DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-...
Gespeichert in:
| Veröffentlicht in: | Journal of healthcare informatics research Jg. 4; H. 1; S. 50 - 70 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer International Publishing
01.03.2020
|
| Schlagworte: | |
| ISSN: | 2509-4971, 2509-498X |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. In this paper, we present a novel framework,
DeepFall
, which formulates the fall detection problem as an anomaly detection problem. The
DeepFall
framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the
DeepFall
framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras, and show superior results in comparison with traditional autoencoder methods to identify unseen falls. |
|---|---|
| AbstractList | Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. In this paper, we present a novel framework,
DeepFall
, which formulates the fall detection problem as an anomaly detection problem. The
DeepFall
framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the
DeepFall
framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras, and show superior results in comparison with traditional autoencoder methods to identify unseen falls. Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. In this paper, we present a novel framework, , which formulates the fall detection problem as an anomaly detection problem. The framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras, and show superior results in comparison with traditional autoencoder methods to identify unseen falls. Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. In this paper, we present a novel framework, DeepFall, which formulates the fall detection problem as an anomaly detection problem. The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the DeepFall framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras, and show superior results in comparison with traditional autoencoder methods to identify unseen falls. Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. In this paper, we present a novel framework, DeepFall, which formulates the fall detection problem as an anomaly detection problem. The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the DeepFall framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras, and show superior results in comparison with traditional autoencoder methods to identify unseen falls.Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. In this paper, we present a novel framework, DeepFall, which formulates the fall detection problem as an anomaly detection problem. The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the DeepFall framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras, and show superior results in comparison with traditional autoencoder methods to identify unseen falls. |
| Author | Khan, Shehroz S. Nogas, Jacob Mihailidis, Alex |
| Author_xml | – sequence: 1 givenname: Jacob orcidid: 0000-0002-6120-431X surname: Nogas fullname: Nogas, Jacob email: jacob.nogas@mail.utoronto.ca organization: University of Toronto – sequence: 2 givenname: Shehroz S. surname: Khan fullname: Khan, Shehroz S. organization: University of Toronto – sequence: 3 givenname: Alex surname: Mihailidis fullname: Mihailidis, Alex organization: Toronto Rehabilitation Institute, University Health Network |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35415435$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kUFP3DAQha0KVCjlD_RQ5cjFxXacxO6hElqgRUJwgEpVL5bjHYNR1g52soh_j8MuK9rDnmyPv_dmNO8T2vHBA0JfKPlGCWmOE6d1XWNCJSaE1BTzD2ifVURiLsWfnc29oXvoMKWHDFHBKyaaj2ivrDiteFnto7-nAP257rrvxVXw-MIvdXJLKKZScQoDmMEFXzy54b6Y0OKm17mCb2HRh6i7Yhb8MnTjROXXyTgE8CbMIabPaNfqLsHh-jxAv8_Pbme_8OX1z4vZySU2nNcDFo0wNdOiNdY0LZub0lTU1lxYIFyX2rbUVq0lVDNqhQEJuTqnFFijTRaVB-jHyrcf2wXMDfghD6b66BY6Pqugnfr3x7t7dReWSkjBGimzwdHaIIbHEdKgFi4Z6DrtIYxJsZpLKSsqy4x-fd9r0-RtoRlgK8DEkFIEu0EoUVNwahWcysGp1-AUzyLxn8i4YVrzNK_rtkvLlTTlPv4OonoIY8xRpG2qF_Xvr3c |
