Abnormal gait detection with RGB-D devices using joint motion history features

Chaaraoui, Alexandros André; Padilla-López, José Ramón; Flórez-Revuelta, Francisco

doi:10.1109/fg.2015.7284881

Cited by 44 publications

(58 citation statements)

References 14 publications

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“…Second, HMMs with discrete observations are employed in our work instead of continuous ones for easier implementation and interpretation. In [11], skeleton poses were employed to detect abnormal gaits. The key of this research is that a set of consecutive skeletons is represented by a spatio-temporal feature, which is determined based on 3D position of joints together with the motion’s age.…”

Section: Introductionmentioning

confidence: 99%

Skeleton-Based Abnormal Gait Detection

Nguyen

Huynh

Meunier

2016

Sensors

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Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%.

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Section: Introductionmentioning

confidence: 99%

Skeleton-Based Abnormal Gait Detection

Nguyen

Huynh

Meunier

2016

Sensors

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“…Not results, not specific solutions are the most valuable, bur namely the solution method, the approach to it. The created method is presented in [1][2][3][4][5][6][7][8][9][10][11][12][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Resultsmentioning

confidence: 99%

“…like human-human interaction recognition and prediction [20], abnormal event detection for healthcare systems [21]- [23], unusual events detection [24], the behaviour of customers [25], monitoring of people with disabilities [26], [27], aggressive behaviour and anger detection [28], gait recognition [29] and biometric surveillance [30], and analyzing the human motion to aid clinical decision making [31], [32].…”

Section: Resultsmentioning

confidence: 99%

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2017

RENSIT

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“…The method is applied to detect simulated gait anomalies on subjects climbing a stair. A more recent work [2] applies the joint motion history feature (JMH) to normalized skeleton data to simultaneously capture both the human posture and motion within a short temporal window. Such features are used in a bag-of-word paradigm to describe gait sequences with a set of exemplar features.…”

Section: Related Workmentioning

confidence: 99%

Learning shape variations of motion trajectories for gait analysis

Devanne¹,

Wannous²,

Daoudi³

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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The analysis of human gait is more and more investigated due to its large panel of potential applications in various domains, like rehabilitation, deficiency diagnosis, surveillance and movement optimization. In addition, the release of depth sensors offers new opportunities to achieve gait analysis in a non-intrusive context. In this paper, we propose a gait analysis method from depth sequences by analyzing separately each step so as to be robust to gait duration and incomplete cycles. We analyze the shape of the motion trajectory as signature of the gait and consider shape variations within a Riemannian manifold to learn step models. During classification, the derivation of each performed step is evaluated in an online manner to qualitatively analyze the gait. Experiments are carried out in the context of abnormal gait detection and person re-identification trough gait recognition. Results demonstrated the potential of the method in both scenarios.

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Abnormal gait detection with RGB-D devices using joint motion history features

Cited by 44 publications

References 14 publications

Skeleton-Based Abnormal Gait Detection

Skeleton-Based Abnormal Gait Detection

Untitled

Learning shape variations of motion trajectories for gait analysis

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