17.1.3.7.9 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
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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.A.[Syed Moeen Ali], 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
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], Magaña-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

Gurbuz, S.Z., Amin, M.G.,
Radar-Based Human-Motion Recognition With Deep Learning: Promising applications for indoor monitoring,
SPMag(36), No. 4, July 2019, pp. 16-28.
IEEE DOI 1907
Monitoring, Radar imaging, Sensors, Transforms, Doppler radar, Deep learning 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

Mackù, 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.[Eugène 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
Earlier:
Person Detection and Head Tracking to Detect Falls in Depth Maps,
ICCVG14(324-331).
Springer DOI 1410
BibRef
Earlier:
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

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

Santemiz, P.[Pinar], Spreeuwers, L.J.[Luuk J.], Veldhuis, R.N.J.[Raymond N.J.],
Automatic face recognition for home safety using video-based side-view face images,
IET-Bio(7), No. 6, November 2018, pp. 606-614.
DOI Link 1811
BibRef

Martinez-Hernandez, U.[Uriel], Dehghani-Sanij, A.A.[Abbas A.],
Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor,
PRL(118), 2019, pp. 32-41.
Elsevier DOI 1902
Intent recognition, Sit-to-stand, Bayesian methods, Wearable sensors BibRef

Anderson, M., Anderson, S.L., Berenz, V.,
A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm,
PIEEE(107), No. 3, March 2019, pp. 526-540.
IEEE DOI 1903
Autonomous systems, Ethics, Autonomous automobiles, Rescue robots, Medical robotics, Artificial intelligence, Machine learning, robotics BibRef

Kong, Y.Q.[Yong-Qiang], Huang, J.H.[Jian-Hui], Huang, S.S.[Shan-Shan], Wei, Z.G.[Zhen-Gang], Wang, S.K.[Sheng-Ke],
Learning spatiotemporal representations for human fall detection in surveillance video,
JVCIR(59), 2019, pp. 215-230.
Elsevier DOI 1903
Fall detection, Human silhouette, Motion history image, Dynamic image, Convolutional Neural Networks, High-quality representation BibRef

Aviles-Cruz, C.[Carlos], Rodriguez-Martinez, E.[Eduardo], Villegas-Cortez, J.[Juan], Ferreyra-Ramirez, A.[Andrés],
Granger-causality: An efficient single user movement recognition using a smartphone accelerometer sensor,
PRL(125), 2019, pp. 576-583.
Elsevier DOI 1909
Granger-causality, Human activity recognition, Mobile device, Accelerometer, Gyroscope, Time-series BibRef

Alzahrani, M.S.[Mona Saleh], Jarraya, S.K.[Salma Kammoun], Ben-Abdallah, H.[Hanêne], Ali, M.S.[Manar Salamah],
Comprehensive evaluation of skeleton features-based fall detection from Microsoft Kinect v2,
SIViP(13), No. 7, October 2019, pp. 1431-1439.
Springer DOI 1911
BibRef

Feng, Q.[Qi], Gao, C.Q.[Chen-Qiang], Wang, L.[Lan], Zhao, Y.[Yue], Song, T.C.[Tie-Cheng], Li, Q.A.[Qi-Ang],
Spatio-temporal fall event detection in complex scenes using attention guided LSTM,
PRL(130), 2020, pp. 242-249.
Elsevier DOI 2002
Fall event detection, Fall event dataset, LSTM BibRef

Ortíz-Barrios, M.A.[Miguel Angel], Cleland, I.[Ian], Nugent, C.[Chris], Pancardo, P.[Pablo], Järpe, E.[Eric], Synnott, J.[Jonathan],
Simulated Data to Estimate Real Sensor Events: A Poisson-Regression-Based Modelling,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Li, Q.Z.[Quan-Zhe], Shin, S.B.[Sae-Byuk], Hong, C.P.[Chung-Pyo], Kim, S.D.[Shin-Dug],
On-body wearable device localization with a fast and memory efficient SVM-kNN using GPUs,
PRL(139), 2020, pp. 128-138.
Elsevier DOI 2011
Wearable sensor, Body location, Pattern recognition, kNN, GPU, OpenCL BibRef

Honarparvar, S.[Sepehr], Saeedi, S.[Sara], Liang, S.[Steve], Squires, J.[Jeremy],
Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition,
IJGI(10), No. 2, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Nishimura, S.[Satoshi], Vizcarra, J.[Julio], Oota, Y.[Yuichi], Fukuda, K.[Ken],
Collaborative Ontology Development and its Use for Video Annotation in Elderly Care Domain,
IEICE(E104-D), No. 5, May 2021, pp. 528-538.
WWW Link. 2105
BibRef

