Abstract The Human Activity Recognition (HAR) problem leverages pattern recognition to
classify physical human activities as they are captured by several sensor modalities. Remote …

Human activity recognition (HAR) has become ubiquitous in modern daily life, and thus
requires robust classification algorithms. Accelerometers are the most commonly used …

The Human Activity Recognition (HAR) is a pattern recognition task that learns to identify
human physical activities recorded by different sensor modalities. The application areas …

Human activity recognition (HAR) is a rapidly growing field of research that aims to
automatically identify and classify human motions and activities from different tracking …

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 …

Human activity recognition (HAR) using smartphone sensors have been recently studied in
various applications including healthcare, fitness, and smart home. Their recognition …

Deep neural network is an effective choice to automatically recognize human actions
utilizing data from various wearable sensors. These networks automate the process of …

Human Activity Recognition (HAR) has been proven to be effective in various healthcare
and telemonitoring applications. Current HAR methods, especially deep learning, are …

Human activity recognition (HAR) is an essential component of ambient assistive living. HAR
has traditionally relied on computer vision techniques. However, it has several drawbacks …

Deep convolutional neural networks (CNNs) achieve state-of-the-art performance in
wearable human activity recognition (HAR), which has become a new research trend in …