| CitedBy_id | crossref_primary_10_1007_s11042_023_16476_6 crossref_primary_10_1371_journal_pone_0325253 crossref_primary_10_1049_ipr2_12532 crossref_primary_10_1109_ACCESS_2025_3539449 crossref_primary_10_1109_ACCESS_2021_3083064 crossref_primary_10_1109_ACCESS_2023_3307138 crossref_primary_10_3390_s25113313 crossref_primary_10_1016_j_engappai_2025_111819 crossref_primary_10_3390_app13126916 crossref_primary_10_3390_diagnostics12123060 crossref_primary_10_1111_exsy_12564 crossref_primary_10_1186_s12984_021_00918_z crossref_primary_10_3390_s22114194 crossref_primary_10_1016_j_compbiomed_2022_105626 crossref_primary_10_22630_MGV_2025_34_1_3 crossref_primary_10_1016_j_knosys_2025_113038 crossref_primary_10_1007_s10586_022_03818_6 crossref_primary_10_1007_s42979_023_02480_y crossref_primary_10_1109_ACCESS_2020_3022366 crossref_primary_10_1109_TETCI_2024_3358103 crossref_primary_10_1007_s12559_020_09749_x crossref_primary_10_1109_ACCESS_2024_3360691 crossref_primary_10_1111_jonm_13787 crossref_primary_10_3390_s25134128 crossref_primary_10_1016_j_jestch_2025_102185 crossref_primary_10_1007_s41666_022_00121_2 crossref_primary_10_1109_JSYST_2022_3140546 crossref_primary_10_1109_TCSVT_2024_3417810 crossref_primary_10_1109_ACCESS_2022_3143990 crossref_primary_10_1109_ACCESS_2022_3211939 crossref_primary_10_1007_s11042_022_12018_8 crossref_primary_10_1109_ACCESS_2023_3321192 crossref_primary_10_1016_j_eswa_2021_116076 crossref_primary_10_1109_TASE_2020_3042158 crossref_primary_10_1038_s41598_025_11325_y crossref_primary_10_1109_JSEN_2024_3446673 crossref_primary_10_1007_s11760_022_02174_7 crossref_primary_10_1016_j_knosys_2023_110992 crossref_primary_10_1007_s00603_025_04620_7 crossref_primary_10_1109_JSEN_2020_3032728 crossref_primary_10_1007_s10489_024_05645_1 crossref_primary_10_1016_j_procs_2024_08_028 crossref_primary_10_3390_technologies10020047 crossref_primary_10_1007_s10044_020_00901_9 crossref_primary_10_1016_j_jestch_2022_101227 crossref_primary_10_1109_TCSVT_2023_3303258 crossref_primary_10_1186_s12938_023_01065_3 crossref_primary_10_3390_jimaging7070109 |
| Cites_doi | 10.1016/j.patrec.2017.07.016 10.1007/978-3-319-20227-3_19 10.1109/CVPR.2001.990497 10.1016/j.cmpb.2014.09.005 10.1109/JBHI.2014.2304357 10.1109/TPAMI.2012.59 10.1109/ICCV.2015.510 10.1007/978-3-642-33715-4_54 10.1109/WACV.2017.118 10.1109/ICOSP.2014.7015185 10.1016/j.eswa.2017.06.011 10.21528/CBIC2017-49 10.1016/j.cviu.2016.10.010 10.1109/TIP.2017.2670780 10.1109/TKDE.2018.2806975 10.1371/journal.pone.0152173 10.1016/j.ijmedinf.2016.07.004 10.1016/j.asoc.2017.01.034 10.1007/978-3-319-71249-9_3 10.7326/0003-4819-113-4-308 10.1109/CVPR.2016.86 10.1007/978-3-642-21735-7_7 10.1017/S026988891300043X 10.5244/C.31.139 10.29007/xt7r 10.1145/1541880.1541882 10.1109/EMBC.2016.7590763 10.1145/3123266.3123451 10.5244/C.26.124 |
| ContentType | Journal Article |
| Copyright | Springer Nature Switzerland AG 2019 Springer Nature Switzerland AG 2019. |
| Copyright_xml | – notice: Springer Nature Switzerland AG 2019 – notice: Springer Nature Switzerland AG 2019. |
| DBID | AAYXX CITATION NPM 7X8 5PM |
| DOI | 10.1007/s41666-019-00061-4 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Computer Science |
| EISSN | 2509-498X |
| EndPage | 70 |
| ExternalDocumentID | PMC8982799 35415435 10_1007_s41666_019_00061_4 |
