A Kinect-Based Motion Capture System for Robotic Gesture Imitation

Rosado, José; Silva, Filipe; Santos, Vítor

doi:10.1007/978-3-319-03413-3_43

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…An incorrect skeleton joint in a motion capture system is even more damaging than a missed joint since it incorrectly guides the system to infer posture. Therefore, we applied an index called the vibration degree to evaluate the reliability of the joint [28].…”

Section: Reliability Measurementmentioning

confidence: 99%

A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique

Niu

Wang

et al. 2020

Sensors

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Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) “skeleton” joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect.

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Section: Reliability Measurementmentioning

confidence: 99%

A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique

Niu

Wang

et al. 2020

Sensors

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“…This kind of research touches many different areas, among them interaction modalities. Teleoperation can be performed with haptic controllers [19], [4], or by imitation learning with mapping the human motion to a robot [22], [8], [15], [1]. Other ways exist to make human robot interaction more natural, we talk about gesture recognition approach.…”

Section: State Of Artmentioning

confidence: 99%

Gesture recognition for humanoid robot teleoperation

Ajili

Mallem

Didier

2017

2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Interactive robotics is a vast and expanding research field. Interactions must be sufficiently natural, with robots having socially acceptable behavior by humans, adaptable to user expectations. Thus allowing easy integration in our daily lives in various fields (science, industry, health ...). Natural interaction during human-robot collaborative action needs suitable interaction techniques. In our paper we develop an online gesture recognition system for natural and intuitive communication between Human and NAO robot. However recognizing meaningful gesture patterns from whole-body gestures is a complex task. That is why we used the Laban Movement Analysis technique to describe high level gestures for NAO teleoperation. The major contributions of the present work is: (1) an efficient preprocessing step based on view invariant human motion, (2) a robust descriptor vector based on Laban Movement Analysis technique to generate compact and informative representations of Human movement, and (3) an online gesture recognition with Hidden Markov Model method was applied to teleoperate NAO based on our proper data base dedicated to the teleoperation of NAO. Our approach was evaluated with two challenging datasets, Microsoft Research Cambridge-12 (MSRC-12) and UTKinect-Action. Experimental results show that our approach outperforms the state-of-the-art methods.

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“…The pose of an actor is then mapped to a simulated HOAP-2 humanoid robot, using inverse kinematics and iterative optimization. Rosado et al [5] use a Kinect to provide estimates on 3D human poses that are corrected based on constraint optimization and computed joint angles are mapped to upper limbs of a simulated 4 DOF robot model. Suleiman et al [6] formulate the imitation problem for the upper body motion as an optimisation problem, in which the physical limits of a humanoid robot are viewed as constraints and the objective function to be minimized is the difference between the angular values of the robot joints and the ones of the actor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The recuperated angles are constrained within the limits allowed by the robot, and used to control the movement of the arms of the robot. Results are presented not only in the simulator as in [3][4][5][6], but also on a real humanoid robot.…”

Section: Literature Reviewmentioning

confidence: 99%

Human arm motion imitation by a humanoid robot

Filiatrault

Creţu

2014

2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings

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The objective of this work is the development of a system capable to control the arm movement of a robot by mimicking the gestures of an actor captured by a markerless vision sensor. The Kinect for Xbox is used to recuperate angle information at the level of the actor's arm and an interaction module transforms it into a usable format for real-time robot arm control. To avoid self-collisions, the distance between the two arms is computed in real-time and the motion is not executed if this distance becomes smaller the twice the diameter of the member. The interfacing issues are discussed and a software architecture is proposed and implemented for this purpose. The feasibility of our approach is demonstrated on a NAO robot.

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A Kinect-Based Motion Capture System for Robotic Gesture Imitation

Cited by 12 publications

References 11 publications

A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique

A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique

Gesture recognition for humanoid robot teleoperation

Human arm motion imitation by a humanoid robot

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