Qian, K.[Kun], Zhang, Z.X.[Zi-Xing], Yamamoto, Y.[Yoshiharu], Schuller, B.W.[Bjoern W.],
Artificial Intelligence Internet of Things for the Elderly: From Assisted Living to Health-Care Monitoring,
SPMag(38), No. 4, July 2021, pp. 78-88.
IEEE DOI 2107
Industries, Senior citizens, Sociology, Signal processing algorithms, Aging, Internet of Things BibRef

Edwards, J.[John],
Smart Home Technologies Are Saving Money and Lives: Reaching out in new directions, signal processing-supported smart technologies are rapidly changing - and improving - everyday life,
SPMag(38), No. 5, September 2021, pp. 8-11.
IEEE DOI 2109
Performance evaluation, Smart homes, Signal processing BibRef

Liu, J.X.[Ji-Xin], Tan, R.[Rong], Han, G.[Guang], Sun, N.[Ning], Kwong, S.[Sam],
Privacy-Preserving In-Home Fall Detection Using Visual Shielding Sensing and Private Information-Embedding,
MultMed(23), 2021, pp. 3684-3699.
IEEE DOI 2110
Feature extraction, Privacy, Senior citizens, Nonhomogeneous media, Visualization, Cameras, Fall detection, privacy-preserving, information embedded BibRef

Iglesias, A.[Ana], García, J.[Javier], García-Olaya, Á.[Ángel], Fuentetaja, R.[Raquel], Fernández, F.[Fernando], Romero-Garcés, A.[Adrián], Marfil, R.[Rebeca], Bandera, A.[Antonio], Ting, K.L.H.[Karine Lan Hing], Voilmy, D.[Dimitri], Dueñas, Á.[Álvaro], Suárez-Mejías, C.[Cristina],
Extending the Evaluation of Social Assistive Robots With Accessibility Indicators: The AUSUS Evaluation Framework,
HMS(51), No. 6, December 2021, pp. 601-612.
IEEE DOI 2112
Robots, Usability, Human-robot interaction, User experience, Service robots, Human-robot interaction, robotics and automation, and cybernetics BibRef

Ding, C.Z.[Cong-Zhang], Jia, Y.[Yong], Cui, G.L.[Guo-Long], Chen, C.[Chuan], Zhong, X.L.[Xiao-Ling], Guo, Y.[Yong],
Continuous Human Activity Recognition through Parallelism LSTM with Multi-Frequency Spectrograms,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zhou, L.[Li], Qu, X.[Xiao], Zhang, T.[Ting], Wu, J.X.[Jian-Xin], Yin, H.[Hao], Guan, H.Y.[Hong-Yan], Luo, Y.[Yan],
Prediction of pediatric activity intensity with wearable sensors and bi-directional LSTM models,
PRL(152), 2021, pp. 166-171.
Elsevier DOI 2112
Pediatric activity intensity, Data synchronization, Bi-directional LSTM BibRef

Beddiar, D.R.[Djamila Romaissa], Oussalah, M.[Mourad], Nini, B.[Brahim],
Fall detection using body geometry and human pose estimation in video sequences,
JVCIR(82), 2022, pp. 103407.
Elsevier DOI 2201
Body geometry, Elderly assistance, Fall detection, Pose estimation, Video sequence BibRef

Hadjadji, B.[Bilal], Saumard, M.[Matthieu], Aron, M.[Michael],
Multi-oriented run length based static and dynamic features fused with Choquet fuzzy integral for human fall detection in videos,
JVCIR(82), 2022, pp. 103375.
Elsevier DOI 2201
Multi-oriented run length, Static and dynamic features, Choquet fuzzy integral, Tree architecture, Human fall detection BibRef

Zhang, Y.L.[Yin-Long], Zheng, X.Y.[Xiao-Yan], Liang, W.[Wei], Zhang, S.C.[Si-Chao], Yuan, X.D.[Xu-Dong],
Visual Surveillance for Human Fall Detection in Healthcare IoT,
MultMedMag(29), No. 1, January 2022, pp. 36-46.
IEEE DOI 2205
Feature extraction, Fall detection, Surveillance, Older adults, Convolutional neural networks, Skeleton, Visualization, discriminant fall features BibRef