| Genre | Journal Article |
| GroupedDBID | -EM 0R~ 406 AACDK AAHNG AAIAL AAJBT AANZL AARHV AASML AATNV AATVU AAUYE AAYUE ABAKF ABDZT ABECU ABFTV ABJNI ABJOX ABKCH ABMQK ABQBU ABTEG ABTKH ABTMW ABXPI ACAOD ACDTI ACGFS ACHSB ACMLO ACOKC ACPIV ACZOJ ADHHG ADKNI ADKPE ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEJRE AEMSY AEOHA AESKC AEVLU AEXYK AFBBN AFQWF AGDGC AGMZJ AGQEE AGRTI AHKAY AHSBF AIAKS AIGIU AILAN AITGF AJRNO AJZVZ ALFXC ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AMYQR AXYYD BGNMA CSCUP DNIVK DPUIP EBLON EBS EIOEI EJD FERAY FIGPU FINBP FNLPD FSGXE GGCAI H13 HG6 IKXTQ IWAJR J-C JZLTJ KOV LLZTM M4Y NPVJJ NQJWS NU0 O9J PT4 RLLFE ROL RPM RSV SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE TSG UOJIU UTJUX UZXMN VFIZW Z83 ZMTXR AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c446t-878c62a8bcfc7b2dc3c51f648fe04a3afb1f5bf01a21f8ce9e4a3d11e27acbcf3 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 61 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000705500800003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2509-4971 |
| IngestDate | Tue Sep 30 17:00:03 EDT 2025 Fri Jul 11 11:34:17 EDT 2025 Thu Jan 02 22:54:57 EST 2025 Sat Nov 29 02:53:39 EST 2025 Tue Nov 18 22:17:49 EST 2025 Fri Feb 21 02:35:26 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Spatio-temporal Convolutional autoencoders Fall detection Anomaly detection |
| Language | English |
| License | Springer Nature Switzerland AG 2019. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c446t-878c62a8bcfc7b2dc3c51f648fe04a3afb1f5bf01a21f8ce9e4a3d11e27acbcf3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-6120-431X |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/8982799 |
| PMID | 35415435 |
| PQID | 2649995193 |
| PQPubID | 23479 |
| PageCount | 21 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8982799 proquest_miscellaneous_2649995193 pubmed_primary_35415435 crossref_primary_10_1007_s41666_019_00061_4 crossref_citationtrail_10_1007_s41666_019_00061_4 springer_journals_10_1007_s41666_019_00061_4 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-03-01 |
| PublicationDateYYYYMMDD | 2020-03-01 |
| PublicationDate_xml | – month: 03 year: 2020 text: 2020-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Cham |
| PublicationPlace_xml | – name: Cham – name: Switzerland |
| PublicationTitle | Journal of healthcare informatics research |
| PublicationTitleAbbrev | J Healthc Inform Res |
| PublicationTitleAlternate | J Healthc Inform Res |
| PublicationYear | 2020 |
| Publisher | Springer International Publishing |
| Publisher_xml | – name: Springer International Publishing |
| References | Munawar A, Vinayavekhin P, De Magistris G (2017) Spatio-temporal anomaly detection for industrial robots through prediction in unsupervised feature space. In: 2017 IEEE Winter conference on applications of computer vision (WACV). IEEE, pp 1017–1025 Nogas J, Khan SS, Mihailidis A (2018) Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd workshop on aging, rehabilitation and independent assisted living. IJCAI Workshop Tran HT, Hogg D (2017) Anomaly detection using a convolutional winner-take-all autoencoder. In: Proceedings of the British machine vision conference 2017. Leeds YusifSSoarJHafeez-BaigAOlder people, assistive technologies, and the barriers to adoption: a systematic reviewInt J Med Inform20169411211610.1016/j.ijmedinf.2016.07.004 KhanSSMaddenMGOne-class classification: taxonomy of study and review of techniquesKnowl Eng Rev201429334537410.1017/S026988891300043X Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 733–742 Gutoski M, Aquino