Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting
new knowledge. By using DL, the extraction of advanced data representations and …
new knowledge. By using DL, the extraction of advanced data representations and …
Deep learning based systems developed for fall detection: a review
Accidental falls are a major source of loss of autonomy, deaths, and injuries among the
elderly. Accidental falls also have a remarkable impact on the costs of national health …
elderly. Accidental falls also have a remarkable impact on the costs of national health …
EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications
Abstract Human Activity Recognition (HAR) can be defined as the automatic prediction of the
regular human activities performed in our day-to-day life, such as walking, running, cooking …
regular human activities performed in our day-to-day life, such as walking, running, cooking …
A new framework for smartphone sensor-based human activity recognition using graph neural network
Automatic human activity recognition (HAR) through computing devices is a challenging
research topic in the domain of computer vision. It has widespread applications in various …
research topic in the domain of computer vision. It has widespread applications in various …
A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques
Falls among older adults substantially affect mobility, health, and mortality. However,
advancements in wireless and internet-of-things technologies have led to the development …
advancements in wireless and internet-of-things technologies have led to the development …
UI-GAN: Generative adversarial network-based anomaly detection using user initial information for wearable devices
This article proposes an automatic fall detection method for a wearable device that can
promptly alert caregivers when a fall is detected, which could reduce the injuries of elder …
promptly alert caregivers when a fall is detected, which could reduce the injuries of elder …
Hybrid inceptionv3-svm-based approach for human posture detection in health monitoring systems
Posture detection targets toward providing assessments for the monitoring of the health and
welfare of humans have been of great interest to researchers from different disciplines. The …
welfare of humans have been of great interest to researchers from different disciplines. The …
Convolutional neural network modeling strategy for fall-related motion recognition using acceleration features of a scaffolding structure
Falls are the leading cause (eg, 30–35%) of work-related fatalities during construction.
However, conventional sensing approaches to recognizing workers' fall-related movements …
However, conventional sensing approaches to recognizing workers' fall-related movements …
A novel feature set extraction based on accelerometer sensor data for improving the fall detection system
Because falls are the second leading cause of injury deaths, especially in the elderly
according to WHO statistics, there have been a lot of studies on develo** a fall detection …
according to WHO statistics, there have been a lot of studies on develo** a fall detection …
Synthetic IMU datasets and protocols can simplify fall detection experiments and optimize sensor configuration
Falls represent a significant cause of injury among the elderly population. Extensive
research has been devoted to the utilization of wearable IMU sensors in conjunction with …
research has been devoted to the utilization of wearable IMU sensors in conjunction with …