Mallick, R.[Rupayan], Yebda, T.[Thinhinane], Benois-Pineau, J.[Jenny], Zemmari, A.[Akka], Pech, M.[Marion], Amieva, H.[Hélène],
Detection of Risky Situations for Frail Adults With Hybrid Neural Networks on Multimodal Health Data,
MultMedMag(29), No. 1, January 2022, pp. 7-17.
IEEE DOI 2205
Older adults, Biomedical monitoring, Medical services, Neural networks, Wearable computers, Risk management, Smart Health BibRef

Soni, P.K.[Pramod Kumar], Choudhary, A.[Ayesha],
Grassmann manifold based framework for automated fall detection from a camera,
IVC(122), 2022, pp. 104431.
Elsevier DOI 2205
Human fall detection, Assistive living, Grassmann manifolds, Grassmann graph embedding discriminant analysis BibRef

Gomes, M.E.N.[Mouglas Eugênio Nasário], Macêdo, D.[David], Zanchettin, C.[Cleber], de-Mattos-Neto, P.S.G.[Paulo Salgado Gomes], Oliveira, A.[Adriano],
Multi-human Fall Detection and Localization in Videos,
CVIU(220), 2022, pp. 103442.
Elsevier DOI 2206
Fall detection, Video surveillance, Human action recognition, Machine vision, Deep networks, Action detection BibRef

Zhu, N.[Na], Zhao, G.Z.[Guang-Zhe], Zhang, X.L.[Xiao-Long], Jin, Z.X.[Zhe-Xue],
Falling motion detection algorithm based on deep learning,
IET-IPR(16), No. 11, 2022, pp. 2845-2853.
DOI Link 2208
BibRef

Chen, G.[Guang], Qu, S.Q.[San-Qing], Li, Z.J.[Zhi-Jun], Zhu, H.T.[Hai-Tao], Dong, J.X.[Jia-Xuan], Liu, M.[Min], Conradt, J.[Jörg],
Neuromorphic Vision-Based Fall Localization in Event Streams With Temporal-Spatial Attention Weighted Network,
Cyber(52), No. 9, September 2022, pp. 9251-9262.
IEEE DOI 2208
Proposals, Cameras, Location awareness, Vision sensors, Standards, Older adults, Feature extraction, Bioinspired vision, privacy-preserving BibRef

Ackerman, E.[Evan],
How Robots Can Help US Act and Feel Younger: Toyota's Gill Pratt on Enhancing Independence in Old Age,
Spectrum(59), No. 8, August 2022, pp. 26-31.
IEEE DOI 2208
Assistive technologies, Aging, Service robots, Statistics, Older adults, Social factors, Social implications of technology, Interviews BibRef

Dai, R.[Rui], Das, S.[Srijan], Sharma, S.[Saurav], Minciullo, L.[Luca], Garattoni, L.[Lorenzo], Bremond, F.[Francois], Francesca, G.[Gianpiero],
Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection,
PAMI(45), No. 2, February 2023, pp. 2533-2550.
IEEE DOI 2301
Videos, Cameras, Telephone sets, Noise measurement, Annotations, Task analysis, Medical services, Untrimmed videos, real-world settings BibRef

Liu, J.X.[Ji-Xin], Meng, R.[Ru], Sun, N.[Ning], Han, G.[Guang], Kwong, S.[Sam],
Privacy-Preserving Video Fall Detection via Chaotic Compressed Sensing and GAN-Based Feature Enhancement,
MultMedMag(29), No. 4, October 2022, pp. 14-23.
IEEE DOI 2301
Aging, Feature extraction, Fall detection, Sparse matrices, Visualization, Older adults, Accidents, Privacy, Compressed sensing, Visual privacy protection BibRef

Naser, A.[Abdallah], Lotfi, A.[Ahmad], Mwanje, M.D.[Maria Drolence], Zhong, J.[Junpei],
Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System,
HMS(53), No. 1, February 2023, pp. 164-175.
IEEE DOI 2301
Fall detection, Sensors, Temperature sensors, Human in the loop, Older adults, Histograms, Feature extraction, thermal sensor array (TSA) BibRef

Gao, M.Q.[Meng-Qi], Li, J.[Jiangjiao], Zhou, D.Z.[Da-Zheng], Zhi, Y.[Yumin], Zhang, M.L.[Ming-Liang], Li, B.[Bin],
Fall detection based on OpenPose and MobileNetV2 network,
IET-IPR(17), No. 3, 2023, pp. 722-732.
DOI Link 2303
BibRef

