Human activity recognition (HAR) technology that analyzes data acquired from various types
of sensing devices, including vision sensors and embedded sensors, has motivated the …

Increased life expectancy coupled with declining birth rates is leading to an aging
population structure. Aging-caused changes, such as physical or cognitive decline, could …

Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer
biomechanists a wealth of data on healthy and pathological movement. To harness the …

In this paper, we present a deep learning framework “Rehab-Net” for effectively classifying
three upper limb movements of the human arm, involving extension, flexion, and rotation of …

In recent years, significant advancements have taken place in human activity recognition
using various machine learning approaches. However, feature engineering have dominated …

Background Recent advances in wearable sensor technologies enable objective and long-
term monitoring of motor activities in a patient's habitual environment. People with mobility …

Purpose To address the gap in the literature and clarify the expanding role of wearable
sensor data in stroke rehabilitation, we summarized the methods for upper extremity (UE) …

The ability to accurately identify human activities is essential for developing automatic
rehabilitation and sports training systems. In this paper, large-scale exercise motion data …

Without expert coaching, inexperienced exercisers performing core exercises, such as
squats, are subject to an increased risk of spinal or knee injuries. Although it is theoretically …

The integration of wearable sensor technology and machine learning algorithms has
significantly transformed the field of intelligent medical rehabilitation. These innovative …