Contactless Fall Detection for the Elderly

[1]  Anna Ståhlbröst,et al.  Social, Ethical and Ecological Issues in Wearable Technologies , 2019, AMCIS.

[2]  Kabalan Chaccour,et al.  Smart carpet using differential piezoresistive pressure sensors for elderly fall detection , 2015, 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

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

[4]  Peter E. D. Love,et al.  A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network , 2019, Adv. Eng. Informatics.

[5]  Kamin Whitehouse,et al.  The hitchhiker's guide to successful residential sensing deployments , 2011, SenSys.

[6]  Leif E. Peterson K-nearest neighbor , 2009, Scholarpedia.

[7]  Ali Javed,et al.  Fall detection through acoustic Local Ternary Patterns , 2018, Applied Acoustics.

[8]  A. Sanders,et al.  Practice guideline for the ED management of falls in community-dwelling elderly persons. Kaiser Permanente Medical Group. , 1997, Annals of emergency medicine.

[9]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[10]  Yun Li,et al.  A Microphone Array System for Automatic Fall Detection , 2012, IEEE Transactions on Biomedical Engineering.

[11]  Francesco Fioranelli,et al.  Aspect angle dependence and multistatic data fusion for micro-Doppler classification of armed/unarmed personnel , 2015 .

[12]  Marjorie Skubic,et al.  Doppler Radar Fall Activity Detection Using the Wavelet Transform , 2015, IEEE Transactions on Biomedical Engineering.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Xiangyang Li,et al.  Indoor Person Identification and Fall Detection through Non-intrusive Floor Seismic Sensing , 2019, 2019 IEEE International Conference on Smart Computing (SMARTCOMP).

[15]  Francesco Fioranelli,et al.  Multistatic human micro-Doppler classification of armed/unarmed personnel , 2015 .

[16]  Muhammad Salman Khan,et al.  An unsupervised acoustic fall detection system using source separation for sound interference suppression , 2015, Signal Process..

[17]  Francesco Fioranelli,et al.  Analysis of polarimetric multistatic human micro-Doppler classification of armed/unarmed personnel , 2015, 2015 IEEE Radar Conference (RadarCon).

[18]  Stefano Squartini,et al.  Few-Shot Siamese Neural Networks Employing Audio Features for Human-Fall Detection , 2018, PRAI 2018.

[19]  Md. Atiqur Rahman Ahad,et al.  Motion history image: its variants and applications , 2012, Machine Vision and Applications.

[20]  Francesco Piazza,et al.  A Floor Acoustic Sensor for Fall Classification , 2015 .

[21]  Keng Siau,et al.  Factors Influencing the Adoption of Smart Wearable Devices , 2018, Int. J. Hum. Comput. Interact..

[22]  Rached Tourki,et al.  Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification , 2013, J. Electronic Imaging.

[23]  J. Högbom,et al.  APERTURE SYNTHESIS WITH A NON-REGULAR DISTRIBUTION OF INTERFEROMETER BASELINES. Commentary , 1974 .

[24]  Sreeraman Rajan,et al.  Fall Detection Using Standoff Radar-Based Sensing and Deep Convolutional Neural Network , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[25]  A. Narendrakumar Reliable energy efficient trust based data transmission for dynamic Wireless Sensor Networks , 2017, 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT).

[26]  Shuvo Roy,et al.  A Wearable Patch to Enable Long-Term Monitoring of Environmental, Activity and Hemodynamics Variables , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[27]  Stefano Ermon,et al.  Domain Adaptation for Human Fall Detection Using WiFi Channel State Information , 2019, Precision Health and Medicine.

[28]  Nicolas Vayatis,et al.  Fall detection using smart floor sensor and supervised learning , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[29]  Tomoaki Ohtsuki,et al.  Fall Detection Using UHF Passive RFID Based on the Neighborhood Preservation Principle , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[30]  Md. Atiqur Rahman Ahad,et al.  Motion History Images for Action Recognition and Understanding , 2012, SpringerBriefs in Computer Science.

[31]  Francesco Piazza,et al.  A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features , 2017, Comput. Intell. Neurosci..

[32]  Zhaoshuo Jiang,et al.  Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM , 2019, Sensors.

[33]  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.

[34]  Mohan S. Kankanhalli,et al.  Intelligent Multimedia Surveillance , 2013, Springer Berlin Heidelberg.

[35]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[36]  Qiang Li,et al.  Spatio-temporal fall event detection in complex scenes using attention guided LSTM , 2020, Pattern Recognit. Lett..

