing demand in health care for noninvasive patient monitoring and …
driven by an increasing demand in health care for noninvasive patient monitoring and …
Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm
Capturing time and frequency relationships of time series signals offers an inherent barrier
for automatic human activity recognition (HAR) from wearable sensor data. Extracting …
for automatic human activity recognition (HAR) from wearable sensor data. Extracting …