Laban movement analysis and hidden Markov models for dynamic 3D gesture recognition

Truong, Arthur; Zaharia, Titus

doi:10.1186/s13640-017-0202-5

Cited by 12 publications

(8 citation statements)

References 32 publications

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“…Moreover, gesture recognition from motion data is typically done using machine learning algorithm taking either 3D trajectories and their geometrical properties such as speed and acceleration as input. As 3D trajectories are temporal sequences, many studies for gesture recognition have been performed using Hidden Markov Model [31]. Another class of methods performing pattern matching in a auxiliary space also tackle this problem.…”

Section: Gesture Recognitionmentioning

confidence: 99%

Recognition of Laban Effort Qualities from Hand Motion

Garcia

Ronfard

2020

Proceedings of the 7th International Conference on Movement and Computing

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In this paper, we conduct a study for recognizing motion qualities in hand gestures using virtual reality trackers attached to the hand. From this 6D signal, we extract Euclidean, equi-affine and moving frame features and compare their effectiveness in the task of recognizing Laban Effort qualities. Our experimental results reveal that equiaffine features are highly discriminant features for this task. We also compare two classification methods on this task. In the first method, we trained separate HMM models for the 6 Laban Effort qualities (light, strong, sudden, sustained, direct, indirect). In the second method, we trained separate HMM models for the 8 Laban motion verbs (dab, glide, float, flick, thrust, press, wring, slash) and combined them to recognize individual qualities. In our experiments, the second method gives improved results. Together, those findings suggest that low-dimensional signals from VR trackers can be used to predict motion qualities with reasonable precision. CCS CONCEPTS • Computing methodologies → Maximum likelihood modeling; Classification and regression trees;

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Section: Gesture Recognitionmentioning

confidence: 99%

Recognition of Laban Effort Qualities from Hand Motion

Garcia

Ronfard

2020

Proceedings of the 7th International Conference on Movement and Computing

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“…This is especially true for the Effort category, which describes qualitative aspects of movement. Development of a system capable of quantifying the majority of LMA components in unscripted (natural or improvised) movements of multiple people has remained a challenge, although researchers have made progress on quantification of limited sets of components (Davis, 1981; Gross et al, 2010, 2012; Samadani et al, 2013; Aristidou et al, 2015; Bernstein et al, 2015a,b) or in situations of prescribed movements (Nakata et al, 2002; Hachimura et al, 2005; Torresani et al, 2007; Truong and Zaharia, 2017) or used averaging to overcome the noisy data, and were unable to quantify data for statistical analysis (e.g., Senecal et al, 2016).…”

Section: Quantifying Movement Variablesmentioning

confidence: 99%

How Shall I Count the Ways? A Method for Quantifying the Qualitative Aspects of Unscripted Movement With Laban Movement Analysis

Tsachor

Shafir

2019

Front. Psychol.

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There is significant clinical evidence showing that creative and expressive movement processes involved in dance/movement therapy (DMT) enhance psycho-social well-being. Yet, because movement is a complex phenomenon, statistically validating which aspects of movement change during interventions or lead to significant positive therapeutic outcomes is challenging because movement has multiple, overlapping variables appearing in unique patterns in different individuals and situations. One factor contributing to the therapeutic effects of DMT is movement’s effect on clients’ emotional states. Our previous study identified sets of movement variables which, when executed, enhanced specific emotions. In this paper, we describe how we selected movement variables for statistical analysis in that study, using a multi-stage methodology to identify, reduce, code, and quantify the multitude of variables present in unscripted movement. We suggest a set of procedures for using Laban Movement Analysis (LMA)-described movement variables as research data. Our study used LMA, an internationally accepted comprehensive system for movement analysis, and a primary DMT clinical assessment tool for describing movement. We began with Davis’s (1970) three-stepped protocol for analyzing movement patterns and identifying the most important variables: (1) We repeatedly observed video samples of validated ( Atkinson et al., 2004 ) emotional expressions to identify prevalent movement variables, eliminating variables appearing minimally or absent. (2) We use the criteria repetition, frequency, duration and emphasis to eliminate additional variables. (3) For each emotion, we analyzed motor expression variations to discover how variables cluster: first, by observing ten movement samples of each emotion to identify variables common to all samples; second, by qualitative analysis of the two best-recognized samples to determine if phrasing, duration or relationship among variables was significant. We added three new steps to this protocol: (4) we created Motifs (LMA symbols) combining movement variables extracted in steps 1–3; (5) we asked participants in the pilot study to move these combinations and quantify their emotional experience. Based on the results of the pilot study, we eliminated more variables; (6) we quantified the remaining variables’ prevalence in each Motif for statistical analysis that examined which variables enhanced each emotion. We posit that our method successfully quantified unscripted movement data for statistical analysis.

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“…The proposed framework utilizes the hidden Markov model (HMM) algorithm as its learning algorithm. Truong et al [26] proposed a 3D gesture recognition framework using Laban movement analysis and HMM models. Ma et al [27] proposed an enhanced HMM model that could recognize handwritten characters in real time.…”

Section: Related Workmentioning

confidence: 99%

Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors

et al. 2019

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With intelligent big data, a variety of gesture-based recognition systems have been developed to enable intuitive interaction by utilizing machine learning algorithms. Realizing a high gesture recognition accuracy is crucial, and current systems learn extensive gestures in advance to augment their recognition accuracies. However, the process of accurately recognizing gestures relies on identifying and editing numerous gestures collected from the actual end users of the system. This final end-user learning component remains troublesome for most existing gesture recognition systems. This paper proposes a method that facilitates end-user gesture learning and recognition by improving the editing process applied on intelligent big data, which is collected through end-user gestures. The proposed method realizes the recognition of more complex and precise gestures by merging gestures collected from multiple sensors and processing them as a single gesture. To evaluate the proposed method, it was used in a shadow puppet performance that could interact with on-screen animations. An average gesture recognition rate of 90% was achieved in the experimental evaluation, demonstrating the efficacy and intuitiveness of the proposed method for editing visualized learning gestures.

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Laban movement analysis and hidden Markov models for dynamic 3D gesture recognition

Cited by 12 publications

References 32 publications

Recognition of Laban Effort Qualities from Hand Motion

Recognition of Laban Effort Qualities from Hand Motion

How Shall I Count the Ways? A Method for Quantifying the Qualitative Aspects of Unscripted Movement With Laban Movement Analysis

Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors

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