NMR, Ribeiro M, Lazzaretti AE, Lopes HS (2017) Detection of video anomalies using convolutional autoencoders and one-class support vector machines. In: Proc. XIII Brazilian congress on computational intelligence Viacheslav V, Alexander F, Vladimir M, Svetlana T, Oksana L (2014) Kinect depth map restoration using modified exemplar-based inpainting. In: 2014 12th International conference on signal processing (ICSP). IEEE, pp 1175–1179 PenttiläJA method for anomaly detection in hyperspectral images, using deep convolutional autoencoders. Master’s thesis2017FinlandUniversity of Jyväskylä Masci J, Meier U, Cireşan D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: International conference on artificial neural networks. Springer, pp 52–59 SabokrouMFayyazMFathyMKletteRDeep-cascade: cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenesIEEE Trans Image Process201726419922004363624610.1109/TIP.2017.2670780 Baccouche M, Mamalet F, Wolf C, Garcia C, Baskurt A (2012) Spatio-temporal convolutional sparse autoencoder for sequence classification. In: BMVC. Citeseer Skubic M, Harris BH, Stone E, Ho K, Su BY, Rantz M (2016) Testing non-wearable fall detection methods in the homes of older adults. In: 2016 IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC). IEEE, pp 557–560 Nathan Silberman Derek Hoiem PK, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: ECCV CDC (2017) Enter of disease control and prevention – important facts about falls. https://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html. [Online accessed 22-December-2017] RubensteinLZRobbinsASJosephsonKRSchulmanBLOsterweilDThe value of assessing falls in an elderly population: a randomized clinical trialAnn Intern Med1990113430831610.7326/0003-4819-113-4-308 Bertalmio M, Bertozzi AL, Sapiro G (2001) Navier-stokes, fluid dynamics, and image and video inpainting. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, vol 1, pp I–355–I–362, DOI https://doi.org/10.1109/CVPR.2001.990497 Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680 JiSXuWYangMYuK3d convolutional neural networks for human action recognitionIEEE Trans Pattern Anal Mach Intell201335122123110.1109/TPAMI.2012.59 Zhao Y, Deng B, Shen C, Liu Y, Lu H, Hua XS (2017) Spatio-temporal autoencoder for video anomaly detection. In: Proceedings of the 2017 ACM on multimedia conference, MM ’17. ACM, New York Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: International symposium on neural networks. Springer, pp 189–196 Itseez (2015) Open source computer vision library. https://github.com/itseez/opencv KhanSSKargMEKulićDHoeyJDetecting falls with x-factor hidden Markov modelsAppl Soft Comput20175516817710.1016/j.asoc.2017.01.034 Khan SS, Taati B (2017) Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders. Expert Systems with Applications Vadivelu S, Ganesan S, Murthy OR, Dhall A (2016) Thermal imaging based elderly fall detection. In: ACCV workshop. Springer, pp 541–553 XuDYanYRicciESebeNDetecting anomalous events in videos by learning deep representations of appearance and motionComput Vis Image Underst201715611712710.1016/j.cviu.2016.10.010 Du Tran Lubomir Bourdev RFLTMP (2015) Learning spatiotemporal features with 3d convolutional networks. In: IEEE International conference on computer vision (ICCV). IEEE, DOI https://doi.org/10.1109/ICCV.2015.510 Chollet F., et al. (2015) Keras: the python deep learning library https://github.com/fchollet/keras. Online accessed 20-January-2018 Mercuri M, Garripoli C, Karsmakers P, Soh PJ, Vandenbosch GA, Pace C, Leroux P, Schreurs D (2016) Healthcare system for non-invasive fall detection in