Controlling a remotely located robot using hand gestures in real time: A DSP implementation

Raheja, Jagdish Lal; Rajsekhar, G A; Chaudhary, Ankit

doi:10.1109/wecon.2016.7993420

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…The current and hot issues are teleoperation and telepresence in research. Many systems used various grounded device like space balls or joystick to control the operation of the robotic arm [5]- [6], some uses sensors [7] whereas some systems imply artificial intelligence-trained models to mimic the operator's arm positions [8], different approaches have been used for the realization of force feedback using SMA clutch mechanism on distal finger segments [9] and through spikes generated through a neural spiking model on receiving force signal via load cells mounted on the robotic end effector [10]. A power assist system is a human-robot cooperation technology and is mostly used in the rehabilitation and healthcare of disabled or older people [11].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Force Sensation and Feedback Systems for Telepresence Robotic Arm

Nisa

Samo

Nizamani

et al. 2022

Journal of Robotics and Control

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Humans put their own lives aside to save other human’s life and perform risky and dangerous activities. The risk can be reduced by using new technologies. This research study focuses on telepresence and teleoperation systems with motion and force control systems that replace humans in hazardous workspaces. In telepresence, the system helps humans to visualize the environment in real-time. In teleoperation, the system provides sensation to assist human beings in performing out-of-reach and dangerous operations safely as in real, providing a shadow hand to the operator. In this study, a system is developed that consists of a slave robotic arm and a master wearable device with bidirectional communication between the robotic arm and operator (master wearable device). It also presents a gesture-controlled robotic arm that uses sensors to read and translate human arm movements as commands. The slave robotic arm, senses applied force on an object and a master wearable device develops the force according to sensed force, in a result operator senses/feels the same object in the control room at distance. The slave robotic arm also mimics the operator arm to reach the proper position of an object. Several experiments were conducted with untrained personnel and satisfactory results were yielded, which showed that the motion and force replication is 90-95% accurate.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of Force Sensation and Feedback Systems for Telepresence Robotic Arm

Nisa

Samo

Nizamani

et al. 2022

Journal of Robotics and Control

View full text Add to dashboard Cite

show abstract

“…Hand gesture recognition can be divided into static hand gesture recognition and dynamic hand gesture recognition according to the recognized subject. Compared to static hand gesture recognition, dynamic hand gesture recognition has a more natural and comfortable interactive experience in some practical applications such as sign language recognition [ 1 ], interaction systems [ 2 ], virtual reality [ 3 ], interactive gaming [ 4 ], and human-machine collaborations [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography

Sun

Wang

et al. 2022

Sensors

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Electrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with static gestures, dynamic gestures are more informative and can achieve more functions in human-machine collaboration. In order to verify the feasibility of dynamic gesture recognition based on EIT, a traditional excitation drive pattern is optimized in this paper. The drive pattern of the fixed excitation electrode is tested for the first time to simplify the measurement process of the dynamic gesture. To improve the recognition accuracy of the dynamic gestures, a dual-channel feature extraction network combining a convolutional neural network (CNN) and gated recurrent unit (GRU), namely CG-SVM, is proposed. The new center distance loss is designed in order to simultaneously supervise the intra-class distance and inter-class distance. As a result, the discriminability of the confusing data is improved. With the new excitation drive pattern and classification network, the recognition accuracy of different interference data has increased by 2.7~14.2%. The new method has stronger robustness, and realizes the dynamic gesture recognition based on EIT for the first time.

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“…Recently, hand gesture recognition research has developed rapidly, which is an essential element in the development of new technologies in the computer vision and pattern recognition fields. Especially, real-time 3D hand pose estimation combined with depth cameras has contributed to the successful launch of virtual reality and augmented reality applications such as sign language recognition [1], virtual reality [2], robotics [3], interaction systems [4], and interactive gaming [5]. Nevertheless, there exist various challenges hindering the achievement of accurate results due to the complex topology of the hand skeleton with high similarity among fingers and a small size.…”

Section: Introductionmentioning

confidence: 99%

Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM

Kim

Yang

et al. 2020

Applied Sciences

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This study builds robust hand shape features from the two modalities of depth and skeletal data for the dynamic hand gesture recognition problem. For the hand skeleton shape approach, we use the movement, the rotations of the hand joints with respect to their neighbors, and the skeletal point-cloud to learn the 3D geometric transformation. For the hand depth shape approach, we use the feature representation from the hand component segmentation model. Finally, we propose a multi-level feature LSTM with Conv1D, the Conv2D pyramid, and the LSTM block to deal with the diversity of hand features. Therefore, we propose a novel method by exploiting robust skeletal point-cloud features from skeletal data, as well as depth shape features from the hand component segmentation model in order for the multi-level feature LSTM model to benefit from both. Our proposed method achieves the best result on the Dynamic Hand Gesture Recognition (DHG) dataset with 14 and 28 classes for both depth and skeletal data with accuracies of 96.07% and 94.40%, respectively.

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Controlling a remotely located robot using hand gestures in real time: A DSP implementation

Cited by 8 publications

References 10 publications

Design and Implementation of Force Sensation and Feedback Systems for Telepresence Robotic Arm

Design and Implementation of Force Sensation and Feedback Systems for Telepresence Robotic Arm

Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography

Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM

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