Hankelet-based action classification for motor intention recognition

Dindo, Haris; Presti, Liliana Lo; Cascia, Marco La; Chella, Antonio; Dedić, Remzo

doi:10.1016/j.robot.2017.04.003

Cited by 15 publications

(5 citation statements)

References 49 publications

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“…In automated and semi-automated systems, complex behaviors are often modeled in terms of dynamical systems [22], [23]. In particular, behavior dynamics can be expressed as sequences of simple, stationary or quasi-stationary dynamical processes, each one characterised by its own set of parameters [24], [25], [26].…”

Section: A Human Behavior Characterizationmentioning

confidence: 99%

Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent

Monteleone

Presti

Cascia

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

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The study of human behaviors in cognitive sciences provides clues to understand and describe people's personal and interpersonal functioning. In particular, the temporal analysis of behavioral dynamics can be a powerful tool to reveal events, correlations and causalities but also to discover abnormal behaviors. However, the annotation of these dynamics can be expensive in terms of temporal and human resources. To tackle this challenge, this paper proposes a methodology to semi-automatically annotate behavioral data. Behavioral dynamics can be expressed as sequences of simple dynamical processes: transitions between such processes are generally known as change-points. This paper describes the necessary steps to detect and classify change-points in behavioral data by using a dataset collected in a real usecase scenario. This dataset includes motor observations from children with typical development and with neuro-developmental disorders. Abnormal movements which are present in such disorders are useful to validate the system in conditions that are challenging even for experienced annotators. Results show that the system: can be effective in the semi-automated annotation task; can be efficient in presence of abnormal behaviors; may achieve good performance when trained with limited manually annotated data.

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Section: A Human Behavior Characterizationmentioning

confidence: 99%

Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent

Monteleone

Presti

Cascia

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

Self Cite

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“…Dynamic Time Warping (DTW) is one of the most utilized similarity measures for matching two time-series sequences [11,12]. Often reproached for being slow, Rakthanmanon et al [13] demonstrated that DTW is quicker than Euclidean distance search algorithms and even suggests that the method can spot gestures in real time.…”

Section: Introductionmentioning

confidence: 99%

A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

Otis

Vandewynckel

2021

Applied Sciences

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Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.

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Section: Introductionmentioning

confidence: 99%

“…Machine learning is an extremely important tool that is widely applied in biomedical engineering (Dindo et al, 2017;Mcfarland and Wolpaw, 2017;Ofner et al, 2017;Pereira et al, 2017;Bockbrader et al, 2018). For studying action intention understanding, good classification accuracy is one of the most critical factors (Dindo et al, 2017;Ofner et al, 2017;Bockbrader et al, 2018). In recent years, many researchers have performed numerous experiments with machine learning, but most of the classification results are unsatisfactory (Zhang et al, 2015(Zhang et al, , 2017Liu et al, 2017;Pereira et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Weighted Brain Network Metrics for Decoding Action Intention Understanding Based on EEG

Xiong

et al. 2020

Front. Hum. Neurosci.

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Background: Understanding the action intentions of others is important for social and human-robot interactions. Recently, many state-of-the-art approaches have been proposed for decoding action intention understanding. Although these methods have some advantages, it is still necessary to design other tools that can more efficiently classify the action intention understanding signals. New Method: Based on EEG, we first applied phase lag index (PLI) and weighted phase lag index (WPLI) to construct functional connectivity matrices in five frequency bands and 63 micro-time windows, then calculated nine graph metrics from these matrices and subsequently used the network metrics as features to classify different brain signals related to action intention understanding. Results: Compared with the single methods (PLI or WPLI), the combination method (PLI+WPLI) demonstrates some overwhelming victories. Most of the average classification accuracies exceed 70%, and some of them approach 80%. In statistical tests of brain network, many significantly different edges appear in the frontal, occipital, parietal, and temporal regions. Conclusions: Weighted brain networks can effectively retain data information. The integrated method proposed in this study is extremely effective for investigating action intention understanding. Both the mirror neuron and mentalizing systems participate as collaborators in the process of action intention understanding.

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Hankelet-based action classification for motor intention recognition

Cited by 15 publications

References 49 publications

Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent

Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent

A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

Weighted Brain Network Metrics for Decoding Action Intention Understanding Based on EEG

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