[37]  Ali Javed,et al.  A framework for fall detection of elderly people by analyzing environmental sounds through acoustic local ternary patterns , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[38]  Marjorie Skubic,et al.  An acoustic fall detector system that uses sound height information to reduce the false alarm rate , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  M. A. R. Ahad,et al.  Complex nurse care activity recognition using statistical features , 2020, UbiComp/ISWC Adjunct.

[40]  Farhaan Mirza,et al.  A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living , 2019, Journal of Medical Systems.

[41]  Md Atiqur Rahman Ahad,et al.  Sensor-Based Human Activity Recognition: Challenges Ahead , 2020, IoT Sensor-Based Activity Recognition.

[42]  Hans-Peter Kriegel,et al.  Angle-based outlier detection in high-dimensional data , 2008, KDD.

[43]  Yuji Iwahori,et al.  A HOG-SVM Based Fall Detection IoT System for Elderly Persons Using Deep Sensor , 2018, IIKI.

[44]  G. ÓLaighin,et al.  A proposal for the classification and evaluation of fall detectors Une proposition pour la classification et l'évaluation des détecteurs de chutes , 2008 .

[45]  Zhu Zhang,et al.  Human Fall Detection Based on Machine Learning Using a THz Radar System , 2019, 2019 IEEE Radar Conference (RadarConf).

[46]  A. Enis Çetin,et al.  HMM Based Falling Person Detection Using Both Audio and Video , 2005, 2006 IEEE 14th Signal Processing and Communications Applications.

[47]  Irene Y. H. Gu,et al.  Human fall detection in videos via boosting and fusing statistical features of appearance, shape and motion dynamics on Riemannian manifolds with applications to assisted living , 2016, Comput. Vis. Image Underst..

[48]  Francesco Piazza,et al.  An End-To-End Unsupervised Approach Employing Convolutional Neural Network Autoencoders for Human Fall Detection , 2019, Quantifying and Processing Biomedical and Behavioral Signals.

[49]  Xuemei Guo,et al.  Floor Pressure Imaging for Fall Detection with Fiber-Optic Sensors , 2016, IEEE Pervasive Computing.

[50]  W. K. Lee,et al.  Smart ECG Monitoring Patch with Built-in R-Peak Detection for Long-Term HRV Analysis , 2015, Annals of Biomedical Engineering.

[51]  E. Finkelstein,et al.  The costs of fatal and non-fatal falls among older adults , 2006, Injury Prevention.

[52]  Way-Soong Lim,et al.  Development of Electronic Floor Mat for Fall Detection and Elderly Care , 2018, Asian Journal of Scientific Research.

[53]  Bart Vanrumste,et al.  Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms. , 2016, Healthcare technology letters.

[54]  Seiichi Serikawa,et al.  Automatic Fall Detection System of Unsupervised Elderly People Using Smartphone , 2017 .

[55]  Israel Gannot,et al.  Fall detection of elderly through floor vibrations and sound , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[56]  Heng Li,et al.  Computer vision aided inspection on falling prevention measures for steeplejacks in an aerial environment , 2018, Automation in Construction.

[57]  Francesco Fioranelli,et al.  Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features , 2015, IEEE Geoscience and Remote Sensing Letters.

[58]  Vangelis Metsis,et al.  A viewpoint-independent statistical method for fall detection , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[59]  Omar Shahid,et al.  A pragmatic signal processing approach for nurse care activity recognition using classical machine learning , 2020, UbiComp/ISWC Adjunct.

[60]  Gang Wang,et al.  NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[61]  Daqing Zhang,et al.  RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices , 2017, IEEE Transactions on Mobile Computing.

[62]  Constantinos S. Pattichis,et al.  XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 , 2016 .

[63]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .

[64]  L. N. Anishchenko,et al.  Contactless fall detection by means of CW bioradar , 2016, 2016 Progress in Electromagnetic Research Symposium (PIERS).

[65]  J. Stevens,et al.  Gender differences for non-fatal unintentional fall related injuries among older adults , 2005, Injury Prevention.

[66]  Bogdan Kwolek,et al.  Human fall detection on embedded platform using depth maps and wireless accelerometer , 2014, Comput. Methods Programs Biomed..

[67]  A. Umamakeswari,et al.  A survey on technical approaches in fall detection system , 2015 .

[68]  Francesco Piazza,et al.  Acoustic cues from the floor: A new approach for fall classification , 2016, Expert Syst. Appl..

