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) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

Computing devices that can recognize various human activities or movements can be used
to assist people in healthcare, sports, or human–robot interaction. Readily available data for …

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

Recent technological developments, such as the Internet of Things (IoT), artificial
intelligence, edge, and cloud computing, have paved the way in transforming traditional …

In spite of advances in electronics and internet technologies, current healthcare remains
hospital‐centred. Disruptive technologies are required to translate state‐of‐art wearable …

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 …

Even with the most cutting-edge tools, treating and monitoring patients—including children,
elders, and suspected COVID-19 patients—remains a challenging activity. This study aimed …

The self-regulated recognition of human activities from time-series smartphone sensor data
is a growing research area in smart and intelligent health care. Deep learning (DL) …

Human activity recognition (HAR) is a process of identifying the daily living activities of an
individual using a set of sensors and appropriate learning algorithms. Most of the works on …