Human activity recognition (HAR) technology that analyzes data acquired from various types
of sensing devices, including vision sensors and embedded sensors, has motivated the …

Granger causality provides a framework that uses predictability to identify causation
between time series variables. This is important to policymakers for effective policy …

We propose a novel data-driven feature extraction approach based on direct causality and
fuzzy temporal windows (FTWs) to improve the precision of human activity recognition and …

Human Activity Recognition (HAR) based on Inertial Measurement Unit (IMU) has become
increasingly important in health and fitness applications. These systems can continuously …

Wearable sensor-based HAR (human activity recognition) is a popular human activity
perception method. However, due to the lack of a unified human activity model, the number …

Using different objects or tools to perform activities in a step-by-step manner is a common
practice in various settings, including workplaces, households, and recreational activities …

A human motion pattern recognition algorithm based on Nano-sensor and deep learning is
studied to recognize human motion patterns in real time and with high accuracy. First …

With advances in spatial–temporal Internet of Things (IoT) technologies, human activity
recognition (HAR) has played a major role in human safety and medical health. Recently …

Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to
implement human-computer collaboration with users. Most of the existing methods in this …

Existing fault prediction algorithms based on deep learning have achieved good prediction
performance. These algorithms treat all features fairly and assume that the progression of …