Abstract Human Activity Recognition (HAR) is an essential task in various applications such
as pervasive healthcare, smart environment, and security and surveillance. The need to …

Recognizing human activities and monitoring population behavior are fundamental needs of
our society. Population security, crowd surveillance, healthcare support and living …

Recently, a significant amount of literature concerning machine learning techniques has
focused on automatic recognition of activities performed by people. The main reason for this …

Human activity recognition (HAR) is a line of research whose goal is to design and develop
automatic techniques for recognizing activities of daily living (ADLs) using signals from …

Stress detection is becoming a popular field in machine learning and this study focuses on
recognizing stress using the sensors of commercially available smartwatches. In most of the …

Monitoring and assessing awkward postures is a proactive approach for Musculoskeletal
Disorders (MSDs) prevention in construction. Machine Learning models have shown …

Sensor-based human activity recognition (HAR) requires to predict the action of a person
based on sensor-generated time series data. HAR has attracted major interest in the past …

The existing action recognition systems mainly focus on generalized methods to categorize
human actions. However, the generalized systems cannot attain the same level of …

Recognizing activities of daily living (ADL) provides vital contextual information that
enhances the effectiveness of various mobile health and wellness applications …

Mobile activity recognition is significant to the development of human-centric pervasive
applications including elderly care, personalized recommendations, etc. Nevertheless, the …