16.7.4.7.6 Human Safety, Falling, Fall Detection, Home Care, Smart Home

Chapter Contents (Back)
Human Safety. Home Care. Smart Home. Fall Detection.

Brodsky, T.[Tomas], Dagtas, S.[Serhan],
Video based detection of fall-down and other events,
US_Patent7,110,569, Sep 19, 2006
WWW Link. BibRef 0609

McKenna, S.J.[Stephen J.], Nait-Charif, H.[Hammadi],
Summarising Contextual Activity and Detecting Unusual Inactivity in a Supportive Home Environment,
PAA(7), No. 4, December 2004, pp. 386-401.
PDF File. BibRef 0412
Earlier: A2, A1:
Activity summarisation and fall detection in a supportive home environment,
ICPR04(IV: 323-326).
IEEE DOI 0409
BibRef

Anderson, D.T.[Derek T.], Luke, R.H.[Robert H.], Keller, J.M.[James M.], Skubic, M.[Marjorie], Rantz, M.[Marilyn], Aud, M.[Myra],
Linguistic summarization of video for fall detection using voxel person and fuzzy logic,
CVIU(113), No. 1, January 2009, pp. 80-89.
Elsevier DOI 0812
Linguistic summarization; Activity analysis; Fuzzy logic; Fall detection; Eldercare; Voxel person BibRef

Thome, N.[Nicolas], Miguet, S.[Serge], Ambellouis, S.,
A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach,
CirSysVideo(18), No. 11, November 2008, pp. 1522-1532.
IEEE DOI 0811
BibRef
Earlier: A1, A2, Only:
A HHMM-Based Approach for Robust Fall Detection,
ICARCV06(1-8).
IEEE DOI 0612
BibRef
Earlier: A1, A2, Only:
A robust appearance model for tracking human motions,
AVSBS05(528-533).
IEEE DOI 0602
BibRef

Thome, N.[Nicolas], Merad, D.[Djamel], Miguet, S.[Serge],
Learning articulated appearance models for tracking humans: A spectral graph matching approach,
SP:IC(23), No. 10, November 2008, pp. 769-787,.
Elsevier DOI 0804
BibRef
Earlier:
Human Body Part Labeling and Tracking Using Graph Matching Theory,
AVSBS06(38-38).
IEEE DOI 0611
Real-time multiple people tracking; On-line articulated appearance learning; People identification; Body part labeling from silhouette; Spectral graph matching; Topological model BibRef

Pop, I.[Ionel], Mihaela, S.[Scuturici], Miguet, S.[Serge],
Common Motion Map Based on Codebooks,
ISVC09(II: 1181-1190).
Springer DOI 0911
BibRef

Pop, I.[Ionel], Mihaela, S.[Scuturici], Miguet, S.[Serge],
Incremental trajectory aggregation in video sequences,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Lai, C.F.[Chin-Feng], Huang, Y.M.[Yueh-Min], Park, J.H.[Jong Hyuk], Chao, H.C.[Han-Chieh],
Adaptive Body Posture Analysis for Elderly-Falling Detection with Multisensors,
IEEE_Int_Sys(25), No. 2, March-April 2010, pp. 20-30.
IEEE DOI 1006
BibRef

Rougier, C., Meunier, J.[Jean], St-Arnaud, A.[Alain], Rousseau, J.[Jacqueline],
Robust Video Surveillance for Fall Detection Based on Human Shape Deformation,
CirSysVideo(21), No. 5, May 2011, pp. 611-622.
IEEE DOI 1105
BibRef

Auvinet, E.[Edouard], Multon, F.[Franck], St-Arnaud, A.[Alain], Rousseau, J.[Jacqueline], Meunier, J.[Jean],
Fall Detection Using Body Volume Recontruction and Vertical Repartition Analysis,
ICISP10(376-383).
Springer DOI 1006
BibRef

