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Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Daniel Bachmann, Frank Weichert, Gerhard Rinkenauer
2018 Sensors  
Current works in machine learning mostly refer to the field of deep learning and in particular to recurrent neural networks [23, 24] .  ...  Multimedia interaction describes the concept of using more than one media (e.g., learning system, web-based e-commerce systems) [25] .  ...  Author Contributions: All authors contributed extensively to the work presented in this paper.  ... 
doi:10.3390/s18072194 pmid:29986517 pmcid:PMC6068627 fatcat:x527tlhyzjdjpdrlsppwgm57m4

Human Activity Recognition: Review, Taxonomy and Open Challenges

Muhammad Haseeb Arshad, Muhammad Bilal, Abdullah Gani
2022 Sensors  
Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities  ...  Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of  ...  monitoring videos. [70] A two-stream neural network was proposed using AIoT to recognize anomalies in Big Video Data.  ... 
doi:10.3390/s22176463 pmid:36080922 pmcid:PMC9460866 fatcat:lqzdufjrs5aldfvkztbrdu73c4

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Boštjan Šumak, Saša Brdnik, Maja Pušnik
2021 Sensors  
The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.  ...  Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable  ...  Sensors 2022, 22, 20  ... 
doi:10.3390/s22010020 pmid:35009562 pmcid:PMC8747169 fatcat:2kwsqyrsmvftpgfgme2xpptb7q

A Comprehensive Review of Digital Twin – Part 1: Modeling and Twinning Enabling Technologies [article]

Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, Zhen Hu
2022 arXiv   pre-print
In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in  ...  As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision  ...  The researchers used a Microsoft Kinect 2.0 LiDAR sensor to detect humans in a robotic arm cell.  ... 
arXiv:2208.14197v1 fatcat:5s4564bhmrdyppndd4hhqihqie

Wearable Sensor-Based Human Activity Recognition via Two-Layer Diversity-Enhanced Multiclassifier Recognition Method

Yiming Tian, Xitai Wang, Lingling Chen, Zuojun Liu
2019 Sensors  
In addition, bootstrap resampling is utilized to increase the diversities of the dataset for training the base classifiers in the multiclassifier system.  ...  Secondly, a combined diversity measure for selecting the base classifiers with excellent performance and large diversity is proposed to optimize the performance of the multiclassifier system.  ...  Conflicts of Interest: The authors declare no conflict of interests. Sensors 2019, 19, 2039  ... 
doi:10.3390/s19092039 fatcat:elc46ojj4bg2ln67f2hariszdu

Survey on Deep Neural Networks in Speech and Vision Systems [article]

Mahbubul Alam, Manar D. Samad, Lasitha Vidyaratne, Alexander Glandon,, Khan M. Iftekharuddin
2019 arXiv   pre-print
This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in vision and speech applications.  ...  With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation  ...  Note the views and findings reported in this work completely belong to the authors and not the NSF or NIH.  ... 
arXiv:1908.07656v2 fatcat:7acubicqzzac3dqemkiccoogm4

Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models

Adebola Omolaja, Abayomi Otebolaku, Ali Alfoudi
2022 Applied Sciences  
Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices.  ...  Neural Network and Long Short-Term Memory (CNN–LSTM) learning models.  ...  [56] where multilayer features were extracted from sensory data using a hybrid of the CNN and BLSTM networks.  ... 
doi:10.3390/app12189305 fatcat:vsacnsocjne2bmq4kwfj2wqe2m

Risk Factors Discovery for Cancer Survivability Analysis Using Graph-Rule Mining

Chaoyu Yang, Jie Yang, Zhenyu Yang
2020 Mathematical Problems in Engineering  
The proposed algorithm is then evaluated using one of the largest cancer data resources.  ...  Mining and understanding patients' disease-development pattern is a major healthcare need.  ...  More recently, a Bidirectional Long Short-Term Memory Neural Network (BLSTM-NN) was employed to build an interaction monitoring system in [13] .  ... 
doi:10.1155/2020/2384130 fatcat:da5eou3lkrhhngskxiznplcxey

Deep Learning in Science [article]

Stefano Bianchini, Moritz Müller, Pierre Pelletier
2020 arXiv   pre-print
These search terms allow us to retrieve DL-related publications from Web of Science across all sciences. Based on that sample, we document the DL diffusion process in the scientific system.  ...  This paper provides insights on the diffusion and impact of DL in science.  ...  Furthermore, AI in general and DL in particular have already led to a variety of innovations in the health realm -improving healthcare systems, supporting clinicians in surgery, monitoring patient diseases  ... 
arXiv:2009.01575v2 fatcat:4ttqgjdjfjbydp7flnhcgg5p7m

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Isinsu Katircioglu
Our goal is to alleviate the challenges in these problems using various encoder-decoder models.  ...  We encode this intuition into a self-supervised loss function that we exploit to train a proposal-based encoder-decoder segmentation network.  ...  Depth-based Methods The availability of high-speed depth sensors and the launch of the Microsoft Kinect camera has paved the way for pose and shape estimation for articulated objects using one single depth  ... 
doi:10.5075/epfl-thesis-8036 fatcat:clrvl6zmyneq3ou26nnoddeum4