[HTML][HTML] A systematic review of smartphone-based human activity recognition methods for health research
Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous
measurement of activities of daily living, making them especially well-suited for health …
measurement of activities of daily living, making them especially well-suited for health …
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 …
A systematic literature review on multimodal machine learning: Applications, challenges, gaps and future directions
Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
Human activity recognition in IoHT applications using arithmetic optimization algorithm and deep learning
Nowadays, people use smart devices everywhere and for different applications such as
healthcare. The Internet of Healthcare Things (IoHT) generates enormous amounts of data …
healthcare. The Internet of Healthcare Things (IoHT) generates enormous amounts of data …
Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds
M Gjoreski, A Gradišek, B Budna, M Gams… - Ieee …, 2020 - ieeexplore.ieee.org
Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is
increasing by 2% annually. Despite the significant burden that CHF poses and despite the …
increasing by 2% annually. Despite the significant burden that CHF poses and despite the …
Deep learning based fall detection using smartwatches for healthcare applications
We implement a smart watch-based system to predict fall detection. We differentiate fall
detection from four common daily activities: sitting, squatting, running, and walking …
detection from four common daily activities: sitting, squatting, running, and walking …
Distributional and spatial-temporal robust representation learning for transportation activity recognition
Transportation activity recognition (TAR) provides valuable support for intelligent
transportation applications, such as urban transportation planning, driving behavior …
transportation applications, such as urban transportation planning, driving behavior …
Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals
It is only a matter of time until autonomous vehicles become ubiquitous; however, human
driving supervision will remain a necessity for decades. To assess the driver's ability to take …
driving supervision will remain a necessity for decades. To assess the driver's ability to take …
[HTML][HTML] 1D Convolution approach to human activity recognition using sensor data and comparison with machine learning algorithms
K Muralidharan, A Ramesh, G Rithvik, S Prem… - International Journal of …, 2021 - Elsevier
Abstract Human Activity Recognition (HAR) has emerged as a major player in this era of
cutting-edge technological advancement. A key role that HAR plays is its ability to remotely …
cutting-edge technological advancement. A key role that HAR plays is its ability to remotely …
[HTML][HTML] Detection of gait abnormalities for fall risk assessment using wrist-worn inertial sensors and deep learning
Falls are a significant threat to the health and independence of elderly people and represent
an enormous burden on the healthcare system. Successfully predicting falls could be of …
an enormous burden on the healthcare system. Successfully predicting falls could be of …