Applying adversarial auto-encoder for estimating human walking gait abnormality index
This paper proposes an approach that estimates a human walking gait abnormality index using an adversarial auto-encoder (AAE), i.e., a combination of auto-encoder and generative adversarial network (GAN). Since most GAN-based models have been employed as data generators, our work introduces another...
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| Published in | Pattern analysis and applications : PAA Vol. 22; no. 4; pp. 1597 - 1608 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.11.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1433-7541 1433-755X |
| DOI | 10.1007/s10044-019-00790-7 |
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| Abstract | This paper proposes an approach that estimates a human walking gait abnormality index using an adversarial auto-encoder (AAE), i.e., a combination of auto-encoder and generative adversarial network (GAN). Since most GAN-based models have been employed as data generators, our work introduces another perspective of their application. This method directly works on a sequence of 3D point clouds representing the walking postures of a subject. By fitting a cylinder onto each point cloud and feeding cylindrical histograms to an appropriate AAE, our system is able to provide different measures that may be used as gait abnormality indices. The combinations of such quantities are also investigated to obtain improved indicators. The ability of our method is demonstrated by experimenting on a large dataset of nearly 100 thousands point clouds, and the results outperform related approaches that employ different input data types. |
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| AbstractList | This paper proposes an approach that estimates a human walking gait abnormality index using an adversarial auto-encoder (AAE), i.e., a combination of auto-encoder and generative adversarial network (GAN). Since most GAN-based models have been employed as data generators, our work introduces another perspective of their application. This method directly works on a sequence of 3D point clouds representing the walking postures of a subject. By fitting a cylinder onto each point cloud and feeding cylindrical histograms to an appropriate AAE, our system is able to provide different measures that may be used as gait abnormality indices. The combinations of such quantities are also investigated to obtain improved indicators. The ability of our method is demonstrated by experimenting on a large dataset of nearly 100 thousands point clouds, and the results outperform related approaches that employ different input data types. |
| Author | Meunier, Jean Nguyen, Trong-Nguyen |
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| References | Nguyen TN, Huynh HH, Meunier J (2018b) Assessment of gait normality using a depth camera and mirrors. In: 2018 IEEE EMBS international conference on biomedical health informatics (BHI), Las Vegas, NV, USA, pp 37–41. https://doi.org/10.1109/BHI.2018.8333364 Davis JW (2001) Hierarchical motion history images for recognizing human motion. In: IEEE workshop on detection and recognition of events in video. Proceedings. IEEE, pp 39–46 ShottonJGirshickRFitzgibbonASharpTCookMFinocchioMMooreRKohliPCriminisiAKipmanABlakeAEfficient human pose estimation from single depth imagesIEEE Trans Pattern Anal Mach Intell201335122821284010.1109/TPAMI.2012.241 Nguyen TN, Meunier J (2018) Walking gait dataset: point clouds, skeletons and silhouettes. Technical Report 1379, DIRO, University of Montreal. http://www.iro.umontreal.ca/~labimage/GaitDataset/dataset.pdf RenPTangSFangFLuoLXuLBringas-VegaMLYaoDKendrickKMValdes-SosaPAGait rhythm fluctuation analysis for neurodegenerative diseases by empirical mode decompositionIEEE Trans Biomed Eng2017641526010.1109/TBME.2016.2536438 Sakurada M, Yairi T (2014) Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis, ACM, New York, NY, USA, MLSDA’14, pp 4:4–4:11. https://doi.org/10.1145/2689746.2689747 