A kinematic and EMG dataset of online adjustment of reach-to-grasp movements to visual perturbations

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.

Synergy-based functional electrical stimulation and robotic-assisted for retraining reach-to-grasp in stroke: a study protocol for a randomized controlled trial

“…The frequency, repeated time and rest time will be consistent with separate FES or RAT. To realize the synchronous triggering and control of FES and RAT, the control terminal will be developed through a multifunctional I/O Device (NI6255, National Instruments Inc., Austin, TX) [ 36 ]. The FES stimulus envelope and manipulator kinematic datasets will be aligned via the start switch trigger recorded digitally in HANDoVR, and the analog reading of the digital output will be sent from HANDoVR to MATLAB.…”

“…The outcomes of kinematic analysis will include the following: the movement time (ms), peak velocity (cm/s), time to peak transport velocity (ms), trajectory length ratio (%), trajectory smoothness, peak aperture (cm), peak aperture velocity (cm/s), opening distance (cm) and reach grasp coupling index. The definitions of kinematic variables have been described in several previous studies [ 36 , 40 , 42 – 44 ]. Additionally, the muscle synergy pattern will be extracted from the raw EMG signals through the method of non-negative matrix [ 11 , 32 ], which considers muscle activation as the linear combination of multiple muscle components (muscle vectors) with the corresponding activation coefficients (time profiles).…”

Synergy-based functional electrical stimulation and robotic-assisted for retraining reach-to-grasp in stroke: a study protocol for randomised controlled trial

“…The frequency, repeated time and rest time will be consistent with separate FES or RAT. In order to realize the synchronous triggering and control of FES and RAT, the control terminal will be developed through Multifunctional I/O Device (NI6255, National Instruments Inc., Austin, TX) [35]. The FES stimulus envelope and manipulator kinematic data sets were aligned via the start switch trigger recorded digitally in HANDoVR, and the analog reading of the digital output sent from HANDoVR to MATLAB.…”

“…The outcomes of kinematic analysis will include the following: movement time (ms), peak velocity (cm/s), time to peak transport velocity (ms), trajectory length ratio (%), trajectory smoothness, peak aperture (cm), peak aperture velocity (cm/s), opening distance (cm) and reach grasp coupling index. The de nitions of kinematic variables have been described in several previous studies [35,[39][40][41][42]. Additionally, the muscle synergy will be extracted from the raw EMG signals through the method of nonnegative matrix [11,31], which process considered muscle activation as the linear combination of multiple muscle components (muscle vectors) with the corresponding activation coe cients (time pro les).…”

Forearm sEMG data from young healthy humans during the execution of hand movements

“…In the acquisition process were placed 28 monopolar sEMG electrodes in the form of four rings around the forearm, which caused high-time consumption in the acquisition protocol and limited large-scale data collection. Mariusz P. Furmanek et al ., generated a kinematic and sEMG dataset of online adjustment of reach-to-grasp movements to instantaneous perturbations with 20 participants using a virtual environment for kinematic measurement and EMG sensors applying a specific and thorough protocol 14 . These aspects caused the low reproducibility of the data acquisition.…”