Spatial Joint features for 3D human skeletal action  recognition system using spatial graph kernels

Kishore, P. V. V.; Kameswari, P Siva; Niharika, K. Mary; Tanuja, M; Bindu, M. N. Hima; Kumar, D. Anil; Kumar, E. Kiran; Kiran, M Teja

doi:10.14419/ijet.v7i1.1.10152

Cited by 6 publications

(2 citation statements)

References 22 publications

(31 reference statements)

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“…Despite the performance of 2D Localized Trajectories, it can be noted that some state-of-the-art approaches achieve better performance (e.g., [11,24,55,56,57,59,63,64,65,67]), as reported in Table 1, Table 2 and Table 3 and Table 5. We remark that most of these state-of-the-art approaches rely on 3D features [11,24,55,57,59,63,64,65,67]. Indeed, 3D descriptors are directly extracted from depth maps and/or 3D skeleton sequences.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Localized Trajectories for 2D and 3D Action Recognition

Papadopoulos

Demisse

Ghorbel

et al. 2019

Sensors

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The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets.

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Section: Experimental Evaluationmentioning

confidence: 99%

Localized Trajectories for 2D and 3D Action Recognition

Papadopoulos

Demisse

Ghorbel

et al. 2019

Sensors

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“…You can see all the skeleton points in Figure2of[24] article https://www. sciencepubco.com/index.php/ijet/article/download/10152/3614…”

mentioning

confidence: 99%

Case Study on Human-Robot Interaction of the Remote-Controlled Service Robot for Elderly and Disabled Care

Chivarov

Chikurtev

Chivarov³

et al. 2019

cai

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The tendency of continuous aging of the population and the increasing number of people with mobility difficulties leads to increased research in the field of Assistive Service Robotics. These robots can help with daily life tasks such as reminding to take medications, serving food and drinks, controlling home appliances and even monitoring health status. When talking about assisting people in their homes, it should be noted that they will, most of the time, have to communicate with the robot themselves and be able to manage it so that they can get the most out of the robot's services. This research is focused on different methods of remote control of a mobile robot equipped with robotic manipulator. The research investigates in detail methods based on control via gestures, voice commands, and web-based graphical user interface. The capabilities of these methods for Human-Robot Interaction (HRI) have been explored in terms of usability. In this paper, we introduce a new version of the robot Robco 19, new leap motion sensor control of the robot and a new multi-channel control system. The paper presents methodology for performing the HRI experiments from human perception and summarizes the results in applications of the investigated remote control methods in real life scenarios.

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The Effect of Demanding Mental Tasks on Electrodermal Activity and Heart Rate During Physical Activity: A Pilot Study

et al. 2023

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This paper presents the development of a virtual rehabilitation game and mental load with different difficulty blocks. The game was controlled with body tracking and physiological signals -electrocardiography and electrodermal activity -were recorded throughout the session. Several parametersheart rate (HR), heart rate variability (HRV), skin conductance level (SCL), skin conductance response (SCR), energy expenditure . . . -were extracted from these signals to check mental load influence on them. Mental load was found to affect the variation in kinetic power and instantaneous heart rate; a Support Vector Machine with linear kernel was trained with these two variables and an 82.3% accuracy rate was obtained. Furthermore, the mental load was reflected in the number of errors made by the volunteers, in the selection time and in the number of rounds in the game.INDEX TERMS Body tracker, ECG, EDA, mental load, rehabgame.

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Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

Cited by 6 publications

References 22 publications

Localized Trajectories for 2D and 3D Action Recognition

Localized Trajectories for 2D and 3D Action Recognition

Case Study on Human-Robot Interaction of the Remote-Controlled Service Robot for Elderly and Disabled Care

The Effect of Demanding Mental Tasks on Electrodermal Activity and Heart Rate During Physical Activity: A Pilot Study

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