research-article

Human fall detection and activity monitoring: a comparative analysis of vision-based methods for classification and detection techniques

Authors:

Shikha Rastogi,

Jaspreet SinghAuthors Info & Claims

Soft Computing, Volume 26, Issue 8

Pages 3679 - 3701

https://doi.org/10.1007/s00500-021-06717-x

Published: 01 April 2022 Publication History

Abstract

Fall detection (FD) system tends to monitor the fall events with restricted movement patterns and provides alerts to detect actions and corresponds to human falls. Based on high-level features, the resultant information often requires well-detected results like activity monitoring, detection, and classification. The objective of the study focuses on the vision-based FD and activity monitoring (AM) methods using different types of cameras and determines the finest method for different backgrounds and complex surroundings in outdoor and indoor scenes. Several works of literature provide various detection algorithms which cannot differentiate the fall from other actions. So, there is a need for efficient detection techniques which can efficiently work on all sorts of fall event images. Also, the AM algorithm lies in different classification techniques but it is not robust to classify the actions being the same speed with the fall such as jumping, bending, etc. In this paper, we view the comparative study of vision-based FD and monitoring techniques such as Inactivity/Body shape change based, Posture based, 3D head motion-based, Spatial–temporal based, Gait based and skeleton tracking techniques based on the source of their techniques, types, description, advantages, and disadvantages. In addition, several performance metrics were used to evaluate the results and compare the resulting study with the previous comparative evaluations. This comparative analysis leads to a deeper understanding of different FD and AM techniques and suggests the possible direction for the researchers to identify a suitable method for their needs.

References

[1]

Abobakr A, Hossny M, and Nahavandi S A skeleton-free fall detection system from depth images using random decision forest IEEE Syst J 2017 12 3 2994-3005

[2]

Aguiar B, Rocha T, Silva J, Sousa I (2014) Accelerometer-based fall detection for smartphones. In: 2014 IEEE international symposium on medical measurements and applications (MeMeA), pp 1–6

[3]

Alaoui AY, El Hassouny A, Thami RO, Tairi H (2017) Video based human fall detection using von mises distribution of motion vectors. In: 2017 Intelligent systems and computer vision (ISCV). IEEE, pp 1–5

[4]

Alaoui AY, El Fkihi S, and Thami ROH Fall detection for elderly people using the variation of key points of human skeleton IEEE Access 2019 7 154786-154795

[5]

Alhimale L, Zedan H, and Al-Bayatti A The implementation of an intelligent and video-based fall detection system using a neural network Appl Soft Comput 2014 18 59-69

[6]

Anishchenko L (2018) Machine learning in video surveillance for fall detection. In: 2018 ural symposium on biomedical engineering, radioelectronics and information technology (USBEREIT). IEEE, pp 99–102

[7]

Ariz M, Bengoechea JJ, Villanueva A, and Cabeza R A novel 2D/3D database with automatic face annotation for head tracking and pose estimation Comput vis Image Understanding 2016 148 201-210

[8]

Ariz M, Villanueva A, and Cabeza R Robust and accurate 2D-tracking-based 3D positioning method: application to head pose estimation Comput vis Image Understanding 2019 180 13-22

[9]

Beddiar DR, Nini B (2017) Vision based abnormal human activities recognition: an overview. In: 2017 8th international conference on information technology (ICIT). IEEE, pp 548–553

[10]

Bian ZP, Hou J, Chau LP, and Magnenat-Thalmann N Fall detection based on body part tracking using a depth camera IEEE J Biomed Health Inform 2014 19 2 430-439

[11]

Biswas A, Dey B (2019) A literature review of current vision based fall detection methods. In: International conference on communication, devices and networking. Springer, Singapore, pp 411–421

[12]

Braham M, Van Droogenbroeck M (2016) Deep background subtraction with scene-specific convolutional neural networks. In: 2016 international conference on systems signals and image processing (IWSSIP) IEEE, pp 1–4

[13]