AuvinetEMultonFMeunierJNew lower-limb gait asymmetry indices based on a depth cameraSensors20151534605462310.3390/s150304605 Webber CL Jr, Marwan N (eds) (2015) Recurrence quantification analysis: theory and best practices. Springer, Cham. https://doi.org/10.1007/978-3-319-07155-8 YangYLiuRDengCGaoXMulti-task human action recognition via exploring super-categorySignal Process2016124364410.1016/j.sigpro.2015.10.035 López-FernándezDMadrid-CuevasFCarmona-PoyatoAMunoz-SalinasRMedina-CarnicerRA new approach for multi-view gait recognition on unconstrained pathsJ Vis Commun Image Represent20163839640610.1016/j.jvcir.2016.03.020 MartinelliMTronciEDipoppaGBalducelliCNegoitaMGHowlettRJJainLCElectric power system anomaly detection using neural networksKnowledge-based intelligent information and engineering systems2004BerlinSpringer1242124810.1007/978-3-540-30132-5_168 Bigy AAM, Banitsas K, Badii A, Cosmas J (2015) Recognition of postures and freezing of gait in Parkinson’s disease patients using microsoft Kinect sensor. In: 2015 7th international IEEE/EMBS conference on neural engineering (NER), pp 731–734. https://doi.org/10.1109/NER.2015.7146727 Yu TH, Kim TK, Cipolla R (2010) Real-time action recognition by spatiotemporal semantic and structural forest. In: Proceedings of the British machine vision conference, BMVA Press, pp 52.1–52.12. https://doi.org/10.5244/C.24.52 PrabhuPKarunakarAAnithaHPradhanNClassification of gait signals into different neurodegenerative diseases using statistical analysis and recurrence quantification analysisPattern Recogn Lett2018 YangYDengCTaoDZhangSLiuWGaoXLatent max-margin multitask learning with skelets for 3-d action recognitionIEEE Trans Cybern2017472439448 NguyenTNHuynhHHMeunierJSkeleton-based abnormal gait detectionSensors20161611179210.3390/s16111792 ShottonJFitzgibbonACookMSharpTFinocchioMMooreRKipmanABlakeAReal-time human pose recognition in parts from single depth imagesCVPR2011201112971304 Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27, Curran Associates, Inc., pp 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf RodriguezSPérezKQuinteroCLópezJRojasECalderónJSnášelVAbrahamAKrömerPPantMMudaAKIdentification of multimodal human-robot interaction using combined kernelsInnovations in bio-inspired computing and applications2016ChamSpringer International Publishing26327310.1007/978-3-319-28031-8_23 KimHKimYKoDKimJLeeECParkJJJHPanYKimCSYangYPointing gesture interface for large display environments based on the kinect skeleton modelFuture information technology2014BerlinSpringer50951410.1007/978-3-642-55038-6_80 Makhzani A, Shlens J, Jaitly N, Goodfellow I (2016) Adversarial autoencoders. In: International conference on learning representations. arXiv:1511.05644 YangYDengCGaoSLiuWTaoDGaoXDiscriminative multi-instance multitask learning for 3d action recognitionIEEE Trans Multimed201719351952910.1109/TMM.2016.2626959 Auvinet E, Meunier J, Multon F (2012) Multiple depth cameras calibration and body volume reconstruction for gait analysis. In: 2012 11th international conference on information science, signal processing and their applications (ISSPA), pp 478–483. https://doi.org/10.1109/ISSPA.2012.6310598 HanJBhanuBIndividual recognition using gait energy imageIEEE Trans Pattern Anal Mach Intell200628231632210.1109/TPAMI.2006.38 RothKLucchiANowozinSHofmannTGuyonILuxburgUVBengioSWallachHFergusRVishwanathanSGarnettRStabilizing training of generative adversarial networks through regularizationAdvances in neural information processing systems 302017Red HookCurran Associates Inc20182028 BauckhageCTsotsosJKBunnFEAutomatic detection of abnormal gaitImage Vis Comput200927110811510.1016/j.imavis.2006.10.004 Chaaraoui AA, Padilla-López JR, Flórez-Revuelta F (2015) Abnormal gait detection with RGB-D devices using joint motion history features. In: 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG). vol 7. IEEE, pp 1–6 Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980 JiangSWangYZhangYSunJReal time gait recognition system based on Kinect