Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for the MSRC-12 Dataset

Pfitscher, Miguel; Welfer, Daniel; Cuadros, Marco Antônio de Souza Leite; Gamarra, Daniel Fernando Tello

doi:10.1007/978-3-030-16657-1_21

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…Miguel et al [25] used a Kinect sensor and a Convolutional Neural Network (CNN) trained with the MSRC-12 dataset [33] to capture and classify gestures of a user and send related commands to a mobile robot. The used dataset was created by Microsft and had 6244 gesture instances of 12 actions.…”

Section: Related Workmentioning

confidence: 99%

An Innovative System for Supporting Acquisition and Reproduction of Gestures in Storytelling Humanoid Robots

Augello¹,

Ciulla²,

Cuzzocrea³

et al. 2020

JVLC

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The work describes a module that has been implemented for being included in a social humanoid robot architecture, in particular a storyteller robot, named NarRob. This module gives a humanoid robot the capability of mimicking and acquiring the motion of a human user in real-time. This allows the robot to increase the population of his dataset of gestures. The module relies on a Kinect based acquisition setup. The gestures are acquired by observing the typical gesture displayed by humans. The movements are then annotated by several evaluators according to their particular meaning, and they are organized considering a specific typology in the knowledge base of the robot. The properly annotated gestures are then used to enrich the narration of the stories. During the narration, the robot semantically analyses the textual content of the story in order to detect meaningful terms in the sentences and emotions that can be expressed. This analysis drives the choice of the gesture that accompanies the sentences when the story is read.

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Section: Related Workmentioning

confidence: 99%

An Innovative System for Supporting Acquisition and Reproduction of Gestures in Storytelling Humanoid Robots

Augello¹,

Ciulla²,

Cuzzocrea³

et al. 2020

JVLC

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“…• "the distances between the time series are computed using FastDTW" [13]. To gauge how commonly used this algorithm is, consider the fact that at least five papers use the term FastDTW in their title [14], [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates

Keogh

2022

IEEE Trans. Knowl. Data Eng.

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Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Because the classic DTW algorithm has quadratic time complexity, many ideas have been introduced to reduce its amortized time, or to quickly approximate it. One of the most cited approximate approaches is FastDTW. The FastDTW algorithm has well over a thousand citations and has been explicitly used in several hundred research efforts. In this work, we make a surprising claim. In any realistic data mining application, the approximate FastDTW is much slower than the exact DTW. This fact clearly has implications for the community that uses this algorithm: allowing it to address much larger datasets, get exact results, and do so in less time. Our observation also has a more sobering lesson for the community. This work may serve as a reminder to the community to exercise more caution in uncritically accepting published results.

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“…More importantly, the duration time of each hand gesture is usually different from each other, making it difficult to directly use the deep learning method for hand gesture recognition. Therefore, the design of hand gesture recognition algorithms based on the non-deep learning such as dynamic time warping (DTW) [29][30][31], Adaptive Boosting (AdaBoost) [32,33] and Hidden Markov model (HMM) [34,35], has received much attention. The data imbalance of AdaBoost results in the decrease of classification accuracy, and the calculation of the HMM model is complex.…”

Section: Introductionmentioning

confidence: 99%

A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

et al. 2020

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In this paper, a novel method for continuous hand gesture detection and recognition is proposed based on a frequency modulated continuous wave (FMCW) radar. Firstly, we adopt the 2-Dimensional Fast Fourier Transform (2D-FFT) to estimate the range and Doppler parameters of the hand gesture raw data, and construct the range-time map (RTM) and Doppler-time map (DTM). Meanwhile, we apply the Multiple Signal Classification (MUSIC) algorithm to calculate the angle and construct the angletime map (ATM). Secondly, a hand gesture detection method is proposed to segment the continuous hand gestures using a decision threshold. Thirdly, the central time-frequency trajectory of each hand gesture spectrogram is clustered using the k-means algorithm, and then the Fusion Dynamic Time Warping (FDTW) algorithm is presented to recognize the hand gestures. Finally, experiments show that the accuracy of the proposed hand gesture detection method can reach 96.17%. The hand gesture average recognition accuracy of the proposed FDTW algorithm is 95.83%, while its time complexity is reduced by more than 50%. INDEX TERMS FMCW radar, continuous hand gesture recognition, detection, time-frequency trajectory, FDTW.

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Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for the MSRC-12 Dataset

Cited by 9 publications

References 13 publications

An Innovative System for Supporting Acquisition and Reproduction of Gestures in Storytelling Humanoid Robots

An Innovative System for Supporting Acquisition and Reproduction of Gestures in Storytelling Humanoid Robots

FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates

A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

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