Brunetti A, Buongiorno D, Trotta GF, and Bevilacqua V Computer vision and deep learning techniques for pedestrian detection and tracking: a survey Neurocomputing 2018 300 17-33

[14]

Cameron R, Zuo Z, Sexton G, Yang L (2017) A fall detection/recognition system and an empirical study of gradient-based feature extraction approaches. In: UK workshop on computational intelligence, pp 276–289

[15]

Chaaraoui AA, Padilla-López JR, Climent-Pérez P, and Flórez-Revuelta F Evolutionary joint selection to improve human action recognition with RGB-D devices Expert Syst Appl 2014 41 3 786-794

[16]

Chaccour K, Darazi R, El Hassani AH, and Andres E From fall detection to fall prevention: a generic classification of fall-related systems IEEE Sens J 2016 17 3 812-822

[17]

Chen Z, Ellis T, and Velastin SA Vision-based traffic surveys in urban environments J Electron Imaging 2016 25 5 051206

[18]

Chua JL, Chang YC, and Lim WK A simple vision-based fall detection technique for indoor video surveillance SIViP 2015 9 3 623-633

[19]

Debard G, Baldewijns G, Goedemé T, Tuytelaars T, Vanrumste B (2015) Camera-based fall detection using a particle filter. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 6947–6950

[20]

Espinosa R, Ponce H, Gutiérrez S, Martínez-Villaseñor L, Brieva J, Moya-Albor E (2020) Application of convolutional neural networks for fall detection using multiple cameras. In: Challenges and trends in multimodal fall detection for healthcare. Springer, Cham, pp 97–120

[21]

Fan Y, Levine MD, Wen G, and Qiu S A deep neural network for real-time detection of falling humans in naturally occurring scenes Neurocomputing 2017 260 43-58

[22]

Fan K, Wang P, and Zhuang S Human fall detection using slow feature analysis Multimed Tools Appl 2019 78 7 9101-9128

[23]

Feng W, Liu R, and Zhu M Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera Signal Image Video Process 2014 8 6 1129-1138

[24]

Feng Q, Gao C, Wang L, Zhao Y, Song T, and Li Q Spatio-temporal fall event detection in complex scenes using attention guided LSTM Pattern Recogn Lett 2020 130 242-249

[25]

Gomes V, Barcellos P, and Scharcanski J Stochastic shadow detection using a hypergraph partitioning approach Pattern Recogn 2017 63 30-44

[26]

González I, López-Nava IH, Fontecha J, Muñoz-Meléndez A, Pérez-SanPablo AI, and Quiñones-Urióstegui I Comparison between passive vision-based system and a wearable inertial-based system for estimating temporal gait parameters related to the GAITRite electronic walkway J Biomed Inform 2016 62 210-223

[27]

Gopalakrishnan K, Khaitan SK, Choudhary A, and Agrawal A Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection Constr Build Mater 2017 157 322-330

[28]

Gracewell JJ and Pavalarajan S Fall detection based on posture classification for smart home environment J Ambient Intell Human Comput 2019 20 1-8

[29]

Gutiérrez J, Rodríguez V, and Martin S Comprehensive review of vision-based fall detection systems Sensors 2021 21 3 947

[30]

Han S and Lee S A vision-based motion capture and recognition framework for behavior-based safety management Autom Constr 2013 35 131-141

[31]

Hanghan L (2017) Fall detection using wavelet transform and support vector machine. Diss. School of Telecommunication Engineering Institute of Engineering Suranaree University of Technology

[32]

Harrou F, Zerrouki N, Sun Y, and Houacine A Vision-based fall detection system for improving safety of elderly people IEEE Instrum Meas Mag 2017 20 6 49-55

[33]

Harrou F, Zerrouki N, Sun Y, and Houacine A An integrated vision-based approach for efficient human fall detection in a home environment IEEE Access 2019 7 114966-114974

[34]

Hassanalieragh M, Page A, Soyata T, Sharma G, Aktas M, Mateos G, Andreescu S (2015) Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: opportunities and challenges. In: 2015 IEEE international conference on services computing. IEEE, pp 285–292