indoor environment. In: Applications in electronics pervading industry, environment and society. Springer, pp 145–152 Samek W, Wiegand T, Müller KR (2017) Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv:https://arxiv.org/abs/1708.08296 Bogdan KwolekMKHuman fall detection on embedded platform using depth maps and wireless accelerometerComput Methods Programs Biomed201411748950110.1016/j.cmpb.2014.09.005 MaXWangHXueBZhouMJiBLiYDepth-based human fall detection via shape features and improved extreme learning machineIEEE J Biomed Health Inform20141861915192210.1109/JBHI.2014.2304357 GoldsteinMUchidaSA comparative evaluation of unsupervised anomaly detection algorithms for multivariate dataPloS one 1120164e015217310.1371/journal.pone.0152173 Chalapathy R, Menon AK, Chawla S (2017) Robust, deep and inductive anomaly detection. In: European conference on machine learning. Skopje ChandolaVBanerjeeAKumarVAnomaly detection: a surveyACM Comput Surv200941315:115:5810.1145/1541880.1541882 Ribeiro M, Lazzaretti AE, Lopes HS (2017) A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognition Letters KhanSSAhmadARelationship between variants of one-class nearest neighbors and creating their accurate ensemblesIEEE Trans Knowl Data Eng20183091796180910.1109/TKDE.2018.2806975 M Goldstein (61_CR10) 2016; 4 SS Khan (61_CR17) 2017; 55 LZ Rubenstein (61_CR28) 1990; 113 V Chandola (61_CR6) 2009; 41 X Ma (61_CR20) 2014; 18 MK Bogdan Kwolek (61_CR3) 2014; 117 61_CR19 S Yusif (61_CR36) 2016; 94 61_CR30 61_CR31 61_CR1 61_CR32 61_CR2 61_CR11 61_CR33 61_CR12 61_CR34 61_CR13 61_CR14 61_CR37 61_CR7 61_CR8 61_CR9 J Penttilä (61_CR26) 2017 D Xu (61_CR35) 2017; 156 61_CR4 S Ji (61_CR15) 2013; 35 61_CR5 SS Khan (61_CR18) 2014; 29 61_CR27 SS Khan (61_CR16) 2018; 30 M Sabokrou (61_CR29) 2017; 26 61_CR21 61_CR22 61_CR23 61_CR24 61_CR25 |
| References_xml | – reference: CDC (2017) Enter of disease control and prevention – important facts about falls. https://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html. [Online accessed 22-December-2017] – reference: Gutoski M, Aquino NMR, Ribeiro M, Lazzaretti AE, Lopes HS (2017) Detection of video anomalies using convolutional autoencoders and one-class support vector machines. In: Proc. XIII Brazilian congress on computational intelligence – reference: GoldsteinMUchidaSA comparative evaluation of unsupervised anomaly detection algorithms for multivariate dataPloS one 1120164e015217310.1371/journal.pone.0152173 – reference: Chalapathy R, Menon AK, Chawla S (2017) Robust, deep and inductive anomaly detection. In: European conference on machine learning. Skopje – reference: Munawar A, Vinayavekhin P, De Magistris G (2017) Spatio-temporal anomaly detection for industrial robots through prediction in unsupervised feature space. In: 2017 IEEE Winter conference on applications of computer vision (WACV). IEEE, pp 1017–1025 – reference: Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680 – reference: KhanSSKargMEKulićDHoeyJDetecting falls with x-factor hidden Markov modelsAppl Soft Comput20175516817710.1016/j.asoc.2017.01.034 – reference: SabokrouMFayyazMFathyMKletteRDeep-cascade: cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenesIEEE Trans Image Process201726419922004363624610.1109/TIP.2017.2670780 – reference: Bertalmio M, Bertozzi AL, Sapiro G (2001) Navier-stokes, fluid dynamics, and image and video inpainting. