Using raw sensor data to model and train networks for Human Activity Recognition can be
used in many different applications, from fitness tracking to safety monitoring applications …

Motion or inertial sensors such as gyroscope and accelerometer commonly found in
smartwatches and smartphones can measure characteristics such as acceleration and …

To understand human behavior and intrinsically anticipate human intentions, research into
human activity recognition HAR) using sensors in wearable and handheld devices has …

Human activity recognition and deep learning are two fields that have attracted attention in
recent years. The former due to its relevance in many application domains, such as ambient …

Nowadays, the field of human activity recognition (HAR) is a remarkably hot topic within the
scientific community. Given the low cost, ease of use and high accuracy of the sensors from …

The traditional methods of recognizing human activities involve typical machine learning
(ML) algorithms which uses heuristic engineered features. Human activities are dynamic in …

Sensor-based human activity recognition is an essential task for automatic behavior analysis
for sports player, senior citizens, and IoT applications. The traditional approaches are based …

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 …

This paper presents an original approach to Human Activity Recognition (HAR) tasks based
on wearable sensors data. We have trained a Bidirectional Long-Short Term Memory …

Extracting representative features to recognize human activities through the use of
wearables is an area of on-going research. While hand-crafted features and machine …