skeleton feature2015ChamSpringer International Publishing4657 Nguyen TN, Huynh HH, Meunier J (2014) Extracting silhouette-based characteristics for human gait analysis using one camera. In: Proceedings of the 5th symposium on information and communication technology, ACM, New York, NY, USA, SoICT ’14, pp 171–177. https://doi.org/10.1145/2676585.2676612 WilsonACRoelofsRSternMSrebroNRechtBGuyonILuxburgUVBengioSWallachHFergusRVishwanathanSGarnettRThe marginal value of adaptive gradient methods in machine learningAdvances in neural information processing systems 302017Red HookCurran Associates Inc41484158 BeiSZhenZXingZTaochengLQinLMovement disorder detection via adaptively fused gait analysis based on Kinect sensorsIEEE Sens J201818177305731410.1109/JSEN.2018.2839732 NguyenTNHuynhHHMeunierJ3d reconstruction with time-of-flight depth camera and multiple mirrorsIEEE Access20186381063811410.1109/ACCESS.2018.2854262 Nguyen TN, Huynh HH, Meunier J (2018c) Using ToF camera and two mirrors for 3D reconstruction of dynamic objects. Technical Report 1380, DIRO, University of Montreal. http://www.iro.umontreal.ca/~labimage/GaitDataset/reconstruct3D.pdf HuangNEShenZLongSRWuMCShihHHZhengQYenNCTungCCLiuHHThe empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysisProc R Soc Lond A Math Phys Eng Sci19984541971903995163159110.1098/rspa.1998.01930945.62093 790_CR17 790_CR18 790_CR15 790_CR13 Y Yang (790_CR32) 2016; 124 790_CR35 J Shotton (790_CR28) 2011; 2011 J Han (790_CR9) 2006; 28 S Bei (790_CR4) 2018; 18 TN Nguyen (790_CR19) 2016; 16 790_CR21 J Shotton (790_CR29) 2013; 35 D López-Fernández (790_CR14) 2016; 38 NE Huang (790_CR10) 1998; 454 790_CR27 H Kim (790_CR12) 2014 790_CR22 P Ren (790_CR24) 2017; 64 S Rodriguez (790_CR25) 2016 K Roth (790_CR26) 2017 S Jiang (790_CR11) 2015 C Bauckhage (790_CR3) 2009; 27 790_CR8 AC Wilson (790_CR31) 2017 E Auvinet (790_CR2) 2015; 15 M Martinelli (790_CR16) 2004 TN Nguyen (790_CR20) 2018; 6 790_CR1 790_CR30 790_CR7 790_CR6 790_CR5 P Prabhu (790_CR23) 2018 Y Yang (790_CR33) 2017; 19 Y Yang (790_CR34) 2017; 47 |
| References_xml | – reference: Nguyen TN, Meunier J (2018) Walking gait dataset: point clouds, skeletons and silhouettes. Technical Report 1379, DIRO, University of Montreal. http://www.iro.umontreal.ca/~labimage/GaitDataset/dataset.pdf – reference: KimHKimYKoDKimJLeeECParkJJJHPanYKimCSYangYPointing gesture interface for large display environments based on the kinect skeleton modelFuture information technology2014BerlinSpringer50951410.1007/978-3-642-55038-6_80 – reference: JiangSWangYZhangYSunJReal time gait recognition system based on Kinect skeleton feature2015ChamSpringer International Publishing4657 – reference: YangYLiuRDengCGaoXMulti-task human action recognition via exploring super-categorySignal Process2016124364410.1016/j.sigpro.2015.10.035 – reference: BauckhageCTsotsosJKBunnFEAutomatic detection of abnormal gaitImage Vis Comput200927110811510.1016/j.imavis.2006.10.004 – reference: NguyenTNHuynhHHMeunierJ3d reconstruction with time-of-flight depth camera and multiple mirrorsIEEE Access20186381063811410.1109/ACCESS.2018.2854262 – reference: Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980 – reference: ShottonJGirshickRFitzgibbonASharpTCookMFinocchioMMooreRKohliPCriminisiAKipmanABlakeAEfficient human pose estimation from single depth imagesIEEE Trans Pattern Anal Mach Intell201335122821284010.1109/TPAMI.2012.241 – reference: López-FernándezDMadrid-CuevasFCarmona-PoyatoAMunoz-SalinasRMedina-CarnicerRA new approach for multi-view gait recognition on unconstrained pathsJ Vis Commun Image Represent20163839640610.1016/j.jvcir.2016.03.020 – reference: Webber CL Jr, Marwan N (eds) (2015) Recurrence quantification analysis: theory and best practices. Springer, Cham. https://doi.org/10.1007/978-3-319-07155-8 – reference: Sakurada M, Yairi T (2014) Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis, ACM, New York, NY, USA, MLSDA’14, pp 4:4–4:11. https://doi.org/10.1145/2689746.2689747 – reference: Makhzani A, Shlens J, Jaitly N, Goodfellow