Human activity recognition (HAR) has multifaceted applications due to its worldly usage of
acquisition devices such as smartphones, video cameras, and its ability to capture human …

Using artificial intelligence and machine learning techniques in healthcare applications has
been actively researched over the last few years. It holds promising opportunities as it is …

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 …

Advances in deep learning (DL) model design have pushed the boundaries of the areas in
which it can be applied. The fields with an immense availability of complex big data have …

Abstract Human Activity Recognition (HAR) is very useful for the clinical applications, and
many machine learning algorithms have been successfully implemented to achieve high …

Sensor-based human activity recognition (S-HAR) has become an important and high-
impact topic of research within human-centered computing. In the last decade, successful …

Abstract In Artificial Intelligence, Human Activity Recognition (HAR) refers to the capability of
machines to identify various activities performed by the users. The knowledge acquired from …

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that
were recorded during free living suffer from non-fixed sensor placement, the usage of only …

This paper presents a comprehensive dataset intended to evaluate passive Human Activity
Recognition (HAR) and localization techniques with measurements obtained from …

Some recent studies use a convolutional neural network (CNN) or long short-term memory
(LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high …