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2112
Robots, Usability, Human-robot interaction, User experience,
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Body geometry, Elderly assistance, Fall detection,
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Multi-oriented run length, Static and dynamic features,
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2205
Feature extraction, Fall detection, Surveillance, Older adults,
Convolutional neural networks, Skeleton, Visualization,
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2205
Older adults, Biomedical monitoring, Medical services,
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2205
Human fall detection, Assistive living, Grassmann manifolds,
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Multi-human Fall Detection and Localization in Videos,
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2206
Fall detection, Video surveillance, Human action recognition,
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Neuromorphic Vision-Based Fall Localization in Event Streams With
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2208
Proposals, Cameras, Location awareness, Vision sensors, Standards,
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2208
Assistive technologies, Aging, Service robots, Statistics,
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Toyota Smarthome Untrimmed:
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PAMI(45), No. 2, February 2023, pp. 2533-2550.
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2301
Videos, Cameras, Telephone sets, Noise measurement, Annotations,
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Meng, R.[Ru],
Sun, N.[Ning],
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Privacy-Preserving Video Fall Detection via Chaotic Compressed
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MultMedMag(29), No. 4, October 2022, pp. 14-23.
IEEE DOI
2301
Aging, Feature extraction, Fall detection, Sparse matrices,
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Privacy-Preserving, Thermal Vision With Human in the Loop Fall
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HMS(53), No. 1, February 2023, pp. 164-175.
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2301
Fall detection, Sensors, Temperature sensors, Human in the loop,
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Gao, M.Q.[Meng-Qi],
Li, J.[Jiangjiao],
Zhou, D.Z.[Da-Zheng],
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Fall detection based on OpenPose and MobileNetV2 network,
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Detection of non-suicidal self-injury based on spatiotemporal
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IET-Bio(12), No. 2, 2023, pp. 91-101.
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behavioural sciences computing,
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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
Wu, L.[Lian],
Huang, C.[Chao],
Fei, L.[Lunke],
Zhao, S.P.[Shu-Ping],
Zhao, J.C.[Jian-Chuan],
Cui, Z.[Zhongwei],
Xu, Y.[Yong],
Video-Based Fall Detection Using Human Pose and Constrained
Generative Adversarial Network,
CirSysVideo(34), No. 4, April 2024, pp. 2179-2194.
IEEE DOI
2404
Fall detection, Feature extraction, Pose estimation, Cameras,
Generative adversarial networks, Privacy, Deep learning, unsupervised learning
BibRef
Umer, M.[Muhammad],
Alarfaj, A.A.[Aisha Ahmed],
Alabdulqader, E.A.[Ebtisam Abdullah],
Alsubai, S.[Shtwai],
Cascone, L.[Lucia],
Narducci, F.[Fabio],
Enhancing fall prediction in the elderly people using LBP features
and transfer learning model,
IVC(145), 2024, pp. 104992.
Elsevier DOI
2405
NASNet, Fall detection, Local binary patterns, Healthcare
BibRef
Cavallaro, A.[Antonella],
Perillo, F.[Francesca],
Romano, M.[Marco],
Sebillo, M.[Monica],
Vitiello, G.[Giuliana],
Social robot in service of the cognitive therapy of elderly people:
Exploring robot acceptance in a real-world scenario,
IVC(147), 2024, pp. 105072.
Elsevier DOI
2406
Social robot, Elderly, Psycological test
BibRef
Alanazi, T.[Thamer],
Babutain, K.[Khalid],
Muhammad, G.[Ghulam],
Mitigating human fall injuries: A novel system utilizing 3D 4-stream
convolutional neural networks and image fusion,
IVC(148), 2024, pp. 105153.
Elsevier DOI
2407
Human fall detection, 3D CNN, Multi-stream CNN, Autoencoders,
Support vector machines
BibRef
Patel, A.N.[Aryan Nikul],
Murugan, R.[Ramalingam],
Maddikunta, P.K.R.[Praveen Kumar Reddy],
Yenduri, G.[Gokul],
Jhaveri, R.H.[Rutvij H.],
Zhu, Y.[Yaodong],
Gadekallu, T.R.[Thippa Reddy],
AI-powered trustable and explainable fall detection system using
transfer learning,
IVC(149), 2024, pp. 105164.
Elsevier DOI
2408
Artificial intelligence, Explainable artificial intelligence,
Transfer learning, Deep neural networks, Fall detection
BibRef
Khan, H.[Habib],
Ullah, I.[Inam],
Shabaz, M.[Mohammad],
Omer, M.F.[Muhammad Faizan],
Usman, M.T.[Muhammad Talha],
Guellil, M.S.[Mohammed Seghir],
Koo, J.[JaKeoung],
Visionary vigilance: Optimized YOLOV8 for fallen person detection
with large-scale benchmark dataset,
IVC(149), 2024, pp. 105195.
Elsevier DOI Code:
WWW Link.
2408
Assisted living, Fall detection,
Data benchmarking, Visual intelligence, Safety monitoring
BibRef
Li, B.[Bin],
Li, J.[Jiangjiao],
Wang, P.[Peng],
Fall detection algorithm based on global and local feature extraction,
PRL(185), 2024, pp. 31-37.
Elsevier DOI
2410
Dual-stream network, Convolutional neural network,
Regional attention module, Transformer
BibRef
Nakabayashi, T.[Takuya],
Saito, H.[Hideo],
Unsupervised Fall Detection on Edge Devices,
MVA23(1-5)
DOI Link
2403
Training, Performance evaluation, Image edge detection,
Real-time systems, Servers, Fall detection, Older adults
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,
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, 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],
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Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Lifelog, Daily Activities .