[69]  Bo Tan,et al.  Passive Radar for Opportunistic Monitoring in E-Health Applications , 2018, IEEE Journal of Translational Engineering in Health and Medicine.

[70]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[71]  Vasily G. Moshnyaga,et al.  Fall Detection on a single Doppler Radar Sensor by using Convolutional Neural Networks , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).

[72]  Md Atiqur Rahman Ahad,et al.  Exploring Human Activities Using eSense Earable Device , 2020, Smart Innovation, Systems and Technologies.

[73]  R. L. Stratonovich 36 – CONDITIONAL MARKOV PROCESSES*† , 1965 .

[74]  K. A. Taher,et al.  Camera Model Identification using Deep CNN and Transfer Learning Approach , 2019, 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST).

[75]  Mark Hasegawa-Johnson,et al.  Acoustic fall detection using Gaussian mixture models and GMM supervectors , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[76]  Ananda Mohon Ghosh,et al.  Remote health monitoring system through IoT , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).

[77]  Franck Multon,et al.  Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution , 2011, IEEE Transactions on Information Technology in Biomedicine.

[78]  Thomas Serre,et al.  HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.

[79]  Amy Loutfi,et al.  Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper , 2016, J. Sensors.

[80]  Xiaohua Zhu,et al.  Fall detection with multi-domain features by a portable FMCW radar , 2019, 2019 IEEE MTT-S International Wireless Symposium (IWS).

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

[82]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[83]  Vassilis Athitsos,et al.  A survey on vision-based fall detection , 2015, PETRA.

[84]  Hadi Heidari,et al.  Activities Recognition and Fall Detection in Continuous Data Streams Using Radar Sensor , 2019, 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC).

[85]  William Stafford Noble,et al.  Support vector machine , 2013 .

[86]  Xu Yang,et al.  WmFall: WiFi-based multistage fall detection with channel state information , 2018, Int. J. Distributed Sens. Networks.

[87]  Md. Atiqur Rahman Ahad,et al.  Computer Vision and Action Recognition - A Guide for Image Processing and Computer Vision Community for Action Understanding , 2011, Atlantis Ambient and Pervasive Intelligence.

[88]  Karim Baïna,et al.  Reduce False Positive Alerts for Elderly Person Fall Video-Detection Algorithm by convolutional neural network model , 2019, Procedia Computer Science.

[89]  Cristi Iuga,et al.  Fall monitoring and detection for at-risk persons using a UAV , 2018 .

[90]  K. Aminian,et al.  Development of a standard fall data format for signals from body-worn sensors , 2013, Zeitschrift für Gerontologie und Geriatrie.

[91]  M. Popescu,et al.  Acoustic fall detection using one-class classifiers , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[92]  Mirza Mansoor Baig,et al.  Falls risk assessment for hospitalised older adults: a combination of motion data and vital signs , 2016, Aging Clinical and Experimental Research.

[93]  M. A. R. Ahad,et al.  Nurse care activity recognition: using random forest to handle imbalanced class problem , 2020, UbiComp/ISWC Adjunct.

[94]  Mihail Popescu,et al.  A Fuzzy Logic System for Acoustic Fall Detection , 2008, AAAI Fall Symposium: AI in Eldercare: New Solutions to Old Problems.

[95]  Francesco Fioranelli,et al.  Centroid features for classification of armed/unarmed multiple personnel using multistatic human micro-Doppler , 2016 .

[96]  Damien Brulin,et al.  Design of a Smart Sole with Advanced Fall Detection Algorithm , 2019, Journal of Sensor Technology.

[97]  Shengke Wang,et al.  Learning spatiotemporal representations for human fall detection in surveillance video , 2019, J. Vis. Commun. Image Represent..

[98]  Moeness G. Amin,et al.  Radar Data Cube Analysis for Fall Detection , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[99]  Yoong Choon Chang,et al.  A simple vision-based fall detection technique for indoor video surveillance , 2015, Signal Image Video Process..

[100]  Aytaç,et al.  The likely worldwide increase in erectile dysfunction between 1995 and 2025 and some possible policy consequences , 1999, BJU international.

[101]  Norihiko Shinomiya,et al.  Machine learning-based fall detection system for the elderly using passive RFID sensor tags , 2019, 2019 13th International Conference on Sensing Technology (ICST).

[102]  Sozo Inoue,et al.  A Method for Sensor-Based Activity Recognition in Missing Data Scenario , 2020, Sensors.

[103]  Shehroz S. Khan,et al.  Review of Fall Detection Techniques: A Data Availability Perspective , 2016, Medical engineering & physics.