Liao, Y.T.[Yi Ting], Huang, C.L.[Chung-Lin], Hsu, S.C.[Shih-Chung],
Slip and fall event detection using Bayesian Belief Network,
PR(45), No. 1, January 2012, pp. 24-32.
Elsevier DOI 1109
BibRef
Earlier: A1, A2, Only:
Slip and Fall Events Detection by Analyzing the Integrated Spatiotemporal Energy Map,
ICPR10(1718-1721).
IEEE DOI 1008
Bayesian Belief Network (BBN); Slip and fall event detection; Motion history image (MHI); Integrated spatiotemporal energy (ISTE) map; Motion active (MA) area BibRef

Yu, M., Naqvi, S.M., Rhuma, A., Chambers, J.,
One class boundary method classifiers for application in a video-based fall detection system,
IET-CV(6), No. 2, 2012, pp. 90-100.
DOI Link 1204
BibRef

Rougier, C.[Caroline], Meunier, J.[Jean], St-Arnaud, A.[Alain], Rousseau, J.[Jacqueline],
3D head tracking for fall detection using a single calibrated camera,
IVC(31), No. 3, March 2013, pp. 246-254.
Elsevier DOI 1303
Computer vision; 3D; Head tracking; Monocular; Particle Filter; Video surveillance; Fall detection BibRef

Yazar, A.[Ahmet], Keskin, F.[Furkan], Töreyin, B.U.[B. Ugur], Çetin, A.E.[A. Enis],
Fall detection using single-tree complex wavelet transform,
PRL(34), No. 15, 2013, pp. 1945-1952.
Elsevier DOI 1309
Vibration sensor BibRef

Katz, P.[Philippe], Aron, M.[Michael], Alfalou, A.[Ayman],
A face-tracking system to detect falls in the elderly,
SPIE(Newsroom), August 8, 2013.
DOI Link 1310
An automated surveillance method that uses multiple image processing can detect, analyze, and track movements to identify emergency situations. BibRef

Grewe, L.[Lynne], Magańa-Zook, S.[Steven],
Building a cyber-physical fall detection system for seniors,
SPIE(Newsroom), April 17, 2014
DOI Link 1407
A 3D commercial vision sensor helps older people live autonomously at home for longer. BibRef

Suryadevara, N.K.[Nagender K.], Mukhopadhyay, S.C.[Subhas C.],
Determining Wellness through an Ambient Assisted Living Environment,
IEEE_Int_Sys(29), No. 3, May 2014, pp. 30-37.
IEEE DOI 1408
Aging BibRef

Feng, W.G.[Wei-Guo], Liu, R.[Rui], Zhu, M.[Ming],
Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera,
SIViP(8), No. 6, September 2014, pp. 1129-1138.
Springer DOI 1408
BibRef

Mastorakis, G.[Georgios], Makris, D.[Dimitrios],
Fall detection system using Kinect's infrared sensor,
RealTimeIP(9), No. 4, December 2014, pp. 635-646.
WWW Link. 1411
BibRef

Kirkpatrick, K.[Keith],
Sensors for Seniors,
CACM(57), No. 12, December 2014, pp. 17-19.
DOI Link 1412
BibRef

Chua, J.L.[Jia-Luen], Chang, Y.C.[Yoong Choon], Lim, W.K.[Wee Keong],
A simple vision-based fall detection technique for indoor video surveillance,
SIViP(9), No. 3, March 2015, pp. 623-633.
WWW Link. 1503
BibRef

Waterson, P.E., Kendrick, V.L., Ryan, B., Jun, T., Haslam, R.A.,
Probing deeper into the risks of slips, trips and falls for an ageing rail passenger population: applying a systems approach,
IET-ITS(10), No. 1, 2016, pp. 25-31.
DOI Link 1602
geriatrics BibRef

Amin, M.G., Zhang, Y.D., Ahmad, F., Ho, K.C.D.,
Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring,
SPMag(33), No. 2, March 2016, pp. 71-80.
IEEE DOI 1603
Biomedical monitoring BibRef