Zhang, X.P.[Xiao-Ping], Ji, J.[Jiahui], Wang, L.[Li], He, Z.[Zhonghe], Liu, S.[Shida],
Image-based fall detection in bus compartment scene,
IET-IPR(17), No. 4, 2023, pp. 1181-1194.
DOI Link 2303
bus compartment, fall detection, feature extraction, pose estimation BibRef

Migliorelli, L.[Lucia], Tiribelli, S.[Simona], Cacciatore, A.[Alessandro], Giovanola, B.[Benedetta], Frontoni, E.[Emanuele], Moccia, S.[Sara],
Accountable Deep-Learning-Based Vision Systems for Preterm Infant Monitoring,
Computer(56), No. 5, May 2023, pp. 84-93.
IEEE DOI 2305
Ethics, Pediatrics, Machine vision, Monitoring BibRef

Feng, X.[Xiang], Shan, Z.L.[Zheng-Liang], Zhao, Z.F.[Zhan-Feng], Xu, Z.R.[Zi-Rui], Zhang, T.P.[Tian-Peng], Zhou, Z.[Zihe], Deng, B.[Bo], Guan, Z.[Zirui],
Millimeter-Wave Radar Monitoring for Elder's Fall Based on Multi-View Parameter Fusion Estimation and Recognition,
RS(15), No. 8, 2023, pp. 2101.
DOI Link 2305
BibRef

Yang, G.[Guanci], Yang, S.Y.[Si-Yuan], Luo, K.[Kexin], Lan, S.G.[Shan-Gen], He, L.[Ling], Li, Y.[Yang],
Detection of non-suicidal self-injury based on spatiotemporal features of indoor activities,
IET-Bio(12), No. 2, 2023, pp. 91-101.
DOI Link 2305
behavioural sciences computing, convolutional neural nets, feature extraction, object detection BibRef

Pérez, F.M.[Francisco Maciá], Fonseca, I.L.[Iren Lorenzo], Martínez, J.V.B.[José Vicente Berná], Maciá-Fiteni, A.[Alex],
Distributed Architecture for an Elderly Accompaniment Service Based on IoT Devices, AI, and Cloud Services,
MultMedMag(30), No. 1, January 2023, pp. 17-27.
IEEE DOI 2305
Older adults, Statistics, Social factors, Medical services, Aging, Internet of Things, Diseases, Social networking (online), Quality of Life BibRef

Pequeño-Zurro, A.[Alejandro], Ignasov, J.[Jevgeni], Ramírez, E.R.[Eduardo R.], Haarslev, F.[Frederik], Juel, W.K.[William K.], Bodenhagen, L.[Leon], Krüger, N.[Norbert], Shaikh, D.[Danish], Rañó, I.[Iñaki], Manoonpong, P.[Poramate],
Proactive Control for Online Individual User Adaptation in a Welfare Robot Guidance Scenario: Toward Supporting Elderly People,
SMCS(53), No. 6, June 2023, pp. 3364-3376.
IEEE DOI 2305
Robots, Older adults, Legged locomotion, Behavioral sciences, Navigation, Visualization, Adaptation models, Adaptive behavior, welfare robot BibRef

Elagovan, R.[Ramanujam], Perumal, T.[Thinagaran], Krishnan, S.[Shankar],
Fall Detection Systems at Night,
Computer(56), No. 6, June 2023, pp. 44-51.
IEEE DOI 2306
Fall detection, Older adults, Sensors, Cameras, Biomedical monitoring, Lighting, Wearable computers, Privacy BibRef

Morawski, I.[Igor], Lie, W.N.[Wen-Nung], Aing, L.[Lee], Chiang, J.C.[Jui-Chiu], Chen, K.T.[Kuan-Ting],
Deep-Learning Technique for Risk-Based Action Prediction Using Extremely Low-Resolution Thermopile Sensor Array,
CirSysVideo(33), No. 6, June 2023, pp. 2852-2863.
IEEE DOI 2306
BibRef
Earlier: A1, A2, A4, Only:
Action Prediction Using Extremely Low-Resolution Thermopile Sensor Array for Elderly Monitoring,
ICIP21(984-988)
IEEE DOI 2201
Older adults, Monitoring, Task analysis, Accidents, Cameras, Statistics, Sociology, Action prediction, thermopile sensor array, low resolution. Privacy, Hospitals, Senior citizens, Sociology, Predictive models, Cameras BibRef

