Intelligent Real-Time Multimodal Fall Detection in Fog Infrastructure Using Ensemble Learning

[1]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[2]  Osman Hasan,et al.  Survey of fall detection and daily activity monitoring techniques , 2010, 2010 International Conference on Information and Emerging Technologies.

[3]  Ling Shao,et al.  A survey on fall detection: Principles and approaches , 2013, Neurocomputing.

[4]  Rajkumar Buyya,et al.  Fog Computing: Helping the Internet of Things Realize Its Potential , 2016, Computer.

[5]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[6]  Jenq-Neng Hwang,et al.  A Review on Video-Based Human Activity Recognition , 2013, Comput..

[7]  Eduardo Casilari-Pérez,et al.  Comparison and Characterization of Android-Based Fall Detection Systems , 2014, Sensors.

[8]  Beniamino Di Martino Applications Portability and Services Interoperability among Multiple Clouds , 2014, IEEE Cloud Computing.

[9]  Vassilis Athitsos,et al.  Evaluating Depth-Based Computer Vision Methods for Fall Detection under Occlusions , 2014, ISVC.

[10]  Jae-Young Pyun,et al.  Real life applicable fall detection system based on wireless body area network , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[11]  Kiseon Kim,et al.  FallDroid: An Automated Smart-Phone-Based Fall Detection System Using Multiple Kernel Learning , 2019, IEEE Transactions on Industrial Informatics.

[12]  Turgay Tugay Bilgin,et al.  A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data , 2012, IET Commun..

[13]  Vivienne Sze,et al.  Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.

[14]  Songqing Chen,et al.  FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation , 2015, 2015 IEEE International Conference on Networking, Architecture and Storage (NAS).

[15]  S. Robinovitch,et al.  Effect of the "squat protective response" on impact velocity during backward falls. , 2004, Journal of biomechanics.

[16]  Miao Yu,et al.  A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment , 2012, IEEE Transactions on Information Technology in Biomedicine.

[17]  Jong-Hoon Youn,et al.  Survey and evaluation of real-time fall detection approaches , 2009, 2009 6th International Symposium on High Capacity Optical Networks and Enabling Technologies (HONET).

[18]  Miguel A. Labrador,et al.  Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors , 2014, Sensors.

[19]  Lingmei Ren,et al.  Research of Fall Detection and Fall Prevention Technologies: A Systematic Review , 2019, IEEE Access.

[20]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[21]  Sherali Zeadally,et al.  Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities , 2018, Future Gener. Comput. Syst..

[22]  Inmaculada Plaza,et al.  Challenges, issues and trends in fall detection systems , 2013, Biomedical engineering online.

[23]  Xinguo Yu Approaches and principles of fall detection for elderly and patient , 2008, HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services.

[24]  Dong Xuan,et al.  PerFallD: A pervasive fall detection system using mobile phones , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[25]  A. Sengto,et al.  Human falling detection algorithm using back propagation neural network , 2012, The 5th 2012 Biomedical Engineering International Conference.

[26]  Cem Ersoy,et al.  A Review and Taxonomy of Activity Recognition on Mobile Phones , 2013 .

[27]  Benny Rochwerger,et al.  A Monitoring and Audit Logging Architecture for Data Location Compliance in Federated Cloud Infrastructures , 2011, 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum.

[28]  Amar Ramdane-Cherif,et al.  Multimodal System for Fall Detection and Location of person in an Intelligent Habitat , 2017, ANT/SEIT.

[29]  Haibo Wang,et al.  Depth-Based Human Fall Detection via Shape Features and Improved Extreme Learning Machine , 2014, IEEE Journal of Biomedical and Health Informatics.

[30]  James Brusey,et al.  Fall Detection with Wearable Sensors--Safe (Smart Fall Detection) , 2011, 2011 Seventh International Conference on Intelligent Environments.

[31]  Muhammad Shoaib,et al.  View-invariant Fall Detection for Elderly in Real Home Environment , 2010, 2010 Fourth Pacific-Rim Symposium on Image and Video Technology.

[32]  Silviu Panica,et al.  Support Services for Applications Execution in Multi-clouds Environments , 2016, 2016 IEEE International Conference on Autonomic Computing (ICAC).

[33]  Paul Panek,et al.  Fall detection with distributed floor-mounted accelerometers: An overview of the development and evaluation of a fall detection system within the project eHome , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[34]  Song Han,et al.  Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.

[35]  Thi-Lan Le,et al.  Continuous detection of human fall using multimodal features from Kinect sensors in scalable environment , 2017, Comput. Methods Programs Biomed..

[36]  Samee Ullah Khan,et al.  Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers , 2018, Comput. Networks.

[37]  Miao Yu,et al.  An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment , 2013, IEEE Journal of Biomedical and Health Informatics.

[38]  Surapa Thiemjarus,et al.  Automatic Fall Monitoring: A Review , 2014, Sensors.

[39]  Sergio Escalera,et al.  A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[40]  Lianwen Jin,et al.  A naturalistic 3D acceleration-based activity dataset & benchmark evaluations , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[41]  Igor Radusinovic,et al.  Software-Defined Fog Network Architecture for IoT , 2016, Wireless Personal Communications.

[42]  Tao Xu,et al.  New Advances and Challenges of Fall Detection Systems: A Survey , 2018 .

[43]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[44]  Ali A. Ghorbani,et al.  A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT , 2017, IEEE Access.

[45]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[46]  María de Lourdes Martínez-Villaseñor,et al.  UP-Fall Detection Dataset: A Multimodal Approach , 2019, Sensors.

[47]  Jesús Francisco Vargas-Bonilla,et al.  SisFall: A Fall and Movement Dataset , 2017, Sensors.

[48]  Song Guo,et al.  Adaptive and Fault-Tolerant Data Processing in Healthcare IoT Based on Fog Computing , 2020, IEEE Transactions on Network Science and Engineering.