Yun, Y.X.[Yi-Xiao], Gu, I.Y.H.[Irene Yu-Hua],
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,
CVIU(148), No. 1, 2016, pp. 111-122.
Elsevier DOI 1606
BibRef
Earlier:
Human fall detection via shape analysis on Riemannian manifolds with applications to elderly care,
ICIP15(3280-3284)
IEEE DOI 1512
Human fall detection BibRef

Senouci, B.[Benaoumeur], Charfim, I.[Imen], Heyrman, B.[Barthelemy], Dubois, J.[Julien], Miteran, J.[Johel],
Fast prototyping of a SoC-based smart-camera: A real-time fall detection case study,
RealTimeIP(12), No. 4, December 2016, pp. 649-662.
Springer DOI 1612
BibRef

Ozcan, K.[Koray], Velipasalar, S.[Senem], Varshney, P.K.[Pramod K.],
Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure,
HMS(47), No. 1, February 2017, pp. 31-39.
IEEE DOI 1702
cameras BibRef

Macků, L.[Lubomír], Matejíková, M.[Markéta],
Detection and Prevention of Seniors Falls,
Sensors(206), No. 11, November 2016, pp. 59-67.
HTML Version. 1705
BibRef

Mousse, M.A.[Mikaël A.], Motamed, C.[Cina], Ezin, E.C.[Eugčne C.],
Percentage of human-occupied areas for fall detection from two views,
VC(33), No. 12, December 2017, pp. 1529-1540.
WWW Link. 1710
BibRef

Kepski, M.[Michal], Kwolek, B.[Bogdan],
Event-driven system for fall detection using body-worn accelerometer and depth sensor,
IET-CV(12), No. 1, February 2018, pp. 48-58.
DOI Link 1801
BibRef

Bertini, F., Bergami, G., Montesi, D., Veronese, G., Marchesini, G., Pandolfi, P.,
Predicting Frailty Condition in Elderly Using Multidimensional Socioclinical Databases,
PIEEE(106), No. 4, April 2018, pp. 723-737.
IEEE DOI 1804
Data warehouses, Databases, Medical services, Predictive models, Senior citizens, Statistics, Sustainable development, smart healthcare BibRef

Badeche, M.[Mohamed], Bousefsaf, F.[Frédéric], Moussaoui, A.[Abdelhak], Benmohammed, M.[Mohamed], Pruski, A.[Alain],
An automatic natural feature selection system for indoor tracking - application to Alzheimer patient support,
IJCVR(8), No. 2, 2018, pp. 201-220.
DOI Link 1806
BibRef

Bouachir, W.[Wassim], Gouiaa, R.[Rafik], Li, B.[Bo], Noumeir, R.[Rita],
Intelligent video surveillance for real-time detection of suicide attempts,
PRL(110), 2018, pp. 1-7.
Elsevier DOI 1806
Suicide detection, Video surveillance, Kinect, Depth images, Prisons BibRef


Chen, O.T.C., Tsai, C.H., Manh, H.H., Lai, W.C.,
Activity recognition using a panoramic camera for homecare,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, geriatrics, image colour analysis, image matching, image motion analysis, image recognition, TV BibRef

Chang, M.C., Yi, T., Duan, K., Luo, J., Tu, P., Priebe, M., Wood, E., Stachura, M.,
In-bed patient motion and pose analysis using depth videos for pressure ulcer prevention,
ICIP17(4118-4122)
IEEE DOI 1803
biomechanics, computer vision, health care, image capture, image colour analysis, medical image processing, pressure ulcer prevention BibRef

Solbach, M.D., Tsotsos, J.K.,
Vision-Based Fallen Person Detection for the Elderly,
ACVR17(1433-1442)
IEEE DOI 1802
Cameras, Head, Injuries, Magnetic heads, Senior citizens, Wearable sensors BibRef

Jahanjoo, A., Tahan, M.N., Rashti, M.J.,
Accurate fall detection using 3-axis accelerometer sensor and MLF algorithm,
IPRIA17(90-95)
IEEE DOI 1712
accelerometers, fuzzy set theory, geriatrics, health care, learning (artificial intelligence), minimax techniques, triaxial accelerometer BibRef

