Discriminative functional analysis of human movements

Samadani, Ali-Akbar; Ghodsi, Ali; Kulić, Dana

doi:10.1016/j.patrec.2012.12.018

Cited by 20 publications

(10 citation statements)

References 36 publications

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“…In computational human motion analysis, movements are commonly represented in terms of joint angle trajectories (e.g., [10]), or derived discrete or time-series features (e.g., maximum velocity [11]). Movement modeling based on these kinematic features does not necessarily capture expressive qualities.…”

Section: Introductionmentioning

confidence: 99%

Laban Effort and Shape Analysis of Affective Hand and Arm Movements

Samadani

Burton

Gorbet

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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Abstract-The Laban Effort and Shape components provide a systematic tool for a compact and informative description of the dynamic qualities of movements. To enable the application of Laban notation in computational movement analysis, measurable physical correlates of Effort and Shape components need to be identified. Such physical correlates enable quantification of Effort and Shape components, which in turn facilitates computational analysis of affective movements. In this work, two existing approaches to quantification of Laban Effort components (Weight, Time, Space, and Flow) based on measurable movement features (position, velocity, acceleration, and jerk) are adapted for hand and arm movements, and a new approach for quantifying Shape Directional based on the average trajectory curvature is proposed. The results show a high correlation between Laban annotations provided by a certified movement analyst (CMA) and the quantified Effort Weight (81%), Time (77%) and Shape Directional (93%) for an affective hand and arm movement dataset.

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Section: Introductionmentioning

confidence: 99%

Laban Effort and Shape Analysis of Affective Hand and Arm Movements

Samadani

Burton

Gorbet

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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“…Dimensional reduction techniques are usually applied to this type of data to simplify its structure. As stated by Samadani et al, Statistical dimensionality reduction (DR) techniques has the potential to reduce a high-dimensional data to a lower-dimensional subspace [72].…”

Section: Using Dimensional Reductionmentioning

confidence: 99%

“…In their studies, the authors did not combine the hand/arm model with the full body data set, and did not incorporate any high-level motion analysis. Samadani et al [72] investigated the use of statistical dimensionality reduction techniques in emotion recognition from body movement. A fixed length representation of the features was obtained from sequential observations using the Basis Function Expansion method.…”

Section: Using Dimensional Reductionmentioning

confidence: 99%

“…Not only has there been differences in identification across cultures, but also age groups from the one culture can identify different emotions from the same body expression [87]. Alternatively, [48], [55], [54], [72], [75] use the emotion intended by actors as the intended emotion. This, however, relies on the expertise of actors, which is not often reliable and actors tend to only use the extremes of each emotion which can create an unwanted bias.…”

Section: Authorsmentioning

confidence: 99%

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Automatic Affect Perception Based on Body Gait and Posture: A Survey

Stephens-Fripp

Naghdy

Stirling

et al. 2017

Int J of Soc Robotics

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There has been a growing interest in machine-based recognition of emotions from body gait and its combination with other modalities. In order to highlight the major trends and state of the art in this area, the literature dealing with machine-based human emotion perception through gait and posture is explored. Initially the effectiveness of human intellect and intuition in perceiving emotions in a range of cultures is examined. Subsequently, major studies in machine-based affect recognition are reviewed and their performance is compared. The survey concludes by critically analysing some of the issues raised in affect recognition using gait and posture, and identifying gaps in the current understanding in this area.

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“…1 no previous study has considered FPCA for robot imitation. Samadani et al 26,27 proposed a method based on FPCA for recognition of a®ective states using hand motion and full-body data. The method, which was not focused on robot imitation, was evaluated on four a®ective states (anger, happiness, fear and sadness).…”

Section: State Of the Artmentioning

confidence: 99%

Functional Principal Component Analysis for Recognition of Arm Gestures and Humanoid Imitation

Aleotti

Caselli

2013

Int. J. Human. Robot.

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This paper investigates the use of functional principal component analysis (FPCA) for automatic recognition of dynamic human arm gestures and robot imitation. FPCA is a statistical technique of functional data analysis that generalizes standard multivariate principal component analysis. Functional data analysis signals (e.g., gestures) are functions that are considered as observations of a random variable on a functional space. In particular, FPCA reduces the dimensionality of the input data by projecting them onto a¯nite-dimensional space spanned by a few prominent eigenfunctions. The main contribution of this work is the proposal of a novel technique for unsupervised clustering of training data and dynamic gesture recognition based on FPCA. FPCA has not been considered in previous studies on humanoid learning. The proposed approach has been evaluated in two experimental settings for motion capture. In the¯rst setup single arm gestures are recognized from inertial sensors attached to the arm of the user. In the second setup the method is extended to two-arm gestures acquired from a range sensor. Recognized gestures are reproduced by a small humanoid robot. The FPCA method has also been compared to a high performance algorithm for gesture classi¯cation based on dynamic time warping (DTW). The FPCA algorithm achieves comparable results in both recognition rate and robustness to missing data, while it outperforms DTW in terms of e±ciency in execution time.

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Discriminative functional analysis of human movements

Cited by 20 publications

References 36 publications

Laban Effort and Shape Analysis of Affective Hand and Arm Movements

Laban Effort and Shape Analysis of Affective Hand and Arm Movements

Automatic Affect Perception Based on Body Gait and Posture: A Survey

Functional Principal Component Analysis for Recognition of Arm Gestures and Humanoid Imitation

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