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, vol 1, pp I–355–I–362, DOI https://doi.org/10.1109/CVPR.2001.990497 – reference: Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 733–742 – reference: XuDYanYRicciESebeNDetecting anomalous events in videos by learning deep representations of appearance and motionComput Vis Image Underst201715611712710.1016/j.cviu.2016.10.010 – reference: RubensteinLZRobbinsASJosephsonKRSchulmanBLOsterweilDThe value of assessing falls in an elderly population: a randomized clinical trialAnn Intern Med1990113430831610.7326/0003-4819-113-4-308 – reference: JiSXuWYangMYuK3d convolutional neural networks for human action recognitionIEEE Trans Pattern Anal Mach Intell201335122123110.1109/TPAMI.2012.59 – reference: KhanSSAhmadARelationship between variants of one-class nearest neighbors and creating their accurate ensemblesIEEE Trans Knowl Data Eng20183091796180910.1109/TKDE.2018.2806975 – reference: Baccouche M, Mamalet F, Wolf C, Garcia C, Baskurt A (2012) Spatio-temporal convolutional sparse autoencoder for sequence classification. In: BMVC. Citeseer – reference: Vadivelu S, Ganesan S, Murthy OR, Dhall A (2016) Thermal imaging based elderly fall detection. In: ACCV workshop. Springer, pp 541–553 – reference: Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: International symposium on neural networks. Springer, pp 189–196 – reference: MaXWangHXueBZhouMJiBLiYDepth-based human fall detection via shape features and improved extreme learning machineIEEE J Biomed Health Inform20141861915192210.1109/JBHI.2014.2304357 – reference: Masci J, Meier U, Cireşan D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: International conference on artificial neural networks. Springer, pp 52–59 – reference: Nathan Silberman Derek Hoiem PK, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: ECCV – reference: Mercuri M, Garripoli C, Karsmakers P, Soh PJ, Vandenbosch GA, Pace C, Leroux P, Schreurs D (2016) Healthcare system for non-invasive fall detection in indoor environment. In: Applications in electronics pervading industry, environment and society. Springer, pp 145–152 – reference: Itseez (2015) Open source computer vision library. https://github.com/itseez/opencv – reference: Samek W, Wiegand T, Müller KR (2017) Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv:https://arxiv.org/abs/1708.08296 – reference: KhanSSMaddenMGOne-class classification: taxonomy of study and review of techniquesKnowl Eng Rev201429334537410.1017/S026988891300043X – reference: Ribeiro M, Lazzaretti AE, Lopes HS (2017) A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognition Letters – reference: YusifSSoarJHafeez-BaigAOlder people, assistive technologies, and the barriers to adoption: a systematic reviewInt J Med Inform20169411211610.1016/j.ijmedinf.2016.07.004 – reference: Skubic M, Harris BH, Stone E, Ho K, Su BY, Rantz M (2016) Testing non-wearable fall detection methods in the homes of older adults. In: 2016 IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC). IEEE, pp 557–560 – reference: Bogdan KwolekMKHuman fall detection on embedded platform using depth maps and wireless accelerometerComput Methods Programs Biomed201411748950110.1016/j.cmpb.2014.09.005 – reference: Chollet F., et al. (2015) Keras: the python deep learning library https://github.com/fchollet/keras. Online accessed 20-January-2018 – reference: Nogas J, Khan SS, Mihailidis A (2018) Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd workshop on aging, rehabilitation and independent assisted living. IJCAI Workshop – reference: Tran HT, Hogg D (2017) Anomaly detection using a convolutional winner-take-all autoencoder. In: Proceedings of the British machine vision conference 2017. Leeds – reference: Du Tran Lubomir Bourdev RFLTMP (2015) Learning spatiotemporal features with 3d convolutional networks. In: IEEE