Carletti, V.[Vincenzo], Greco, A.[Antonio], Saggese, A.[Alessia], Vento, M.[Mario],
A Smartphone-Based System for Detecting Falls Using Anomaly Detection,
CIAP17(II:490-499).
Springer DOI 1711
BibRef

Alaoui, A.Y., Hassouny, A.E., Thami, R.O.H., Tairi, H.,
Video based human fall detection using von Mises distribution of motion vectors,
ISCV17(1-5)
IEEE DOI 1710
Biomedical optical imaging, Classification algorithms, Image motion analysis, Optical imaging, Optical sensors, Senior citizens, fall detection, motion vectors, optical flow, von, Mises, distribution BibRef

Adhikari, K., Bouchachia, H., Nait-Charif, H.,
Activity recognition for indoor fall detection using convolutional neural network,
MVA17(81-84)
DOI Link 1708
Feature extraction, Monitoring, Neural networks, Organizations, Senior citizens, Sensitivity, Training BibRef

Vadivelu, S.[Somasundaram], Ganesan, S.[Sudakshin], Murthy, O.V.R.[O.V. Ramana], Dhall, A.[Abhinav],
Thermal Imaging Based Elderly Fall Detection,
CV4AC16(III: 541-553).
Springer DOI 1704
BibRef

Iazzi, A.[Abderrazak], Rziza, M.[Mohammed], Thami, R.O.H.[Rachid Oulad Haj], Aboutajdine, D.[Driss],
A New Method for Fall Detection of Elderly Based on Human Shape and Motion Variation,
ISVC16(II: 156-167).
Springer DOI 1701
BibRef

Pramerdorfer, C.[Christopher], Planinc, R.[Rainer], Van Loock, M.[Mark], Fankhauser, D.[David], Kampel, M.[Martin], Brandstötter, M.[Michael],
Fall Detection Based on Depth-Data in Practice,
ACVR16(II: 195-208).
Springer DOI 1611
BibRef

Ayed, I.[Ines], Moyŕ-Alcover, B.[Biel], Martínez-Bueso, P.[Pau], Varona, J.[Javier], Ghazel, A.[Adel], Jaume-i-Capó, A.[Antoni],
Balance Clinical Measurement Using RGBD Devices,
AMDO16(125-134).
Springer DOI 1608
clinical prevention of falls. BibRef

Gu, I.Y.H.[Irene Yu-Hua], Kumar, D.P.[Durga Priya], Yun, Y.X.[Yi-Xiao],
Privacy-Preserving Fall Detection in Healthcare Using Shape and Motion Features from Low-Resolution RGB-D Videos,
ICIAR16(490-499).
Springer DOI 1608
BibRef

Rajabi, H., Nahvi, M.,
An intelligent video surveillance system for fall and anesthesia detection for elderly and patients,
IPRIA15(1-6)
IEEE DOI 1603
Gaussian processes BibRef

Lisowska, A., Wheeler, G., Inza, V.C., Poole, I.,
An Evaluation of Supervised, Novelty-Based and Hybrid Approaches to Fall Detection Using Silmee Accelerometer Data,
ACVR15(402-408)
IEEE DOI 1602
Accelerometers BibRef

Flores-Barranco, M.M.[Martha Magali], Ibarra-Mazano, M.A.[Mario-Alberto], Cheng, I.[Irene],
Accidental Fall Detection Based on Skeleton Joint Correlation and Activity Boundary,
ISVC15(II: 489-498).
Springer DOI 1601
BibRef

Castellanos-Dominguez, G.[German],
Fall Detection Algorithm Based on Thresholds and Residual Events,
CIARP15(575-583).
Springer DOI 1511
BibRef

Trullo, R.[Roger], Martinez, D.[Duber],
Detecting Human Falls: A Vision-FSM Approach,
CAIP15(I:766-777).
Springer DOI 1511
BibRef

Boulard, L., Baccaglini, E., Scopigno, R.,
Insights into the role of feedbacks in the tracking loop of a modular fall-detection algorithm,
VCIP14(406-409)
IEEE DOI 1504
geriatrics BibRef

