3D Convolutional Neural Network for Human Behavior Analysis in Intelligent Sensor Network

2022 ◽  
Author(s):  
Bao Peng ◽  
Zhi Yao ◽  
Qibao Wu ◽  
Hailing Sun ◽  
Guofu Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Behavior Analysis ◽  
Sensor Network ◽  
Human Behavior ◽  
Intelligent Sensor ◽  
Human Behavior Analysis

scholarly journals Inferring Emotion Tags from Object Images Using Convolutional Neural Network

2020 ◽  
Vol 10 (15) ◽  
pp. 5333
Author(s):  
Anam Manzoor ◽  
Waqar Ahmad ◽  
Muhammad Ehatisham-ul-Haq ◽  
Abdul Hannan ◽  
Muhammad Asif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Behavior ◽  
Real Life ◽  
Vital Role ◽  
Object Categories ◽  
Different Types ◽  
Gender Based ◽  
High Level ◽  
Human Behavior Analysis

Emotions are a fundamental part of human behavior and can be stimulated in numerous ways. In real-life, we come across different types of objects such as cake, crab, television, trees, etc., in our routine life, which may excite certain emotions. Likewise, object images that we see and share on different platforms are also capable of expressing or inducing human emotions. Inferring emotion tags from these object images has great significance as it can play a vital role in recommendation systems, image retrieval, human behavior analysis and, advertisement applications. The existing schemes for emotion tag perception are based on the visual features, like color and texture of an image, which are poorly affected by lightning conditions. The main objective of our proposed study is to address this problem by introducing a novel idea of inferring emotion tags from the images based on object-related features. In this aspect, we first created an emotion-tagged dataset from the publicly available object detection dataset (i.e., “Caltech-256”) using subject evaluation from 212 users. Next, we used a convolutional neural network-based model to automatically extract the high-level features from object images for recognizing nine (09) emotion categories, such as amusement, awe, anger, boredom, contentment, disgust, excitement, fear, and sadness. Experimental results on our emotion-tagged dataset endorse the success of our proposed idea in terms of accuracy, precision, recall, specificity, and F1-score. Overall, the proposed scheme achieved an accuracy rate of approximately 85% and 79% using top-level and bottom-level emotion tagging, respectively. We also performed a gender-based analysis for inferring emotion tags and observed that male and female subjects have discernment in emotions perception concerning different object categories.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

Convolutional neural network for human behavior recognition based on smart bracelet

2020 ◽  
Vol 38 (5) ◽  
pp. 5615-5626
Author(s):  
Junsuo Qu ◽  
Ning Qiao ◽  
Haonan Shi ◽  
Chang Su ◽  
Abolfazl Razi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Behavior ◽  
Behavior Recognition ◽  
Human Behavior Recognition

Sensors for Human Behavior Analysis

2012 ◽  
pp. 7-13
Author(s):  
Alberto Amato ◽  
Vincenzo Di Lecce ◽  
Vincenzo Piuri
Keyword(s):  
Behavior Analysis ◽  
Human Behavior ◽  
Human Behavior Analysis
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