International conference on computer vision (ICCV). IEEE, DOI https://doi.org/10.1109/ICCV.2015.510 – reference: Viacheslav V, Alexander F, Vladimir M, Svetlana T, Oksana L (2014) Kinect depth map restoration using modified exemplar-based inpainting. In: 2014 12th International conference on signal processing (ICSP). IEEE, pp 1175–1179 – reference: PenttiläJA method for anomaly detection in hyperspectral images, using deep convolutional autoencoders. Master’s thesis2017FinlandUniversity of Jyväskylä – reference: ChandolaVBanerjeeAKumarVAnomaly detection: a surveyACM Comput Surv200941315:115:5810.1145/1541880.1541882 – reference: Zhao Y, Deng B, Shen C, Liu Y, Lu H, Hua XS (2017) Spatio-temporal autoencoder for video anomaly detection. In: Proceedings of the 2017 ACM on multimedia conference, MM ’17. ACM, New York – reference: Khan SS, Taati B (2017) Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders. Expert Systems with Applications – ident: 61_CR27 doi: 10.1016/j.patrec.2017.07.016 – ident: 61_CR22 doi: 10.1007/978-3-319-20227-3_19 – ident: 61_CR30 – ident: 61_CR2 doi: 10.1109/CVPR.2001.990497 – ident: 61_CR11 – volume: 117 start-page: 489 year: 2014 ident: 61_CR3 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2014.09.005 – volume: 18 start-page: 1915 issue: 6 year: 2014 ident: 61_CR20 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2014.2304357 – volume: 35 start-page: 221 issue: 1 year: 2013 ident: 61_CR15 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2012.59 – ident: 61_CR9 doi: 10.1109/ICCV.2015.510 – ident: 61_CR8 – ident: 61_CR24 doi: 10.1007/978-3-642-33715-4_54 – ident: 61_CR4 – ident: 61_CR23 doi: 10.1109/WACV.2017.118 – ident: 61_CR34 doi: 10.1109/ICOSP.2014.7015185 – ident: 61_CR19 doi: 10.1016/j.eswa.2017.06.011 – ident: 61_CR12 doi: 10.21528/CBIC2017-49 – volume: 156 start-page: 117 year: 2017 ident: 61_CR35 publication-title: Comput Vis Image Underst doi: 10.1016/j.cviu.2016.10.010 – volume: 26 start-page: 1992 issue: 4 year: 2017 ident: 61_CR29 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2017.2670780 – volume: 30 start-page: 1796 issue: 9 year: 2018 ident: 61_CR16 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2018.2806975 – volume: 4 start-page: e0152173 year: 2016 ident: 61_CR10 publication-title: PloS one 11 doi: 10.1371/journal.pone.0152173 – volume: 94 start-page: 112 year: 2016 ident: 61_CR36 publication-title: Int J Med Inform doi: 10.1016/j.ijmedinf.2016.07.004 – ident: 61_CR14 – volume: 55 start-page: 168 year: 2017 ident: 61_CR17 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.01.034 – ident: 61_CR5 doi: 10.1007/978-3-319-71249-9_3 – volume: 113 start-page: 308 issue: 4 year: 1990 ident: 61_CR28 publication-title: Ann Intern Med doi: 10.7326/0003-4819-113-4-308 – ident: 61_CR33 – ident: 61_CR13 doi: 10.1109/CVPR.2016.86 – ident: 61_CR21 doi: 10.1007/978-3-642-21735-7_7 – volume: 29 start-page: 345 issue: 3 year: 2014 ident: 61_CR18 publication-title: Knowl Eng Rev doi: 10.1017/S026988891300043X – ident: 61_CR7 – ident: 61_CR32 doi: 10.5244/C.31.139 – ident: 61_CR25 doi: 10.29007/xt7r – volume: 41 start-page: 15:1 issue: 3 year: 2009 ident: 61_CR6 publication-title: ACM Comput Surv doi: 10.1145/1541880.1541882 – ident: 61_CR31 doi: 10.1109/EMBC.2016.7590763 – ident: 61_CR37 doi: 10.1145/3123266.3123451 – ident: 61_CR1 doi: 10.5244/C.26.124 – volume-title: A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders. Master’s thesis year: 2017 ident: 61_CR26 |
| SSID | ssj0001845287 |
| Score | 2.4205885 |
| Snippet | Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to... |