Zhang, Z.[Zhong], Conly, C.[Christopher], Athitsos, V.[Vassilis],
Evaluating Depth-Based Computer Vision Methods for Fall Detection under Occlusions,
ISVC14(II: 196-207).
Springer DOI 1501
BibRef

Demiröz, B.E.[Baris Evrim], Salah, A.A.[Albert Ali], Akarun, L.[Lale],
Coupling Fall Detection and Tracking in Omnidirectional Cameras,
HBU14(73-85).
Springer DOI 1411
BibRef

Kepski, M.[Michal], Kwolek, B.[Bogdan],
Person Detection and Head Tracking to Detect Falls in Depth Maps,
ICCVG14(324-331).
Springer DOI 1410
BibRef

Hung, D.H.[Dao Huu], Saito, H., Hsu, G.S.[Gee-Sern],
Detecting Fall Incidents of the Elderly Based on Human-Ground Contact Areas,
ACPR13(516-521)
IEEE DOI 1408
object detection BibRef

Jiang, M.[Mei], Chen, Y.[Yuyang], Zhao, Y.[Yanyun], Cai, A.[Anni],
A real-time fall detection system based on HMM and RVM,
VCIP13(1-6)
IEEE DOI 1402
geriatrics BibRef

Planinc, R.[Rainer], Kampel, M.[Martin],
Combining Spatial and Temporal Information for Inactivity Modeling,
ICPR14(4234-4239)
IEEE DOI 1412
BibRef
And:
Robust Fall Detection by Combining 3D Data and Fuzzy Logic,
CDF12(II:121-132).
Springer DOI 1304
Hidden Markov models BibRef

Zhang, Z.[Zhong], Liu, W.H.[Wei-Hua], Metsis, V.[Vangelis], Athitsos, V.[Vassilis],
A viewpoint-independent statistical method for fall detection,
ICPR12(3626-3630).
WWW Link. 1302
BibRef

Chen, Y.T.[Yie-Tarng], Lin, Y.R.[You-Rong], Fang, W.H.[Wen-Hsien],
A Novel Shadow-Assistant Human Fall Detection Scheme Using a Cascade of SVM Classifiers,
SSSPR12(710-718).
Springer DOI 1211
BibRef

Kepski, M.[Michal], Kwolek, B.[Bogdan],
Unobtrusive Fall Detection at Home Using Kinect Sensor,
CAIP13(457-464).
Springer DOI 1308
BibRef
Earlier:
Human Fall Detection by Mean Shift Combined with Depth Connected Components,
ICCVG12(457-464).
Springer DOI 1210
BibRef

Makantasis, K.[Konstantinos], Protopapadakis, E.[Eftychios], Doulamis, A.[Anastasios], Grammatikopoulos, L.[Lazaros], Stentoumis, C.[Christos],
Monocular Camera Fall Detection System Exploiting 3D Measures: A Semi-supervised Learning Approach,
ARTEMIS12(III: 81-90).
Springer DOI 1210
BibRef

Debard, G.[Glen], Karsmakers, P.[Peter], Deschodt, M.[Mieke], Vlaeyen, E.[Ellen], Dejaeger, E.[Eddy], Milisen, K.[Koen], Goedemé, T.[Toon], Vanrumste, B.[Bart], Tuytelaars, T.[Tinne],
Camera-Based Fall Detection on Real World Data,
WTFCV11(356-375).
Springer DOI 1210
BibRef

Meffre, A.[Alban], Collet, C.[Christophe], Lachiche, N.[Nicolas], Gançarski, P.[Pierre],
Real-Time Fall Detection Method Based on Hidden Markov Modelling,
ICISP12(521-530).
Springer DOI 1208
BibRef

Sokolova, M.V.[Marina V.], Fernández-Caballero, A.[Antonio],
Fuzzy Sets for Human Fall Pattern Recognition,
MCPR12(117-126).
Springer DOI 1208
BibRef