Wang, Y.C.[You-Chiun], Yao, Y.N.[Yi-No],
Design and Implementation of a Simulator to Emulate Elder Behavior in a Nursing Home,
IEICE(E106-D), No. 6, June 2023, pp. 1155-1164.
WWW Link. 2306
BibRef

Truong, C.[Charles], Atiq, M.[Mounir], Minvielle, L.[Ludovic], Serra, R.[Renan], Mougeot, M.[Mathilde], Vayatis, N.[Nicolas],
A Data Set for Fall Detection with Smart Floor Sensors,
IPOL(13), 2023, pp. 183-197.
DOI Link 2307
Dataset, Fall Detection. BibRef

Jafarpournaser, N.[Naghmeh], Delavar, M.R.[Mahmoud Reza], Noroozian, M.[Maryam],
A Wandering Detection Method Based on Processing GPS Trajectories Using the Wavelet Packet Decomposition Transform for People with Cognitive Impairment,
IJGI(12), No. 9, 2023, pp. 379.
DOI Link 2310
BibRef

Gutiérrez, J.[Jesús], Martin, S.[Sergio], Rodriguez, V.[Victor],
Human stability assessment and fall detection based on dynamic descriptors,
IET-IPR(17), No. 11, 2023, pp. 3177-3195.
DOI Link 2310
convolutional neural nets, image processing BibRef

Zeng, G.T.[Guo-Tian], Zeng, B.[Bi], Hu, H.T.[Hui-Ting],
Real-world efficient fall detection: Balancing performance and complexity with FDGA workflow,
CVIU(237), 2023, pp. 103832.
Elsevier DOI 2311
Fall detection, Skeletal information, Genetic Algorithm, Feature selection, Performance-speed trade-off BibRef

Zhao, R.Y.[Rong-Yong], Wang, Y.[Yan], Jia, P.[Ping], Zhu, W.J.[Wen-Jie], Li, C.L.[Cui-Ling], Ma, Y.L.[Yun-Long], Li, M.[Miyuan],
Abnormal Behavior Detection Based on Dynamic Pedestrian Centroid Model: Case Study on U-Turn and Fall-Down,
ITS(24), No. 8, August 2023, pp. 8066-8078.
IEEE DOI 2308
Behavioral sciences, Feature extraction, Biological system modeling, Convolutional neural networks, pedestrian kinematics BibRef

Li, C.L.[Cui-Ling], Zhao, R.Y.[Rong-Yong], Wang, Y.[Yan], Jia, P.[Ping], Zhu, W.J.[Wen-Jie], Ma, Y.L.[Yun-Long], Li, M.[Miyuan],
Disturbance Propagation Model of Pedestrian Fall Behavior in a Pedestrian Crowd and Elimination Mechanism Analysis,
ITS(25), No. 2, February 2024, pp. 1519-1529.
IEEE DOI 2402
Pedestrians, Behavioral sciences, Fall detection, Cameras, Accidents, Numerical models, Force, Fall behavior, pressure characteristics, disturbance elimination BibRef


Noor, N.[Nadhira], Park, I.K.[In Kyu],
A Lightweight Skeleton-Based 3D-CNN for Real-Time Fall Detection and Action Recognition,
JRDB23(2171-2180)
IEEE DOI 2401
BibRef

Ochi, S.[Shunsuke], Miura, J.[Jun],
Depth-based In-bed Human Pose Estimation with Synthetic Dataset Generation and Deep Keypoint Estimation,
ACVR22(672-685).
Springer DOI 2304
BibRef

Kim, I.[Inkyung], Kim, D.[Daehee], Kwon, S.[Sunyoung], Lee, S.[Sheayun], Lee, J.[Jaekoo],
Fall Detection using Biometric Information Based on Multi-Horizon Forecasting,
ICPR22(1364-1370)
IEEE DOI 2212
Biometrics (access control), Biological system modeling, Predictive models, Transformers, Behavioral sciences, Older adults BibRef

Kim, K.[Kijung], Yun, G.[Guhnoo], Park, S.K.[Sung-Kee], Kim, D.H.[Dong Hwan],
Efficient Fall Detection for a Healthcare Robot System Based on 3-Axis Accelerometer and Depth Sensor Fusion with LSTM Networks,
ICPR22(2207-2212)
IEEE DOI 2212
Accelerometers, Visualization, Medical services, Sensor fusion, Vision sensors, Robot sensing systems, Feature extraction BibRef

