A kinematic, imaging and electromyography dataset for human muscular manipulability index prediction
暂无分享,去创建一个
G. J. García | C. Jara | A. Úbeda | Francisco Gomez-Donoso | Óscar G. Hernández | V. Morell-Gimenez | Jose M. Lopez-Castellanos
[1] Qiang Zhang,et al. A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control , 2022, Wearable Technologies.
[2] G. Palmieri,et al. Manipulability Optimization of a Rehabilitative Collaborative Robotic System , 2022, Machines.
[3] Mariusz P. Furmanek,et al. A kinematic and EMG dataset of online adjustment of reach-to-grasp movements to visual perturbations , 2022, Scientific data.
[4] Jason R. Franz,et al. Personalized fusion of ultrasound and electromyography-derived neuromuscular features increases prediction accuracy of ankle moment during plantarflexion , 2022, Biomed. Signal Process. Control..
[5] Robert D. Gregg,et al. Lower-limb kinematics and kinetics during continuously varying human locomotion , 2021, Scientific Data.
[6] Robert P. Sheridan,et al. Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships , 2021, ArXiv.
[7] C. Santos,et al. Lower limb kinematic, kinetic, and EMG data from young healthy humans during walking at controlled speeds , 2021, Scientific data.
[8] C. Cipriani,et al. A database of high-density surface electromyogram signals comprising 65 isometric hand gestures , 2021, Scientific data.
[9] Roberto Merletti,et al. High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans , 2020, Scientific data.
[10] Hyun Kim,et al. Evaluation of Light Gradient Boosted Machine Learning Technique in Large Scale Land Use and Land Cover Classification , 2020, Environments.
[11] Jihed Khiari,et al. Boosting Algorithms for Delivery Time Prediction in Transportation Logistics , 2020, 2020 International Conference on Data Mining Workshops (ICDMW).
[12] Chenfei Ma,et al. Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach , 2020, Biomed. Signal Process. Control..
[13] D G Lloyd,et al. Machine learning methods to support personalized neuromusculoskeletal modelling , 2020, Biomechanics and Modeling in Mechanobiology.
[14] Ilaria Carpinella,et al. Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks , 2019, Scientific Data.
[15] Néstor J. Jarque-Bou,et al. A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living , 2019, Scientific Data.
[16] Itzel Jared Rodríguez Martínez,et al. SEEDS, simultaneous recordings of high-density EMG and finger joint angles during multiple hand movements , 2019, Scientific Data.
[17] Zhe Wang,et al. sEMG-Based Continuous Estimation of Knee Joint Angle Using Deep Learning with Convolutional Neural Network , 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).
[18] C. Schreiber,et al. A multimodal dataset of human gait at different walking speeds established on injury-free adult participants , 2019, Scientific Data.
[19] Jun Morimoto,et al. Assistive Arm-Exoskeleton Control Based on Human Muscular Manipulability , 2019, Front. Neurorobot..
[20] Dario Farina,et al. Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms , 2018, IEEE Transactions on Biomedical Engineering.
[21] Tie-Yan Liu,et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.
[22] Jun Morimoto,et al. Power-augmentation control approach for arm exoskeleton based on human muscular manipulability , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[23] Manfredo Atzori,et al. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands , 2016, Front. Neurorobot..
[24] Livio Pinto,et al. Calibration of Kinect for Xbox One and Comparison between the Two Generations of Microsoft Sensors , 2015, Sensors.
[25] Yoshiyuki Tanaka,et al. Human muscular mobility ellipsoid: End-point acceleration manipulability measure in fast motion of human upper arm , 2014 .
[26] Philippe Gorce,et al. The manipulability: a new index for quantifying movement capacities of upper extremity , 2012, Ergonomics.
[27] Peter I. Corke,et al. Robotics, Vision and Control - Fundamental Algorithms in MATLAB® , 2011, Springer Tracts in Advanced Robotics.
[28] Zafer Bingul,et al. Comparative study of performance indices for fundamental robot manipulators , 2006, Robotics Auton. Syst..
[29] Patrick van der Smagt,et al. Learning EMG control of a robotic hand: towards active prostheses , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..
[30] Yoshiyuki Tanaka,et al. Manipulability analysis of human arm movements during the operation of a variable-impedance controlled robot , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[31] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[32] Tsuneo Yoshikawa,et al. Manipulability of Robotic Mechanisms , 1985 .
[33] Tsuneo Yoshikawa,et al. Dynamic manipulability of robot manipulators , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.
[34] Yoshiyuki Tanaka,et al. Analysis of Operational Comfort in Manual Tasks Using Human Force Manipulability Measure , 2015, IEEE Transactions on Haptics.
[35] J. Denavit,et al. A kinematic notation for lower pair mechanisms based on matrices , 1955 .