Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
[HTML][HTML] A systematic review of machine learning models in mental health analysis based on multi-channel multi-modal biometric signals
J Ehiabhi, H Wang - BioMedInformatics, 2023 - mdpi.com
With the increase in biosensors and data collection devices in the healthcare industry,
artificial intelligence and machine learning have attracted much attention in recent years. In …
artificial intelligence and machine learning have attracted much attention in recent years. In …
MultiCNN-FilterLSTM: Resource-efficient sensor-based human activity recognition in IoT applications
With the recent advances in the Internet of Things (IoT) technologies, various human-
centered applications have proliferated and improved the quality of users' life. In the …
centered applications have proliferated and improved the quality of users' life. In the …
[HTML][HTML] A hybrid cnn and rnn variant model for music classification
Music genre classification has a significant role in information retrieval for the organization of
growing collections of music. It is challenging to classify music with reliable accuracy. Many …
growing collections of music. It is challenging to classify music with reliable accuracy. Many …
[HTML][HTML] A multimodal IoT-based locomotion classification system using features engineering and Recursive neural network
Locomotion prediction for human welfare has gained tremendous interest in the past few
years. Multimodal locomotion prediction is composed of small activities of daily living and an …
years. Multimodal locomotion prediction is composed of small activities of daily living and an …
A novel optimized parametric hyperbolic tangent swish activation function for 1D-CNN: application of sensor-based human activity recognition and anomaly detection
S Ankalaki, MN Thippeswamy - Multimedia Tools and Applications, 2023 - Springer
Human activity recognition (HAR) and abnormal/anomaly detection have significant
applications for health monitoring in a smart environment. Abnormal/anomaly prediction …
applications for health monitoring in a smart environment. Abnormal/anomaly prediction …
[HTML][HTML] GTSNet: Flexible architecture under budget constraint for real-time human activity recognition from wearable sensor
Human activity recognition is an essential task for human-centered intelligent systems such
as healthcare and smart vehicles, which can be accomplished by analyzing time-series …
as healthcare and smart vehicles, which can be accomplished by analyzing time-series …
[HTML][HTML] Deep neural network for the detections of fall and physical activities using foot pressures and inertial sensing
HL Chan, Y Ouyang, RS Chen, YH Lai, CC Kuo… - Sensors, 2023 - mdpi.com
Fall detection and physical activity (PA) classification are important health maintenance
issues for the elderly and people with mobility dysfunctions. The literature review showed …
issues for the elderly and people with mobility dysfunctions. The literature review showed …
[HTML][HTML] An active semi-supervised deep learning model for human activity recognition
Human activity recognition (HAR), which aims at inferring the behavioral patterns of people,
is a fundamental research problem in digital health and ambient intelligence. The …
is a fundamental research problem in digital health and ambient intelligence. The …
SensorGAN: A Novel Data Recovery Approach for Wearable Human Activity Recognition
Human activity recognition (HAR) and more broadly, activities of daily life recognition using
wearable devices, have the potential to transform a number of applications including mobile …
wearable devices, have the potential to transform a number of applications including mobile …