[35]

He Y, Li Y, Bao SD (2012) Fall detection by built-in tri-accelerometer of smartphone. In: Proceedings of 2012 IEEE-EMBS international conference on biomedical and health informatics, pp 184–187

[36]

Hsu YW, Perng JW, Liu HL (2015) Development of a vision based pedestrian fall detection system with back propagation neural network. In: 2015 IEEE/SICE international symposium on system integration (SII), pp 433–437

[37]

Huang Z, Liu Y, Fang Y, Horn BK (2018) Video-based fall detection for seniors with human pose estimation. In: 2018 4th international conference on universal village (UV). IEEE, pp 1–4

[38]

Igual R, Medrano C, and Plaza I A comparison of public datasets for acceleration-based fall detection Med Eng Phys 2015 37 9 870-878

[39]

Jansi R and Amutha R Detection of fall for the elderly in an indoor environment using a tri-axial accelerometer and Kinect depth data Multidimen Syst Signal Process 2020 31 4 1207-1225

[40]

Jeni LA, Cohn JF, and Kanade T Dense 3d face alignment from 2d video for real-time use Image vis Comput 2017 58 13-24

[41]

Ji X, Cheng J, Tao D, Wu X, and Feng W The spatial Laplacian and temporal energy pyramid representation for human action recognition using depth sequences Knowl Based Syst 2017 122 64-74

[42]

Kataoka H, Aoki Y, Iwata K, Satoh Y (2015) Evaluation of vision-based human activity recognition in dense trajectory framework, In: International symposium on visual computing, pp 634–646

[43]

Kepski M, Kwolek B (2014) Fall detection using ceiling-mounted 3d depth camera. In: 2014 International conference on computer vision theory and applications (VISAPP), vol, 2. IEEE, pp 640–647

[44]

Kerdegari H, Samsudin K, Ramli AR, Mokaram S (2012) Evaluation of fall detection classification approaches. In: 2012 4th international conference on intelligent and advanced systems (ICIAS2012), vol, 1. IEEE, pp 131–136

[45]

Khan SS and Hoey J Review of fall detection techniques: a data availability perspective Med Eng Phys 2017 39 12-22

[46]

Kianoush S, Savazzi S, Vicentini F, Rampa V, and Giussani M Device-free RF human body fall detection and localization in industrial workplaces IEEE Internet Things J 2016 4 2 351-362

[47]

Kong Y, Huang J, Huang S, Wei Z, and Wang S Learning spatiotemporal representations for human fall detection in surveillance video J vis Commun Image Represent 2019 59 215-230

[48]

Kozina S, Gjoreski H, Gams M, Luštrek M (2013) Efficient activity recognition and fall detection using accelerometers. In: International competition on evaluating AAL systems through competitive benchmarking. Springer, Berlin, Heidelberg, pp 13–23

[49]

Kumar G, Bhatia PK (2014) A detailed review of feature extraction in image processing systems. In: 2014 Fourth international conference on advanced computing communication technologies. IEEE, pp 5–12

[50]

Kwolek B and Kepski M Human fall detection on embedded platform using depth maps and wireless accelerometer Comput Methods Programs Biomed 2014 117 3 489-501

[51]

Kwolek B and Kepski M Fuzzy inference-based fall detection using kinect and body-worn accelerometer Appl Soft Comput 2016 40 305-318

[52]

Lapierre N, Neubauer N, Miguel-Cruz A, Rincon AR, Liu L, and Rousseau J The state of knowledge on technologies and their use for fall detection: a scoping review Int J Med Inf 2018 111 58-71

[53]

Lie WN, Le AT, Lin GH (2018) Human fall-down event detection based on 2D skeletons and deep learning approach. In: 2018 International workshop on advanced image technology (IWAIT), pp 1–4

[54]

Liu CL, Lee CH, and Lin PM A fall detection system using k-nearest neighbor classifier Expert Syst Appl 2010 37 10 7174-7181

[55]

