Deep learning approach for complex activity recognition using heterogeneous sensors from wearable device
The classification of simple and complex sequences of operations is made easier according
to the use of heterogeneous sensors from a wearable device. Sensor-based human activity …
to the use of heterogeneous sensors from a wearable device. Sensor-based human activity …
Attention-based multihead deep learning framework for online activity monitoring with smartwatch sensors
The expeditious propagation of Internet of Things (IoT) technologies implanted in different
smart devices, such as smartphones and smartwatches have a ubiquitous consequence on …
smart devices, such as smartphones and smartwatches have a ubiquitous consequence on …
Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities
The recognition of human activities has become a dominant emerging research problem
and widely covered application areas in surveillance, wellness management, healthcare …
and widely covered application areas in surveillance, wellness management, healthcare …
Activity-based person identification using multimodal wearable sensor data
Wearable devices equipped with a variety of sensors facilitate the measurement of
physiological and behavioral characteristics. Activity-based person identification is …
physiological and behavioral characteristics. Activity-based person identification is …
Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition
Iris recognition is a robust biometric system—user-friendly, accurate, fast, and reliable. This
biometric system captures information in a contactless manner, making it suitable for use …
biometric system captures information in a contactless manner, making it suitable for use …
Out-of-distribution detection of human activity recognition with smartwatch inertial sensors
Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data
from activity classes that are not represented in the training data of a Machine Learning (ML) …
from activity classes that are not represented in the training data of a Machine Learning (ML) …
Real-life human activity recognition with tri-axial accelerometer data from smartphone using hybrid long short-term memory networks
N Hnoohom, A Jitpattanakul… - … 15th International Joint …, 2020 - ieeexplore.ieee.org
Human activity recognition (HAR) has an enthusiastic research field in time-series
classification due to its variation of successful applications in various domains. The …
classification due to its variation of successful applications in various domains. The …
A perspective on human activity recognition from inertial motion data
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 …
in recent years both in research and industrial applications. From the abstract perspective …
Convolutional neural network and data augmentation for behavioral-based biometric user identification
S Mekruksavanich, A Jitpattanakul - ICT Systems and Sustainability …, 2021 - Springer
One classification problem that is especially challenging is biometric identification, which
links cybersecurity to the analysis of human behavior. Biometric data can be collected …
links cybersecurity to the analysis of human behavior. Biometric data can be collected …
Multi-dimensional task recognition for human-robot teaming: literature review
P Baskaran, JA Adams - Frontiers in Robotics and AI, 2023 - frontiersin.org
Human-robot teams collaborating to achieve tasks under various conditions, especially in
unstructured, dynamic environments will require robots to adapt autonomously to a human …
unstructured, dynamic environments will require robots to adapt autonomously to a human …