Data Acquisition from the Integration of Kinect Quaternions and Myo Armband EMG Sensors to Aid Equinus Foot Treatment

Araújo, Francisco Marcelino Almeida de; Ferreira, Nuno M. F.; Soares, Salviano; Valente, António; Gilson, Laurens

doi:10.5220/0007565902350240

Cited by 4 publications

(2 citation statements)

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“…Some researchers combine different teleoperation device [ 28 ] modes to improve arm tracking accuracy and stability. The experimental results of multimodal fusion, for example, Myo Armband and Leap Motion camera fusion [ 23 , 29 ], Kinect and EMG Fusion [ 30 ], Leap motion and data glove fusion [ 31 , 32 ], Vision and EEG [ 33 ], etc., have effectively verified the complementary advantages of multi-modal information.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

et al. 2023

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Sensor fusion is a technique that combines information from multiple sensors in order to improve the accuracy and reliability of the data being collected. In the context of teleoperation control of an anthropomorphic robotic arm, sensor fusion technology can be used to enhance the precise control of anthropomorphic robotic arms by combining data from multiple sensors, such as cameras, data gloves, force sensors, etc. By fusing and processing this sensing information, it can enable real-time control of anthropomorphic robotic arms and dexterous hands, replicating the motion of human manipulators. In this paper, we present a sensor fusion-based teleoperation control system for the anthropomorphic robotic arm and dexterous hand, which utilizes a filter to fuse data from multiple sensors in real-time. As such, the real-time perceived human arms motion posture information is analyzed and processed, and wireless communication is used to intelligently and flexibly control the anthropomorphic robotic arm and dexterous hand. Finally, the user is able to manage the anthropomorphic operation function in a stable and reliable manner. We also discussed the implementation and experimental evaluation of the system, showing that it is able to achieve improved performance and stability compared to traditional teleoperation control methods.

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Section: Introductionmentioning

confidence: 99%

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

et al. 2023

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“…Wang used IMU together with Apple Watch to reconstruct arm posture for upper-body exercises [34]. Several studies utilized both sEMG sensor and computervision; for instance, equinus foot treatment system by Araújo et al [35], prosthesis control interface by Blana et al [36], and rehabilitation video games by Rincon et al [37] and Esfahlani et al [38], respectively. As aforementioned, "an individual is performing a proper and exact workout" means that his or her joints are following a correct posture and target muscles are being stimulated primarily.…”

Section: Introductionmentioning

confidence: 99%

Classifying Upper Arm Gym-Workouts via Convolutional Neural Network by Imputing a Biopotential-Kinematic Relationship

Yoo

Jung

et al. 2021

Applied Sciences

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This paper proposes a systemic approach to upper arm gym-workout classification according to spatio-temporal features depicted by biopotential as well as joint kinematics. The key idea of the proposed approach is to impute a biopotential-kinematic relationship by merging the joint kinematic data into a multichannel electromyography signal and visualizing the merged biopotential-kinematic data as an image. Under this approach, the biopotential-kinematic relationship can be imputed by counting on the functionality of a convolutional neural network: an automatic feature extractor followed by a classifier. First, while a professional trainer is demonstrating upper arm gym-workouts, electromyography and joint kinematic data are measured by an armband-type surface electromyography (sEMG) sensor and a RGB-d camera, respectively. Next, the measured data are augmented by adopting the amplitude adjusted Fourier Transform. Then, the augmented electromyography and joint kinematic data are visualized as one image by merging and calculating pixel components in three different ways. Lastly, for each visualized image type, upper arm gym-workout classification is performed via the convolutional neural network. To analyze classification accuracy, two-way rANOVA is performed with two factors: the level of data augmentation and visualized image type. The classification result substantiates that a biopotential-kinematic relationship can be successfully imputed by merging joint kinematic data in-between biceps- and triceps-electromyography channels and visualizing as a time-series heatmap image.

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Integrating Sensor Fusion for Teleoperation Control of Anthropomorphic Dual-Arm Robots^*

Wang,

Chen,

Yang

et al. 2023

2023 WRC Symposium on Advanced Robotics and Automation (WRC SARA)

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Data Acquisition from the Integration of Kinect Quaternions and Myo Armband EMG Sensors to Aid Equinus Foot Treatment

Cited by 4 publications

References 0 publications

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Classifying Upper Arm Gym-Workouts via Convolutional Neural Network by Imputing a Biopotential-Kinematic Relationship

Integrating Sensor Fusion for Teleoperation Control of Anthropomorphic Dual-Arm Robots^*

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Data Acquisition from the Integration of Kinect Quaternions and Myo Armband EMG Sensors to Aid Equinus Foot Treatment

Cited by 4 publications

References 0 publications

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Classifying Upper Arm Gym-Workouts via Convolutional Neural Network by Imputing a Biopotential-Kinematic Relationship

Integrating Sensor Fusion for Teleoperation Control of Anthropomorphic Dual-Arm Robots*

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Integrating Sensor Fusion for Teleoperation Control of Anthropomorphic Dual-Arm Robots^*