Humenberger, M.[Martin], Schraml, S.[Stephan], Sulzbachner, C.[Christoph], Belbachir, A.N.[Ahmed Nabil], Srp, A.[Agoston], Vajda, F.[Ferenc],
Embedded fall detection with a neural network and bio-inspired stereo vision,
ECVW12(60-67).
IEEE DOI 1207
BibRef

Dubey, R.[Rachit], Ni, B.B.[Bing-Bing], Moulin, P.[Pierre],
A Depth Camera Based Fall Recognition System for the Elderly,
ICIAR12(II: 106-113).
Springer DOI 1206
BibRef

Qian, H.M.[Hui-Min], Mao, Y.B.[Yao-Bin], Xiang, W.[Wenbo], Wang, Z.Q.[Zhi-Quan],
Home environment fall detection system based on a cascaded multi-SVM classifier,
ICARCV08(1567-1572).
IEEE DOI 1109
BibRef

Belbachir, A.N.[Ahmed Nabil], Schraml, S.[Stephan], Nowakowska, A.[Aneta],
Event-driven stereo vision for fall detection,
ECVW11(78-83).
IEEE DOI 1106
BibRef

Shoaib, M., Dragon, R., Ostermann, J.,
View-invariant Fall Detection for Elderly in Real Home Environment,
PSIVT10(52-57).
IEEE DOI 1011
BibRef

Zweng, A.[Andreas], Zambanini, S.[Sebastian], Kampel, M.[Martin],
Introducing a Statistical Behavior Model into Camera-Based Fall Detection,
ISVC10(I: 163-172).
Springer DOI 1011
See also Performance evaluation of an improved relational feature model for pedestrian detection. BibRef

Zweng, A.[Andreas], Rittler, T.[Thomas], Kampel, M.[Martin],
Evaluation of Histogram-Based Similarity Functions for Different Color Spaces,
CAIP11(II: 455-462).
Springer DOI 1109
BibRef

Chen, Y.T.[Yie-Tarng], Lin, Y.C.[Yu-Ching], Fang, W.H.[Wen-Hsien],
A hybrid human fall detection scheme,
ICIP10(3485-3488).
IEEE DOI 1009
BibRef

Huang, Y.C.[Yi-Chang], Miaou, S.G.[Shaou-Gang], Liao, T.Y.[Tsung-Yen],
A Human Fall Detection System Using an Omni-Directional Camera in Practical Environments for Health Care Applications,
MVA09(455-).
PDF File. 0905
BibRef

Doulamis, A.D., Doulamis, N.D., Kalisperakis, I., Stentoumis, C.,
A Real-time Single-camera Approach For Automatic Fall Detection,
CloseRange10(xx-yy).
PDF File. 1006
BibRef

Foroughi, H.[Homa], Rezvanian, A.[Alireza], Paziraee, A.[Amirhossien],
Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine,
ICCVGIP08(413-420).
IEEE DOI 0812
BibRef

Hazelhoff, L.[Lykele], Han, J.G.[Jun-Gong], de With, P.H.N.[Peter H.N.],
Video-Based Fall Detection in the Home Using Principal Component Analysis,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Distante, C.[Cosimo], Leone, A.[Alessandro], Malcovati, P.[Piero],
A multi-sensor approach for People Fall Detection in home environment,
M2SFA208(xx-yy). 0810
BibRef

Rougier, C.[Caroline], Meunier, J.[Jean], St-Arnaud, A.[Alain], Rousseau, J.[Jacqueline],
Procrustes Shape Analysis for Fall Detection,
VS08(xx-yy). 0810
BibRef

Vishwakarma, V.[Vinay], Mandal, C.[Chittaranjan], Sural, S.[Shamik],
Automatic Detection of Human Fall in Video,
PReMI07(616-623).
Springer DOI 0712
BibRef

Töreyin, B.U.[B. Ugur], Dedeoglu, Y.[Yigithan], Çetin, A.E.[A. Enis],
HMM Based Falling Person Detection Using Both Audio and Video,
CVHCI05(211).
Springer DOI 0601
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Lifelog .


Last update:Jun 23, 2018 at 14:58:54