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Human Activities

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Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors.

IEEE journal of biomedical and health informatics
The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Plenty of mechanisms have been developed to detect the health condition of the elderly based on the explicit irregularity of several biomedical parameter...

A Semi-Automatic Annotation Approach for Human Activity Recognition.

Sensors (Basel, Switzerland)
Modern smartphones and wearables often contain multiple embedded sensors which generate significant amounts of data. This information can be used for body monitoring-based areas such as healthcare, indoor location, user-adaptive recommendations and t...

A Survey of the Techniques for The Identification and Classification of Human Actions from Visual Data.

Sensors (Basel, Switzerland)
Recognition of human actions form videos has been an active area of research because it has applications in various domains. The results of work in this field are used in video surveillance, automatic video labeling and human-computer interaction, am...

A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition.

Sensors (Basel, Switzerland)
Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facil...

Classifier Personalization for Activity Recognition Using Wrist Accelerometers.

IEEE journal of biomedical and health informatics
Intersubject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this paper, we propose an approach for personalizing classification rules...

Synthesizing and Reconstructing Missing Sensory Modalities in Behavioral Context Recognition.

Sensors (Basel, Switzerland)
Detection of human activities along with the associated context is of key importance for various application areas, including assisted living and well-being. To predict a user's context in the daily-life situation a system needs to learn from multimo...

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network.

Sensors (Basel, Switzerland)
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets tha...

Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS.

Journal of environmental management
Over the past few years, the need for sustainable environmental management has increased rapidly and green management has emerged as an important tool for the same. The role of Green Human Resource Management (GHRM) practices in environmental managem...

Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone.

Sensors (Basel, Switzerland)
Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, espec...

Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0.

Sensors (Basel, Switzerland)
According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a m...