Liu J, Xia Y, and Tang Z Privacy-preserving video fall detection using visual shielding information Vis Comput 2021 37 2 359-370

[56]

López-Nava IH, González I, Muñoz-Meléndez A, Bravo J (2015) Comparison of a vision-based system and a wearable inertial-based system for a quantitative analysis and calculation of spatio-temporal parameters. In: Ambient intelligence for health, pp, 116–122

[57]

Lotfi A, Albawendi S, Powell H, Appiah K, and Langensiepen C Supporting independent living for older adults; employing a visual based fall detection through analysing the motion and shape of the human body IEEE Access 2018 6 70272-70282

[58]

Lu G, Zhou Y, Li X, and Kudo M Efficient action recognition via local position offset of 3D skeletal body joints Multimed Tools Appl 2016 75 6 3479-3494

[59]

Luvizon DC, Tabia H, and Picard D Learning features combination for human action recognition from skeleton sequences Pattern Recogn Lett 2017 99 13-20

[60]

Ma X, Wang H, Xue B, Zhou M, Ji B, and Li Y Depth-based human fall detection via shape features and improved extreme learning machine IEEE J Biomed Health Inform 2014 18 6 1915-1922

[61]

Malasinghe LP, Ramzan N, and Dahal K Remote patient monitoring: a comprehensive study Computing 2019 10 1 57-76

[62]

Mellone S, Tacconi C, Schwickert L, Klenk J, Becker C, Chiari L (2012) Smartphone-based solutions for fall detection and prevention: the FARSEEING approach, Zeitschrift für Gerontologie und Geriatrie 45(8):b 722–727

[63]

Merrouche F, Baha N (2016) Depth camera based fall detection using human shape and movement. In: 2016 IEEE international conference on signal and image processing (ICSIP). IEEE, pp 586–590

[64]

Min W, Cui H, Rao H, Li Z, and Yao L Detection of human falls on furniture using scene analysis based on deep learning and activity characteristics IEEE Access 2018 6 9324-9335

[65]

Min W, Zou S, and Li J Human fall detection using normalized shape aspect ratio Multimed Tools Appl 2019 78 11 14331-14353

[66]

Mubashir M, Shao L, and Seed L A survey on fall detection: principles and approaches Neurocomputing 2013 100 144-152

[67]

Nieto-Hidalgo M, Ferrández-Pastor FJ, Valdivieso-Sarabia RJ, Mora-Pascual J, and García-Chamizo JM A vision based proposal for classification of normal and abnormal gait using RGB camera J Biomed Inform 2016 63 82-89

[68]

Nieto-Hidalgo M, García-Chamizo JM (2017) Classification of pathologies using a vision based feature extraction. In: International conference on ubiquitous computing and ambient intelligence. Springer, Cham, pp 265–274

[69]

Nogas J, Khan SS, and Mihailidis A DeepFall: non-invasive fall detection with deep spatio-temporal convolutional autoencoders J Healthc Inf Res 2018 2018 1-21

[70]

Nunez JC, Cabido R, Pantrigo JJ, Montemayor AS, and Velez JF Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition Pattern Recogn 2018 76 80-94

[71]

Ortells J, Herrero-Ezquerro MT, and Mollineda RA Vision-based gait impairment analysis for aided diagnosis Med Biol Eng Comput 2018 56 9 1553-1564

[72]

Panahi L and Ghods V Human fall detection using machine vision techniques on RGB–D images Biomed Signal Process Control 2018 44 146-153

[73]

Parra-Dominguez GS, Snoek J, Taati B, and Mihailidis A Lower body motion analysis to detect falls and near falls on stairs Biomed Eng Lett 2015 5 2 98-108

[74]

Paulo J, Asvadi A, Peixoto P, and Amorim P Human gait pattern changes detection system: a multimodal vision-based and novelty detection learning approach Biocybern Biomed Eng 2017 37 4 701-717

[75]

Poonsri A, Chiracharit W (2017) Fall detection using Gaussian mixture model and principle component analysis. In: 2017 9th International conference on information technology and electrical engineering (ICITEE). IEEE, pp 1–4