Gan, C.[Chuang], Gu, Y.[Yi], Zhou, S.Y.[Si-Yuan], Schwartz, J.[Jeremy], Alter, S.[Seth], Traer, J.[James], Gutfreund, D.[Dan], Tenenbaum, J.B.[Joshua B.], McDermott, J.H.[Josh H.], Torralba, A.[Antonio],
Finding Fallen Objects Via Asynchronous Audio-Visual Integration,
CVPR22(10513-10523)
IEEE DOI 2210
Location awareness, Visualization, Robot vision systems, Virtual environments, Reinforcement learning, Reflection, Vision + X BibRef

Ha, T.V.[Thao V.], Nguyen, H.[Hoang], Huynh, S.T.[Son T.], Nguyen, T.T.[Trung T.], Nguyen, B.T.[Binh T.],
Fall Detection Using Multimodal Data,
MMMod22(I:392-403).
Springer DOI 2203
BibRef

Zahan, S.[Sania], Hassan, G.M.[Ghulam Mubashar], Mian, A.[Ajmal],
Modeling Human Skeleton Joint Dynamics for Fall Detection,
DICTA21(01-07)
IEEE DOI 2201
Privacy, Computational modeling, Sociology, Senior citizens, Streaming media, Feature extraction, Skeleton BibRef

Rai, N.[Nishant], Chen, H.F.[Hao-Feng], Ji, J.W.[Jing-Wei], Desai, R.[Rishi], Kozuka, K.[Kazuki], Ishizaka, S.[Shun], Adeli, E.[Ehsan], Niebles, J.C.[Juan Carlos],
Home Action Genome: Cooperative Compositional Action Understanding,
CVPR21(11179-11188)
IEEE DOI 2111
Location awareness, Learning systems, Annotations, Image color analysis, Genomics, Data visualization BibRef

Maruyama, T.[Tsubasa], Toda, H.[Haruki], Endo, Y.[Yui], Tada, M.[Mitsunori], Hagiwara, H.[Hiroyuki], Kitamura, K.[Koji],
Digital Human Simulation for Fall Risk Evaluation When Sitting on Stepladders,
DHM21(I:58-66).
Springer DOI 2108
BibRef

Mehta, V.[Vineet], Dhall, A.[Abhinav], Pal, S.[Sujata], Khan, S.S.[Shehroz S.],
Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging,
ICPR21(6321-6328)
IEEE DOI 2105
Privacy, Tracking, Cameras, Pattern recognition, Health and safety, Fall detection, thermal BibRef

O'Gorman, L.[Lawrence], Liu, X.Y.[Xin-Yi], Sarker, M.I.[Md Imran], Milanova, M.[Mariofanna],
Video Analytics Gait Trend Measurement for Fall Prevention and Health Monitoring,
ICPR21(489-496)
IEEE DOI 2105
Visual analytics, Time series analysis, Sociology, Senior citizens, Tools, Market research, Time measurement, gait, pose estimation, video analytics BibRef

Dentamaro, V.[Vincenzo], Gattulli, V.[Vincenzo], Giglio, P.[Paolo], Impedovo, D.[Donato], Pirlo, G.[Giuseppe],
Human Description in the Wild: Description of the Scene with Ensembles of AI Models,
SSSPR22(311-322).
Springer DOI 2301
Natural language description of a scene - Action recognition models, face recognition with gender and age and clothing recognition. BibRef

Dentamaro, V.[Vincenzo], Impedovo, D.[Donato], Pirlo, G.[Giuseppe],
Fall Detection by Human Pose Estimation and Kinematic Theory,
ICPR21(2328-2335)
IEEE DOI 2105
Decision support systems, Computational modeling, Pose estimation, Pipelines, Kinematics, Feature extraction, eXplainable-AI BibRef

Romaissa, B.D.[Beddiar Djamila], Mourad, O.[Oussalah], Brahim, N.[Nini],
Vision-Based Multi-Modal Framework for Action Recognition,
ICPR21(5859-5866)
IEEE DOI 2105
Visualization, Correlation, Video sequences, Transfer learning, Activity recognition, Feature extraction, Video surveillance BibRef

Romaissa, B.D.[Beddiar Djamila], Mourad, O.[Oussalah], Brahim, N.[Nini], Yazid, B.[Bounab],
Fall Detection using Body Geometry in Video Sequences,
IPTA20(1-5)
IEEE DOI 2206
BibRef
And:
Vision-based Fall Detection Using Body Geometry,
HCAU20(170-185).
Springer DOI 2103
Geometry, Support vector machines, Head, Video sequences, Senior citizens, Tools, Hip, Fall Detection, Elderly assistance, Pretrained models. BibRef

