ArticleScientific data2022
A kinematic and EMG dataset of online adjustment of reach-to-grasp movements to visual perturbations.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 23 citations in OpenAlex.
- From muscles to motion: the role of sensor layout and physiological factors in hand motion decoding.Scientific reports · 2026Article
- A multimodal biomechanics dataset with synchronized kinematics and internal tissue motions during reaching.Scientific data · 2026Article
- A database of multimodal myography and hand kinematics during realistic daily life activities.Scientific data · 2025Article
- A high-resolution and whole-body dataset of hand-object contact areas based on 3D scanning method.Scientific data · 2025Article
- Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements.Scientific data · 2025Article
- Assisting hand gesture classification and rehabilitation assessment via sEMG and finger motion data.Frontiers in bioengineering and biotechnology · 2025Article
- Body-Related Visual Biasing Affects Accuracy of Reaching.Brain sciences · 2024Article
- Perturbing reach elicits anticipatory responses in transport and grasp.Frontiers in human neuroscience · 2024Article
- Electromyography and kinematics data of the hand in activities of daily living with special interest for ergonomics.Scientific data · 2023Article
- Synergy-based functional electrical stimulation and robotic-assisted for retraining reach-to-grasp in stroke: a study protocol for a randomized controlled trial.BMC neurology · 2023Article
- HANDdata - first-person dataset including proximity and kinematics measurements from reach-to-grasp actions.Scientific data · 2023Article
- Forearm sEMG data from young healthy humans during the execution of hand movements.Scientific data · 2023Article
- A kinematic, imaging and electromyography dataset for human muscular manipulability index prediction.Scientific data · 2023Article
- A kinematic and EMG dataset of online adjustment of reach-to-grasp movements to visual perturbations.Scientific data · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
Control of reach-to-grasp movements for deft and robust interactions with objects requires rapid sensorimotor updating that enables online adjustments to changing external goals (e.g., perturbations or instability of objects we interact with). Rarely do we appreciate the remarkable coordination in reach-to-grasp, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging. Modeling online control of human reach-to-grasp movements is a challenging problem but fundamental to several domains, including behavioral and computational neuroscience, neurorehabilitation, neural prostheses, and robotics. Currently, there are no publicly available datasets that include online adjustment of reach-to-grasp movements to object perturbations. This work aims to advance modeling efforts of reach-to-grasp movements by making publicly available a large kinematic and EMG dataset of online adjustment of reach-to-grasp movements to instantaneous perturbations of object size and distance performed in immersive haptic-free virtual environment (hf-VE). The presented dataset is composed of a large number of perturbation types (10 for both object size and distance) applied at three different latencies after the start of the movement.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.