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 …
Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review
E Ramanujam, T Perumal… - IEEE Sensors Journal, 2021 - ieeexplore.ieee.org
Human Activity Recognition (HAR) is a field that infers human activities from raw time-series
signals acquired through embedded sensors of smartphones and wearable devices. It has …
signals acquired through embedded sensors of smartphones and wearable devices. It has …
Human activity recognition based on multienvironment sensor data
With the development of artificial intelligence and the broad application of sensors, human
activity recognition (HAR) technologies based on noninvasive environmental sensors have …
activity recognition (HAR) technologies based on noninvasive environmental sensors have …
Deep-learning-enhanced multitarget detection for end–edge–cloud surveillance in smart IoT
Along with the rapid development of cloud computing, IoT, and AI technologies, cloud video
surveillance (CVS) has become a hotly discussed topic, especially when facing the …
surveillance (CVS) has become a hotly discussed topic, especially when facing the …
Multi-level feature fusion for multimodal human activity recognition in Internet of Healthcare Things
Abstract Human Activity Recognition (HAR) has become a crucial element for smart
healthcare applications due to the fast adoption of wearable sensors and mobile …
healthcare applications due to the fast adoption of wearable sensors and mobile …
Vision-based human activity recognition: a survey
Human activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …
human activities using acquired information from various types of sensors. Although several …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
Advances in multimodal data fusion in neuroimaging: overview, challenges, and novel orientation
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …
A survey on video-based human action recognition: recent updates, datasets, challenges, and applications
Abstract Human Action Recognition (HAR) involves human activity monitoring task in
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …
made it possible to endow machines/computers with the ability of emotion understanding …