Chitti, E.[Eleonora], Pezzera, M.[Manuel], Borghese, N.A.[N. Alberto],
Multimodal Empathic Feedback Through a Virtual Character,
CARE20(156-162).
Springer DOI 2103
BibRef

Apicella, A.[Andrea], Snidaro, L.[Lauro],
Deep Neural Networks for Real-time Remote Fall Detection,
CARE20(188-201).
Springer DOI 2103
BibRef

Agnihotri, M.[Manish], Rao, S.B.P.[S. B. Pooja], Jayagopi, D.B.[Dinesh Babu], Hebbar, S.[Sushranth], Rasipuram, S.[Sowmya], Maitra, A.[Anutosh], Sengupta, S.[Shubhashis],
Towards Generating Topic-driven and Affective Responses to Assist Mental Wellness,
CARE20(129-143).
Springer DOI 2103
BibRef

Chessa, M.[Manuela], Bassano, C.[Chiara], Solari, F.[Fabio],
A WebGL Virtual Reality Exergame for Assessing the Cognitive Capabilities of Elderly People: A Study About Digital Autonomy for Web-based Applications,
CARE20(163-170).
Springer DOI 2103
BibRef

Leotta, M.[Maurizio], Fasciglione, A.[Andrea], Verri, A.[Alessandro],
Daily Living Activity Recognition Using Wearable Devices: A Features-rich Dataset and a Novel Approach,
CARE20(171-187).
Springer DOI 2103
BibRef

Wang, X.Y.[Xue-Yi], Risi, N.[Nicoletta], Talavera, E.[Estefanía], Chicca, E.[Elisabetta], Karastoyanova, D.[Dimka], Azzopardi, G.[George],
Fall Detection with Event-based Data: A Case Study,
CAIP23(II:33-42).
Springer DOI 2312
BibRef
Earlier: A1, A3, A5, A6, Only:
Fall Detection and Recognition from Egocentric Visual Data: A Case Study,
AIHA20(431-443).
Springer DOI 2103
BibRef

Saidi, H., Labraoui, N., Ari, A.A.A., Bouida, D.,
Remote health monitoring system of elderly based on Fog to Cloud (F2C) computing,
ISCV20(1-7)
IEEE DOI 2011
cloud computing, data privacy, geriatrics, health care, Internet of Things, medical information systems, Medical Data BibRef

Wang, X., Jia, K.,
Human Fall Detection Algorithm Based on YOLOv3,
ICIVC20(50-54)
IEEE DOI 2009
Training, Feature extraction, Detection algorithms, Machine learning, Convolution, Senior citizens, Object detection, fall detection BibRef

Kramer, J.B., Sabalka, L., Rush, B., Jones, K., Nolte, T.,
Automated Depth Video Monitoring For Fall Reduction: A Case Study,
CVPM20(1188-1196)
IEEE DOI 2008
Hospitals, Monitoring, Injuries, Sensitivity, Biomedical imaging BibRef

Masalha, A., Eichler, N., Raz, S., Toledano-Shubi, A., Niv, D., Shimshoni, I., Hel-Or, H.,
Predicting Fall Probability Based on a Validated Balance Scale,
CVPM20(1224-1231)
IEEE DOI 2008
Task analysis, Cameras, Sensors, Calibration, Medical diagnostic imaging, Tools BibRef

Hua, M., Nan, Y., Lian, S.,
Falls Prediction Based on Body Keypoints and Seq2Seq Architecture,
HBU19(1251-1259)
IEEE DOI 2004
fall detection, image classification, image colour analysis, object tracking, video signal processing, sequence to sequence BibRef

Seredin, O.S., Kopylov, A.V., Huang, S.C., Rodionov, D.S.,
A Skeleton Features-based Fall Detection Using Microsoft Kinect V2 With One Class-classifier Outlier Removal,
PTVSBB19(189-195).
DOI Link 1912
BibRef

Wang, P., Lien, S., Lee, M.,
A Learning-Based Prediction Model for Baby Accidents,
ICIP19(629-633)
IEEE DOI 1910
BibRef

Masullo, A.[Alessandro], Burghardt, T.[Tilo], Perrett, T.[Toby], Damen, D.[Dima], Mirmehdi, M.[Majid],
Sit-to-Stand Analysis in the Wild Using Silhouettes for Longitudinal Health Monitoring,
ICIAR19(II:175-185).
Springer DOI 1909
BibRef