[104]  Tanvi Banerjee,et al.  Improvement of acoustic fall detection using Kinect depth sensing , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[105]  Brijesh Sivathanu Adoption of internet of things (IOT) based wearables for healthcare of older adults – a behavioural reasoning theory (BRT) approach , 2018, Journal of Enabling Technologies.

[106]  Roozbeh Jafari,et al.  BioWatch: A Noninvasive Wrist-Based Blood Pressure Monitor That Incorporates Training Techniques for Posture and Subject Variability , 2016, IEEE Journal of Biomedical and Health Informatics.

[107]  Mohan S. Kankanhalli,et al.  Intelligent Multimedia Surveillance: Current Trends and Research , 2013 .

[108]  Matthias Pätzold,et al.  A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems , 2018, IEEE Transactions on Wireless Communications.

[109]  Anil Surve,et al.  Human Fall Detection Using RFID Technology , 2018, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[110]  Bogdan Kwolek,et al.  Improving fall detection by the use of depth sensor and accelerometer , 2015, Neurocomputing.

[111]  J. Buerano,et al.  Microphone system optimization for free fall impact acoustic method in detection of rice kernel damage , 2012 .

[112]  Francesco Piazza,et al.  Human Fall Detection by Using an Innovative Floor Acoustic Sensor , 2018, Multidisciplinary Approaches to Neural Computing.

[113]  Pramod K. Varshney,et al.  Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure , 2017, IEEE Transactions on Human-Machine Systems.

[114]  Haiping Lu,et al.  MPCA: Multilinear Principal Component Analysis of Tensor Objects , 2008, IEEE Transactions on Neural Networks.

[115]  M. Tinetti,et al.  The patient who falls: "It's always a trade-off". , 2010, JAMA.

[116]  Min Chen,et al.  Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring , 2016, Mobile Networks and Applications.

[117]  Qingzhen Xu,et al.  Fall prediction based on key points of human bones , 2020 .

[118]  Xiaotao Huang,et al.  Low PRF Low Frequency Radar Sensor for Fall Detection by Using Deep Learning , 2019, 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP).

[119]  Atsushi Shimada,et al.  Fall detection using optical level anonymous image sensing system , 2019, Optics & Laser Technology.

[120]  Dietrich Paulus,et al.  Simple online and realtime tracking with a deep association metric , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[121]  Moeness G. Amin,et al.  Effects of range spread and aspect angle on radar fall detection , 2016, 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM).

[122]  He Li,et al.  The promising future of healthcare services: When big data analytics meets wearable technology , 2016, Inf. Manag..

[123]  Ennio Gambi,et al.  Radar and RGB-Depth Sensors for Fall Detection: A Review , 2017, IEEE Sensors Journal.

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

[125]  Mufti Mahmud,et al.  Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder , 2020, BI.

[126]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[127]  Lesley M. Day Falls in Older People: Risk Factors and Strategies for Prevention. , 2003 .

[128]  Octavian Fratu,et al.  eWALL: An Intelligent Caring Home Environment Offering Personalized Context-Aware Applications Based on Advanced Sensing , 2015, Wireless Personal Communications.

[129]  Abhinav Dhall,et al.  Thermal Imaging Based Elderly Fall Detection , 2016, ACCV Workshops.

[130]  Cuong Pham,et al.  A multi-modal multi-view dataset for human fall analysis and preliminary investigation on modality , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[131]  Gongjian Wen,et al.  A deep neural network for real-time detection of falling humans in naturally occurring scenes , 2017, Neurocomputing.

[132]  Md Atiqur Rahman Ahad,et al.  Sensor-Based Benchmark Datasets: Comparison and Analysis , 2020 .

[133]  K. Aminian,et al.  Fall detection with body-worn sensors , 2013, Zeitschrift für Gerontologie und Geriatrie.

[134]  Gerhard H Visser,et al.  Automated remote fall detection using impact features from video and audio. , 2019, Journal of biomechanics.

[135]  Lina Yao,et al.  TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags , 2015, MobiQuitous.

[136]  Kostas Karpouzis,et al.  Fall detection using history triple features , 2015, PETRA.

[137]  Ohtsuki Tomoaki,et al.  Fall Detection Using UHF Passive RFID Tags Based on Neighborhood Preservation Principle , 2018 .

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

[139]  Constantinos S. Pattichis,et al.  Erratum to: XIV Mediterranean Conference on Medical and BiologicalEngineering and Computing 2016 , 2017 .