Dataset Information

A Systematic Analysis of Hand Movement Functionality: Qualitative Classification and Quantitative Investigation of Hand Grasp Behavior.

ABSTRACT: Understanding human hand movement functionality is fundamental in neuroscience, robotics, prosthetics, and rehabilitation. People are used to investigate movement functionality separately from qualitative or quantitative perspectives. However, it is still limited to providing an integral framework from both perspectives in a logical manner. In this paper, we provide a systematic framework to qualitatively classify hand movement functionality, build prehensile taxonomy to explore the general influence factors of human prehension, and accordingly design a behavioral experiment to quantitatively understand the hand grasp. In qualitative analysis, two facts are explicitly proposed: (1) the arm and wrist make a vital contribution to hand movement functionality; (2) the relative position (relative position in this paper is defined as the distance between the center of the human wrist and the object center of gravity) is a general influence factor significantly impacting human prehension. In quantitative analysis, the significant influence of three factors, object shape, size, and relative position, is quantitatively demonstrated. Simultaneously considering the impact of relative position, object shape, and size, the prehensile taxonomy and behavioral experiment results presented here should be more representative and complete to understand human grasp functionality. The systematic framework presented here is general and applicable to other body parts, such as wrist, arm, etc. Finally, many potential applications and the limitations are clarified.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8216684 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A Systematic Analysis of Hand Movement Functionality: Qualitative Classification and Quantitative Investigation of Hand Grasp Behavior.

Liu Yuan Y Jiang Li L Liu Hong H Ming Dong D

Frontiers in neurorobotics 20210607

Understanding human hand movement functionality is fundamental in neuroscience, robotics, prosthetics, and rehabilitation. People are used to investigate movement functionality separately from qualitative or quantitative perspectives. However, it is still limited to providing an integral framework from both perspectives in a logical manner. In this paper, we provide a systematic framework to qualitatively classify hand movement functionality, build prehensile taxonomy to explore the general infl ...[more]

PMID: 34163345

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Project description:For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex's functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.

Project description:BackgroundCervical spinal cord injury severely affects grasping ability of its survivors. Fortunately, many individuals with tetraplegia retain residual arm movements that allow them to reach for objects. We propose a wearable technology that utilizes arm movement trajectory information and deep learning methods to determine grasp selection. Furthermore, we combined this approach with neuromuscular stimulation to determine if self-driven functional hand movement could be enabled in spinal cord injury participants.MethodsTwo cervical SCI participants performed arbitrary and natural reaching movements toward target objects in three-dimensional space, which were recorded using an inertial sensor worn on their wrist. Time series classifiers were trained to recognize the trajectories using either a Dynamic Time Warping (DTW) algorithm or a Long Short-Term Memory (LSTM) recurrent neural network. As an initial proof-of-concept, we demonstrate real-time classification of the arbitrary movements using DTW only (due to its implementation simplicity), which when used in combination with a high density neuromuscular stimulation sleeve with textile electrodes, enabled participants to perform functional grasping.ResultsParticipants were able to consistently perform arbitrary two-dimensional and three-dimensional arm movements which could be classified with high accuracy. Furthermore, it was found that natural reaching trajectories for two different target objects (requiring two different grasp types) were distinct and also discriminable with high accuracy. In offline comparisons, LSTM (mean accuracies 99%) performed significantly better than DTW (mean accuracies 86 and 83%) for both arbitrary and natural reaching movements, respectively. Type I and II errors occurred more frequently for DTW (up to 60 and 15%, respectively), whereas it stayed under 5% for LSTM. Also, DTW achieved online accuracy of 79%.ConclusionsWe demonstrate the feasibility of utilizing arm trajectory information to determine grasp selection using a wearable inertial sensor along with DTW and deep learning methods. Importantly, this technology can be successfully used to control neuromuscular stimulation and restore functional independence to individuals living with paralysis.Trial registrationNCT, NCT03385005. Registered September 26, 2017.

Project description:Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

Project description:PurposeRegaining hand function has been identified as the highest priority for persons with tetraplegia. In many patients, finger flexion can be restored with a tendon transfer of extensor carpi radialis longus to flexor digitorum profundus (FDP). In the absence of intrinsic function, this results in a roll-up finger movement, which tends to push large objects out of grasp. To enable patients to grasp objects of varying sizes, a functional grasp is required that has a larger excursion of fingertip-to-palm distance than can be supplied without intrinsic function. The aim of this study was to quantify the role of intrinsic muscle force in creating a functional grasp.MethodsFinger kinematics during grasp were measured on 5 cadaveric hands. To simulate finger flexion, the FDP was activated by a motor and intrinsic muscles were loaded at various levels (0, 125, 250, 375, or 500 g). Finger movement was characterized by the order of metacarpophalangeal, proximal interphalangeal, and distal interphalangeal joint flexion and by the maximal fingertip-to-palm distance during finger closure.ResultsWithout any intrinsic muscle contribution (0-g load), FDP activation resulted in flexion of all 3 joints, whereby flexion began at the proximal interphalangeal joint, followed by the distal interphalangeal joint, and then the metacarpophalangeal joint. With increasing intrinsic muscle load, finger flexion was initiated at the metacarpophalangeal joint, followed by the proximal interphalangeal and distal interphalangeal joints. This altered joint flexion order resulted in a larger maximal fingertip-to-palm distance during finger flexion. The difference between the 2 extreme conditions (0 g vs 500 g of intrinsic muscle load) was 19 mm.ConclusionsThese findings demonstrate that simultaneous activation of the FDP and the intrinsic muscles results in an apparently more functional hand closing compared with FDP activation alone because of altered kinematics and larger fingertip-to-palm distances.Clinical relevanceThese findings suggest that intrinsic muscle balancing during reconstruction of grasp in tetraplegic patients may improve function.

Dataset Information

A Systematic Analysis of Hand Movement Functionality: Qualitative Classification and Quantitative Investigation of Hand Grasp Behavior.

Publications

A Systematic Analysis of Hand Movement Functionality: Qualitative Classification and Quantitative Investigation of Hand Grasp Behavior.

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