Human activity recognition (HAR) using inertial motion data has gained a lot of momentum
in recent years both in research and industrial applications. From the abstract perspective …

Human activity recognition (HAR) and cycle analysis, such as gait analysis, have become an
integral part of daily lives from gesture recognition to step counting. As the available data …

One of the most significant challenges in Human Activity Recognition using wearable
devices is inter-class similarities and subject heterogeneity. These problems lead to the …

In the present paper, an attempt was made to achieve high-performance continuous human
activity recognition (CHAR) using deep neural networks. The present study focuses on …

Human activity recognition (HAR) has received the significant attention in the field of security
and surveillance due to its high potential for real-time monitoring, identifying the abnormal …

Conditional Generative Adversarial Networks (CGAN) have shown great promise in
generating synthetic data for sensor-based activity recognition. However, one key issue …

More recently, Human Activity Recognition (HAR) based on sensors has become a hot topic
due to its wide application. Researchers significantly reduce the cost of feature extraction …

In recent times, deep neural networks have seen increased adoption in highly critical tasks.
They are also susceptible to adversarial attacks, which are specifically crafted changes …

The main objective of sensor-based human activity recognition (HAR) is to classify
predefined human physical activities with multichannel signals acquired from wearable …

Gait-based person authentication using smartphones and wearable devices has attracted a
lot of attention in recent years due to its unobtrusive nature and the widespread inertial …