Zhang, Y.[Yan], Neumann, H.[Heiko],
An Empirical Study Towards Understanding How Deep Convolutional Nets Recognize Falls,
ACVR18(VI:112-127).
Springer DOI 1905
BibRef

Xu, Y., Damen, D.,
Human Routine Change Detection using Bayesian Modelling,
ICPR18(1833-1838)
IEEE DOI 1812
Data models, Bayes methods, Computational modeling, Feature extraction, Force, Mathematical model, Maximum likelihood estimation BibRef

Tremblay, J., To, T., Birchfield, S.,
Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation,
DeepLearnRV18(2119-21193)
IEEE DOI 1812
Pose estimation, Cameras, Fats, Training, Object detection, Robots BibRef

Malhotr, K.R., Davoudi, A., Siegel, S., Bihorac, A., Rashidi, P.,
Autonomous Detection of Disruptions in the Intensive Care Unit Using Deep Mask R-CNN,
WiCV18(1944-19442)
IEEE DOI 1812
Hospitals, Circadian rhythm, Biomedical imaging, Timing, Lighting BibRef

Tran, T., Le, T., Pham, D., Hoang, V., Khong, V., Tran, Q., Nguyen, T., Pham, C.,
A multi-modal multi-view dataset for human fall analysis and preliminary investigation on modality,
ICPR18(1947-1952)
IEEE DOI 1812
Skeleton, Accelerometers, Wearable sensors, Cameras, Feature extraction, Acceleration BibRef

Haque, M.A.[Mohammad A.], Kjeldsen, S.S.[Simon S.], Arguissain, F.G.[Federico G.], Brunner, I.[Iris], Nasrollahi, K.[Kamal], Andersen, O.K.[Ole Kæseler], Nielsen, J.F.[Jørgen F.], Moeslund, T.B.[Thomas B.], Jørgensen, A.[Anders],
Patient's Body Motion Study Using Multimodal RGBDT Videos,
ISVC18(552-564).
Springer DOI 1811
Bed rest for rehab. BibRef

Wu, T.Y.[Tz-Ying], Lin, J.T.[Juan-Ting], Wang, T.H.[Tsun-Hsuang], Hu, C.W.[Chan-Wei], Niebles, J.C.[Juan Carlos], Sun, M.[Min],
Liquid Pouring Monitoring via Rich Sensory Inputs,
ECCV18(XI: 352-369).
Springer DOI 1810
BibRef

Yuan, Y.[Ye], Kitani, K.[Kris],
3D Ego-Pose Estimation via Imitation Learning,
ECCV18(XVI: 763-778).
Springer DOI 1810
Person's pose from wearable camera. BibRef

Sehairi, K., Chouireb, F., Meunier, J.,
Elderly fall detection system based on multiple shape features and motion analysis,
ISCV18(1-8)
IEEE DOI 1807
age issues, feature extraction, finite state machines, health care, image classification, image motion analysis, image segmentation, video surveillance BibRef

Nakagawa, H.[Hiromi], Tukamoto, M.[Masahiro], Yamashiro, K.[Kazuaki], Goto, A.[Akihiko],
Study of Factors that Lead to Falls During Body Position Change from a Dorsal Position to a Seated Position by Nursing Students,
DHM18(205-216).
Springer DOI 1807
BibRef

Homma, K.[Keiko], Fujiwara, K.[Kiyoshi], Kajitani, I.[Isamu], Ogure, T.[Takuya],
Development of Safety Testing Technologies of Defecation Assist Devices,
DHM18(419-428).
Springer DOI 1807
BibRef

Kitajima, Y.[Yasuko], Ikuhisa, K.[Ken], Sirisuwan, P.[Porakoch], Goto, A.[Akihiko], Hamada, H.[Hiroyuki],
Increasing Safety for Assisted Motion During Caregiving,
DHM18(429-439).
Springer DOI 1807
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, 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], Moyà-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

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

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

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.Y.[Yu-Yang], Zhao, Y.Y.[Yan-Yun], Cai, A.N.[An-Ni],
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

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

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.B.[Wen-Bo], 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
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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-).
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Doulamis, A.D., Doulamis, N.D., Kalisperakis, I., Stentoumis, C.,
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Foroughi, H.[Homa], Rezvanian, A.[Alireza], Paziraee, A.[Amirhossien],
Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine,
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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
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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 -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Lifelog, Daily Activities .


Last update:Mar 16, 2024 at 20:36:19