[76]

Rastogi S and Singh J A systematic review on machine learning for fall detection system Comput Intell 2021 37 951-974

[77]

Romero A, Gatta C, and Camps-Valls G Unsupervised deep feature extraction for remote sensing image classification IEEE Trans Geosci Remote Sens 2015 54 3 1349-1362

[78]

Rougier C, Meunier J, St-Arnaud A, and Rousseau J 3D head tracking for fall detection using a single calibrated camera Image vis Comput 2013 31 3 246-254

[79]

Sanches SR, Oliveira C, Sementille AC, and Freire V Challenging situations for background subtraction algorithms Appl Intell 2019 49 5 1771-1784

[80]

Schwickert L, Becker C, Lindemann U, Maréchal C, Bourke A, Chiari L, and Klenk J Fall detection with body-worn sensors Z Gerontol Geriatr 2013 46 8 706-719

[81]

Sehairi K, Chouireb F, Meunier J (2018) Elderly fall detection system based on multiple shape features and motion analysis. In: 2018 International conference on intelligent systems and computer vision (ISCV). IEEE, pp 1–8

[82]

Senouci B, Charfi I, Heyrman B, Dubois J, and Miteran J Fast prototyping of a SoC-based smart-camera: a real-time fall detection case study J Real-Time Image Proc 2016 12 4 649-662

[83]

Shi Y, Shi Y, Wang X (2012) Fall detection on mobile phones using features from a five-phase model. In: 2012 9th international conference on ubiquitous intelligence and computing and 9th international conference on autonomic and trusted computing. IEEE, pp 951–956

[84]

Sobral A and Vacavant A A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos Comput vis Image Understading 2014 122 4-21

[85]

Soni PK, Choudhary A (2018) Automated fall detection using computer vision. In: International conference on intelligent human computer interaction, pp 220–229

[86]

Stone EE and Skubic M Fall detection in homes of older adults using the Microsoft Kinect IEEE J Biomed Health Inf 2014 19 1 290-301

[87]

Su X, Tong H, and Ji P Activity recognition with smartphone sensors Tsinghua Sci Technol 2014 19 3 235-249

[88]

Su S, Wu SS, Chen SY, Duh DJ, and Li S Multi-view fall detection based on spatio-temporal interest points Multimed Tools Appl 2016 75 14 8469-8492

[89]

Sun SW, Kuo CH, and Chang PC People tracking in an environment with multiple depth cameras: a skeleton-based pairwise trajectory matching scheme J vis Commun Image Represent 2016 35 36-54

[90]

Theodoridis T, Solachidis V, Vretos N, Daras P (2017) Human fall detection from acceleration measurements using a recurrent neural network. In: International conference on biomedical and health informatics, pp 145–149

[91]

Vishwakarma DK and Kapoor R Hybrid classifier based human activity recognition using the silhouette and cells Expert Syst Appl 2015 42 20 6957-6965

[92]

Wang J, Lu Y, Gu L, Zhou C, and Chai X Moving object recognition under simulated prosthetic vision using background-subtraction-based image processing strategies Inf Sci 2014 277 512-524

[93]

Wang S, Chen L, Zhou Z, Sun X, and Dong J Human fall detection in surveillance video based on PCANet Multimed Tools Appl 2016 75 19 11603-11613

[94]

Wang K, Cao G, Meng D, Chen W, Cao W (2016b) Automatic fall detection of human in video using combination of features. In: 2016 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 1228–1233

[95]

Wang Z, Ren J, Zhang D, Sun M, and Jiang J A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos Neurocomputing 2018 287 68-83

[96]

Wissel T, Stüber P, Wagner B, Bruder R, Erdmann C, Deutz CS, and Ernst F Enhanced optical head tracking for cranial radiation therapy: supporting surface registration by cutaneous structures Int J Radiat Oncol Biol Phys 2016 95 2 810-817

[97]

Yang L, Ren Y, and Zhang W 3D depth image analysis for indoor fall detection of elderly people Digital Commun Netw 2016 2 1 24-34

