Electromyography data for non-invasive naturally-controlled robotic hand prostheses

By Manfredo Atzori, A. Gijsberts, Claudio Castellini, Barbara Caputo, A. M. Hager, Simone Elsig, G. Giatsidis, F. Bassetto, Henning Müller. Scientific Data, 2014

915 citations recorded by the source corpus.

Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible. Design Type(s) in vivo design • injury design • observation design • parallel group design Measurement Type(s) muscular activity Technology Type(s) electromyography Factor Type(s) Injury Sample Characteristic(s) Homo sapiens • adult organism Design Type(s) in vivo design • injury design • observation design • parallel group design Measurement Type(s) muscular activity Technology Type(s) electromyography Factor Type(s) Injury Sample Characteristic(s) Homo sapiens • adult organism Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0686645a028567f6221ea6bfd54ae96a8f56145c

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Citations in the corpus

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors (2018, methodology): “These datasets were collected independently at different institutes and are publicly available [26–28 , 31 The experiment from which the last EMG dataset resulted was divided into three exercises [34].”
Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network (2017, methodology): “To validate our results we compared the performance of the proposed classiﬁer with SVM which has been shown to achieve the top performance on the NinaPro database (Atzori et al., 2014; Zhai et al., 2016).”
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation (2017, methodology): “We evaluated our approach using three public databases: (1) the CSL-HDEMG database [2]; (2) the NinaPro database [48] (sub-database 1); and (3) our CapgMyo database.”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, methodology): “As previously shown, results over 90% of accuracy can be obtained reducing the number of classified movements to approximately 10 for amputees, even starting from lower classification accuracies (Atzori et al., 2014a, 2016).”
Comparison of six electromyography acquisition setups on hand movement classification tasks (2017, methodology): “In particular, performance of unbalanced studies can be strongly augmented by the high number of rest repetitions (which are easier to classified) [11].”
Gesture recognition by instantaneous surface EMG images (2016, neutral): “For our CapgMyo database, an instantaneous sEMG image was formed as an 8 for CapgMyo, a 7 × 24 image for CSL-HDEMG 23 and a 1 × 10 image for NinaPro 35 .”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “, 2016 [65] Hand movement classification CNN NinaPro DB [62] accuracy: 66.”