I (2016) Adversarial autoencoders. In: International conference on learning representations. arXiv:1511.05644 – reference: Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27, Curran Associates, Inc., pp 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf – reference: RodriguezSPérezKQuinteroCLópezJRojasECalderónJSnášelVAbrahamAKrömerPPantMMudaAKIdentification of multimodal human-robot interaction using combined kernelsInnovations in bio-inspired computing and applications2016ChamSpringer International Publishing26327310.1007/978-3-319-28031-8_23 – reference: MartinelliMTronciEDipoppaGBalducelliCNegoitaMGHowlettRJJainLCElectric power system anomaly detection using neural networksKnowledge-based intelligent information and engineering systems2004BerlinSpringer1242124810.1007/978-3-540-30132-5_168 – reference: Auvinet E, Meunier J, Multon F (2012) Multiple depth cameras calibration and body volume reconstruction for gait analysis. In: 2012 11th international conference on information science, signal processing and their applications (ISSPA), pp 478–483. https://doi.org/10.1109/ISSPA.2012.6310598 – reference: RenPTangSFangFLuoLXuLBringas-VegaMLYaoDKendrickKMValdes-SosaPAGait rhythm fluctuation analysis for neurodegenerative diseases by empirical mode decompositionIEEE Trans Biomed Eng2017641526010.1109/TBME.2016.2536438 – reference: Nguyen TN, Huynh HH, Meunier J (2018b) Assessment of gait normality using a depth camera and mirrors. In: 2018 IEEE EMBS international conference on biomedical health informatics (BHI), Las Vegas, NV, USA, pp 37–41. https://doi.org/10.1109/BHI.2018.8333364 – reference: AuvinetEMultonFMeunierJNew lower-limb gait asymmetry indices based on a depth cameraSensors20151534605462310.3390/s150304605 – reference: Chaaraoui AA, Padilla-López JR, Flórez-Revuelta F (2015) Abnormal gait detection with RGB-D devices using joint motion history features. In: 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG). vol 7. IEEE, pp 1–6 – reference: HanJBhanuBIndividual recognition using gait energy imageIEEE Trans Pattern Anal Mach Intell200628231632210.1109/TPAMI.2006.38 – reference: NguyenTNHuynhHHMeunierJSkeleton-based abnormal gait detectionSensors20161611179210.3390/s16111792 – reference: Bigy AAM, Banitsas K, Badii A, Cosmas J (2015) Recognition of postures and freezing of gait in Parkinson’s disease patients using microsoft Kinect sensor. In: 2015 7th international IEEE/EMBS conference on neural engineering (NER), pp 731–734. https://doi.org/10.1109/NER.2015.7146727 – reference: Davis JW (2001) Hierarchical motion history images for recognizing human motion. In: IEEE workshop on detection and recognition of events in video. Proceedings. IEEE, pp 39–46 – reference: HuangNEShenZLongSRWuMCShihHHZhengQYenNCTungCCLiuHHThe empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysisProc R Soc Lond A Math Phys Eng Sci19984541971903995163159110.1098/rspa.1998.01930945.62093 – reference: RothKLucchiANowozinSHofmannTGuyonILuxburgUVBengioSWallachHFergusRVishwanathanSGarnettRStabilizing training of generative adversarial networks through regularizationAdvances in neural information processing systems 302017Red HookCurran Associates Inc20182028 – reference: YangYDengCGaoSLiuWTaoDGaoXDiscriminative multi-instance multitask learning for 3d action recognitionIEEE Trans Multimed201719351952910.1109/TMM.2016.2626959 – reference: BeiSZhenZXingZTaochengLQinLMovement disorder detection via adaptively fused gait analysis based on Kinect sensorsIEEE Sens J201818177305731410.1109/JSEN.2018.2839732 – reference: Yu TH, Kim TK, Cipolla R (2010) Real-time action recognition by spatiotemporal semantic and structural forest. 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| SubjectTerms | Coders Computer Science Cylinders Gait Histograms Industrial and Commercial Application Pattern Recognition Three dimensional models Walking |
| Title | Applying adversarial auto-encoder for estimating human walking gait abnormality index |
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