Human Action Recognition Using Laban Movement Analysis and Dynamic Time Warping

Ramezanpanah, Zahra; Mallem, Malik; Davesne, Frédéric

doi:10.1016/j.procs.2020.08.040

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…Moreover, we will employ the proposed technique in healthcare domains in order to resolve the privacy concern. Existing works [42](%) [43] (%) [44] (%) [45] (%) [46] (%) [47] (%) [48] (%) [49]…”

Section: Discussionmentioning

confidence: 99%

An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

Ṣiddiqi

Alsirhani

2022

Mathematical Problems in Engineering

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Human activity recognition (HAR) is the examination of gestures and actions of humans from various resources such as depth or RGB cameras. In this work, we have designed a dynamic and robust feature selection algorithm for a HAR system, through which the system accurately recognizes various kinds of activities. In the proposed approach, we employed mutual information algorithm, which selects the prominent features from the extracted features. The proposed algorithm is the expansion of two methods like max-relevance and min-redundancy, respectively. This method has the capability to gather the assets of various extraction algorithms. But the procedure of selection may be unfair due to the dissimilarity between the classification power and redundancy of the features. To resolve this type of unfair selection, we stabilize both parts through the proposed algorithm that has autonomous upper limit of the mutual information function. Likewise, for the feature extraction and recognition, we used the symlet wavelet transform and hidden Markov model, respectively, for action classification. The proposed algorithm has been justified on depth-based database which has thirteen kinds of activities under comprehensive set of experiments. We showed that the proposed feature selection method achieved best classification accuracy against existing works.

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Section: Discussionmentioning

confidence: 99%

An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

Ṣiddiqi

Alsirhani

2022

Mathematical Problems in Engineering

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“…In time series analysis, dynamic time warping (DTW) is introduced to compare the similarity or distance between two arrays or time series of different lengths. DTW was initially used in speech recognition and is now widely used in posture recognition [38][39][40][41].…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

“…In addition to the above two feature extraction methods (HOG [43][44][45][46], SIFT [47][48][49], DTW [50,41,39,40]) for posture recognition, several feature extraction methods are widely used in posture recognition, such as Hu moment invariant (HMI) [51,52], Fourier descriptors (FD) [53,54], nonparametric weighted feature extraction (NWFE) [55,56], gray-level co-occurrence matrix (GLCM) [57,58].…”

Section: Other Feature Extraction Approachesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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“…The proposed approach first calculates self-similarities between adjacent action sequence frames, chooses a reference sequence that perfectly captures the action's essential shape and motion, applies DTW to compare each reference sequence to the test sequence, and finally uses DTW as an action recognition feature. The study [19]suggested a technique for identifying human activity that combined Dynamic time warping and Laban movement analysis (LMA). In this paper [20], the authors have used Convolutional Neural Networks (CNN) alongside Support Vector Machine (SVM) on the time series data for exercise performance evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

STPT: Spatio-Temporal Polychromatic Trajectory Based Elderly Exercise Evaluation System

2023

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This paper introduces an elderly exercise evaluation system. To determine the quality of a performed exercise, the authors propose a novel system to generate and use Spatio-Temporal Polychromatic Trajectory (STPT) images. Usually, the elder people need to perform some exercises or take physiotherapy in order to stay healthy both physically and mentally. It becomes difficult to evaluate the quality of their exercise routine without the aid of a trained physiotherapist. The system aims to overcome this problem by allowing elders to record their exercise videos using an easy-to-use Graphical User Interface and evaluate the results. A dataset of 109 subjects performing four types of shoulder exercises several times was created. The videos are labelled as correct or incorrect and an STPT image is generated from each video. Using our newly introduced method, the movement of the elder person is projected into an image which can be input to a Convolutional Neural Network (CNN). The dataset is further augmented to increase accuracy. Using our proposed method, the best model achieved an F1 Score over 90% in three of the four exercises. The CNN is trained based on these clips and the models are added to the backend of the interface. The proposed system requires only an ordinary camera and a computer with an entry level GPU allowing it to be deployed at a large scale.

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Human Action Recognition Using Laban Movement Analysis and Dynamic Time Warping

Cited by 11 publications

References 36 publications

An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

A Survey on Artificial Intelligence in Posture Recognition

STPT: Spatio-Temporal Polychromatic Trajectory Based Elderly Exercise Evaluation System

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