| SourceID | pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 50 |
| SubjectTerms | Biomedical Engineering and Bioengineering Computational Biology/Bioinformatics Computational Intelligence Computer Science Data Mining and Knowledge Discovery Health Informatics Research Article |
| Title | DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders |
| URI | https://link.springer.com/article/10.1007/s41666-019-00061-4 https://www.ncbi.nlm.nih.gov/pubmed/35415435 https://www.proquest.com/docview/2649995193 https://pubmed.ncbi.nlm.nih.gov/PMC8982799 |
| Volume | 4 |
| WOSCitedRecordID | wos000705500800003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 2509-498X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001845287 issn: 2509-4971 databaseCode: RSV dateStart: 20170601 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwED-xgRAvDMZXYExG4g0s1YmzOLxN2yp4WDWxgSpeorNra5MqZ1rT_v2cXSeoG5oEL3lwzo7jO_t-9p3vAD6iqFBiYTgS9uCyLiTHQiBXcma0xRpLrWOyiWoyUdNpfZYuhS16b_feJBlX6uGymwwWLtr61jwqXi634CGpOxUSNnw___nnZEXJMo-Z8Ui9xxRqIt2W-XszmxrpDsy86y15y2QaNdF45__-4Rk8TciTHa5F5Tk8sH4XdvqsDixN8l14fJrM7S_g17G112Ocz7-wSev5N7_C4O3OQhE7tl104_IsnOWyQMrOo382v1jHu5qzo9avkmyHTy-7NsTNDL7TL-HH-OTi6CtPyRi4oR1jR6umMgc5Km2cqXQ-M4UphTuQytkRMRudFq7UbiQwF04ZW1sqnQlh8woNVSpewbZvvX0DTJPWdMYQMpVOEgLEQqqRcgWODG2ZtctA9AxpTIpUHhJmzJshxnIcx4bGsYnj2MgMPg11rtdxOu6l_tDzuaHpFGwk6G27XDSEDwkyB1ibwes134f2ipLQDsHLDKoNiRgIQqjuzTf-6jKG7Fa1yqu6zuBzLxdNWisW93Tz7b-Rv4MneTgNiB5ye7Dd3Szte3hkVt3V4mYftqqpoufk7HQ_TpjfNDIPBw |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fTxQxEJ4oGvVFBFHXX5TEN2xy3e2yXd8MeIEIFyKHIbw0ba-NJJcu4fbu73fa6y45MST62m27u51p52tn-g3AJ8UqxVVhqELsQXldcKoKpqjgE6OtqlWpdUw2UY1G4uKiPk2XwmZdtHvnkowrdX_ZjQcPF259axoNL-UP4RFHixUY83-c_bw9WRG8zGNmPDTvMYUaS7dl_t7NqkW6AzPvRkv-4TKNlmi4_n__8AKeJ-RJvi5VZQMeWL8J611WB5Im-SY8OUnu9pdweWDt9VBNp1_IqPH0yC9UiHYnoYgc2DaGcXkSznJJqErOYnw2HS_5rqZkv_GLpNvh1fO2CbyZIXZ6C86H38b7hzQlY6AGd4wtrprC7OVKaONMpfOJKUzJ3B4Xzg5Q2Mpp5krtBkzlzAlja4ulE8ZsXimDjYpXsOYbb98A0Wg1nTGITLnjiABVwcVAuEINDG6ZtcuAdQKRJjGVh4QZU9lzLMdxlDiOMo6j5Bns9m2ulzwd99be6eQscToFH4nytpnPJOJDhMwB1mbwein3vr-iRLSD8DKDakUj-gqBqnv1ib_6FSm7RS3yqq4z-NzphUxrxeyez3z7b9W34enh-ORYHh-Nvr-DZ3k4GYjRcu9hrb2Z2w_w2Czaq9nNxzhhfgMwrQ_3 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwED-NDU28bDAYhE8j8Tas1omzOLyhdRUTUE3amCZeLNu1xaTKqda0f__OzgeUTZMQr87ZSeyz73e-L4APihWKq8xQhdiD8jLjVGVMUcGnRltVqlzrWGyimEzE5WV5-kcUf_R270ySTUxDyNLk68F86gZ94BsP1i5Ug0sahTDlD2CLB0f6oK-fXfy-ZRE8T2OVPBT1sZwaayNn7h5mXTrdgpy3PSf_Mp9GqTTe_f__eQw7LSIlnxsWegIb1u_BblftgbSbfw-2v7dm-Kfwc2TtfKxms09kUnl64lcqeMGT0ERGto7uXZ6EO14SSMlZ9Num500erBk5qvyq5fnw6mVdhXyawaf6GfwYH58ffaFtkQZqUJOs8TQV5jBVQhtnCp1OTWZy5g65cHaITKCcZi7XbshUypwwtrTYOmXMpoUy2Cnbh01fefsCiEZp6oxBxModR2SoMi6GwmVqaFCV1i4B1i2ONG0G81BIYyb73MtxHiXOo4zzKHkCB32feZO_417q992aS9xmwXaivK2WC4m4EaF0gLsJPG94oB8vyxEFIexMoFjjjp4gpPBef-KvfsVU3qIUaVGWCXzseES2Z8jins98-W_k72D7dDSW304mX1_BozRcGEQnutewWV8v7Rt4aFb11eL6bdw7N1p7GNs |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=DeepFall%3A+Non-Invasive+Fall+Detection+with+Deep+Spatio-Temporal+Convolutional+Autoencoders&rft.jtitle=Journal+of+healthcare+informatics+research&rft.au=Nogas%2C+Jacob&rft.au=Khan%2C+Shehroz+S.&rft.au=Mihailidis%2C+Alex&rft.date=2020-03-01&rft.pub=Springer+International+Publishing&rft.issn=2509-4971&rft.eissn=2509-498X&rft.volume=4&rft.issue=1&rft.spage=50&rft.epage=70&rft_id=info:doi/10.1007%2Fs41666-019-00061-4&rft_id=info%3Apmid%2F35415435&rft.externalDocID=PMC8982799 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2509-4971&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2509-4971&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2509-4971&client=summon |