[98]

Yoo S and Oh D An artificial neural network–based fall detection Int J Eng Busin Manag 2018 20 10 1847979018787905

[99]

Yu M, Yu Y, Rhuma A, Naqvi SMR, Wang L, and Chambers JA An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment IEEE J Biomed Health Inf 2013 17 6 1002-1014

[100]

Yu S, Chen H, and Brown RA Hidden Markov model-based fall detection with motion sensor orientation calibration: a case for real-life home monitoring IEEE J Biomed Health Inform 2017 22 6 1847-1853

[101]

Yu S, Chen H, Wang Q, Shen L, and Huang Y Invariant feature extraction for gait recognition using only one uniform model Neurocomputing 2017 239 81-93

[102]

Yun Y and Gu IYH Human fall detection in videos by fusing statistical features of shape and motion dynamics on riemannian manifolds Neurocomputing 2016 207 726-734

[103]

Yun Y, Gu IYH (2017) Visual information-based activity recognition and fall detection for assisted living and eHealthCare. In: Ambient assisted living and enhanced living environments. Butterworth-Heinemann, pp 395–425

[104]

Zeng Z, Jia J, Zhu Z, and Yu D Adaptive maintenance scheme for codebook-based dynamic background subtraction Comput vis Image Understanding 2016 152 58-66

[105]

Zerrouki N and Houacine A Combined curvelets and hidden Markov models for human fall detection Multimed Tools Appl 2018 77 5 6405-6424

[106]

Zerrouki N, Harrou F, Sun Y, and Houacine A Vision-based human action classification using adaptive boosting algorithm IEEE Sens J 2018 18 12 5115-5121

[107]

Zhang Z, Conly C, Athitsos V (2015) A survey on vision-based fall detection. In: Proceedings of the 8th ACM international conference on PErvasive technologies related to assistive environments, pp 1–7

Cited By

Gaya-Morey FManresa-Yee CBuades-Rubio J(2024)Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic reviewApplied Intelligence10.1007/s10489-024-05645-154:19(8982-9007)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10489-024-05645-1
Mojarad RChibani AAttal FKhodabandelou GAmirat Y(2024)A hybrid and context-aware framework for normal and abnormal human behavior recognitionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09188-428:6(4821-4845)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00500-023-09188-4
Saini MKaur MSengupta EAhmed K(2023)Artificial intelligence inspired framework for preventing sexual violence at public toilets of educational institutions with the improvisation of gender recognition from gait sequencesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08285-827:13(8739-8758)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s00500-023-08285-8

Index Terms

Human fall detection and activity monitoring: a comparative analysis of vision-based methods for classification and detection techniques
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

Recommendations

A Survey of Fall Detection Algorithm for Elderly Health Monitoring
BDCLOUD '15: Proceedings of the 2015 IEEE Fifth International Conference on Big Data and Cloud Computing

With the arrival of population aging society, the health care of the elderly becomes more important. The fall detection algorithm is the core of the fall detection alarm system, so it is the key for the research and development of the fall detection ...
A smartphone-based fall detection system

Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a ...
A multi-modal approach for activity classification and fall detection
Intelligent multisensory systems in support of information society

The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this ...

Comments

Information & Contributors

Information

Published In

cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 26, Issue 8

Apr 2022

455 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 April 2022

Accepted: 22 December 2021

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gaya-Morey FManresa-Yee CBuades-Rubio J(2024)Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic reviewApplied Intelligence10.1007/s10489-024-05645-154:19(8982-9007)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10489-024-05645-1
Mojarad RChibani AAttal FKhodabandelou GAmirat Y(2024)A hybrid and context-aware framework for normal and abnormal human behavior recognitionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09188-428:6(4821-4845)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00500-023-09188-4
Saini MKaur MSengupta EAhmed K(2023)Artificial intelligence inspired framework for preventing sexual violence at public toilets of educational institutions with the improvisation of gender recognition from gait sequencesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08285-827:13(8739-8758)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s00500-023-08285-8

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents