A Systematic Analysis of Hand Movement Functionality: Qualitative Classification and Quantitative Investigation of Hand Grasp Behavior.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu Y
PROVIDER: S-EPMC8216684 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Modeling and understanding human grasp functionality are fundamental in prosthetics, robotics, medicine, and rehabilitation, since they contribute to exploring motor control mechanism, evaluating grasp function, and designing and controlling prosthetic hands or exoskeletons. However, there are still limitations in providing a comprehensive and quantitative understanding of hand grasp functionality. After simultaneously considering three significant and essential influence factors in daily grasping contained relative position, object shape, and size, this paper presents the tolerance grasping to provide a more comprehensive understanding of human grasp functionality. The results of joint angle distribution and variance explained by PCs supported that tolerance grasping can represent hand grasp functionality more comprehensively. Four synergies are found and account for 93% ± 1.5% of the overall variance. The ANOVA confirmed that there was no significant individual difference in the first four postural synergies. The common patterns of grasping behavior were found and characterized by the mean value of postural synergy across 10 subjects. The independence analysis demonstrates that the tolerance grasping results highly correlate with unstructured natural grasping and more accurately correspond to cortical representation size of finger movement. The potential for exploring the neuromuscular control mechanism of human grasping is discussed. The analysis of hand grasp characteristics that contained joint angle distribution, correlation, independence, and postural synergies, presented here, should be more representative to provide a more comprehensive understanding of hand grasp functionality.
| S-EPMC8660235 | biostudies-literature
Project description:This paper is the first in the two-part series quantitatively modelling human grasp functionality and understanding the way human grasp objects. The aim is to investigate the thumb movement behavior influenced by object shapes, sizes, and relative positions. Ten subjects were requested to grasp six objects (3 shapes × 2 sizes) in 27 different relative positions (3 X deviation × 3 Y deviation × 3 Z deviation). Thumb postures were investigated to each specific joint. The relative position (X, Y, and Z deviation) significantly affects thumb opposition rotation (Rot) and flexion (interphalangeal (IP) and metacarpo-phalangeal (MCP)), while the object property (object shape and size) significantly affects thumb abduction/adduction (ABD) motion. Based on the F value, the Y deviation has the primary effects on thumb motion. When the Y deviation changing from proximal to distal, thumb opposition rotation (Rot) and flexion (IP and MCP joint) angles were increased and decreased, respectively. For principal component analysis (PCA) results, thumb grasp behavior can be accurately reconstructed by first two principal components (PCs) which variance explanation ratio reached 93.8% and described by the inverse and homodromous coordination movement between thumb opposition and IP flexion. This paper provides a more comprehensive understanding of thumb grasp behavior. The postural synergies can reproduce the anthropomorphic motion, reduce the robot hardware, and control dimensionality. All of these provide a more accurate and general basis for the design and control of the bionic thumb and novel wearable assistant robot, thumb function assessment, and rehabilitation.
| S-EPMC8608516 | biostudies-literature
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.
| S-EPMC7449026 | biostudies-literature
Project description:Movement sensor data from seven static and dynamic dog behaviors (sitting, standing, lying down, trotting, walking, playing, and (treat) searching i.e. sniffing) was collected from 45 middle to large sized dogs with six degree-of-freedom movement sensors attached to the collar and the harness. With 17 dogs the collection procedure was repeated. The duration of each of the seven behaviors was approximately three minutes. The order of the tasks was varied between the dogs and the two repetitions (for the 17 dogs). The behaviors were annotated post-hoc based on the video recordings made with two camcorders during the tests with one second resolution. The annotations were accurately synchronized with the raw movement sensors data. The annotated data was originally used for training behavior classification machine learning algorithms for classifying the seven behaviors. The developed signal processing and classification algorithms are provided together with the raw measurement data and reference annotations. The description and results of the original investigation that the dataset relates to are found in: P. Kumpulainen, A. Valldeoriola Cardó, S. Somppi, H. Törnqvist, H. Väätäjä, P. Majaranta, Y. Gizatdinova, C. Hoog Antink, V. Surakka, M. V. Kujala, O. Vainio, A. Vehkaoja, Dog behavior classification with movement sensors placed on the harness and the collar, Applied Animal behavior Science, 241 (2021), 105,393.
| S-EPMC8777071 | biostudies-literature
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.
| S-EPMC5207689 | biostudies-literature
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.
| S-EPMC3879019 | biostudies-literature
Project description:The cerebellum plays important roles in motor coordination and learning. However, motor learning has not been quantitatively evaluated clinically. It thus remains unclear how motor learning is influenced by cerebellar diseases or aging, and is related with incoordination. Here, we present a new application for testing human cerebellum-dependent motor learning using prism adaptation. In our paradigm, the participant wearing prism-equipped goggles touches their index finger to the target presented on a touchscreen in every trial. The whole test consisted of three consecutive sessions: (1) 50 trials with normal vision (BASELINE), (2) 100 trials wearing the prism that shifts the visual field 25° rightward (PRISM), and (3) 50 trials without the prism (REMOVAL). In healthy subjects, the prism-induced finger-touch error, i.e., the distance between touch and target positions, was decreased gradually by motor learning through repetition of trials. We found that such motor learning could be quantified using the "adaptability index (AI)", which was calculated by multiplying each probability of [acquisition in the last 10 trials of PRISM], [retention in the initial five trials of REMOVAL], and [extinction in the last 10 trials of REMOVAL]. The AI of cerebellar patients less than 70 years old (mean, 0.227; n = 62) was lower than that of age-matched healthy subjects (0.867, n = 21; p < 0.0001). While AI did not correlate with the magnitude of dysmetria in ataxic patients, it declined in parallel with disease progression, suggesting a close correlation between the impaired cerebellar motor leaning and the dysmetria. Furthermore, AI decreased with aging in the healthy subjects over 70 years old compared with that in the healthy subjects less than 70 years old. We suggest that our paradigm of prism adaptation may allow us to quantitatively assess cerebellar motor learning in both normal and diseased conditions.
| S-EPMC4364988 | biostudies-literature
Project description:This study aimed to identify and qualify grasp-types used by patients with stroke and determine the clinical parameters that could explain the use of each grasp. Thirty-eight patients with chronic stroke-related hemiparesis and a range of motor and functional capacities (17 females and 21 males, aged 25-78), and 10 healthy subjects were included. Four objects were used (tissue packet, teaspoon, bottle and tennis ball). Participants were instructed to "grasp the object as if you are going to use it". Three trials were video-recorded for each object. A total of 456 grasps were analysed and rated using a custom-designed Functional Grasp Scale. Eight grasp-types were identified from the analysis: healthy subjects used Multi-pulpar, Pluri-digital, Lateral-pinch and Palmar grasps (Standard Grasps). Patients used the same grasps with in addition Digito-palmar, Raking, Ulnar and Interdigital grasps (Alternative Grasps). Only patients with a moderate or relatively good functional ability used Standard grasps. The correlation and regression analyses showed this was conditioned by sufficient finger and elbow extensor strength (Pluri-digital grasp); thumb extensor and wrist flexor strength (Lateral pinch) or in forearm supinator strength (Palmar grasp). By contrast, the patients who had severe impairment used Alternative grasps that did not involve the thumb. These strategies likely compensate specific impairments. Regression and correlation analyses suggested that weakness had a greater influence over grasp strategy than spasticity. This would imply that treatment should focus on improving hand strength and control although reducing spasticity may be useful in some cases.
| S-EPMC5695285 | biostudies-literature
Project description:Methanococcus maripaludis is a methanogenic archaeon. Within its genome, there are two operons for membrane associated hydrogenases, eha and ehb. To investigate the regulation of ehb on the cell, an S40 mutant was constructed in such a way that a portion of the ehb operon was replaced by pac cassette in the wild type parental strain S2 (done by Whitman's group at the University of Georgia). The S40 and S2 strains were grown in 14N and 15N media with acetate separately. A biological replicate was made by switching the media. Mass spectrometry based quantitative proteomics were done on the mixtures to investigate the differences in expression patterns between S40 and S2. Keywords: isotope labeling mass spectrometry, quantitative proteomics
2005-09-20 | GSE2744 | GEO
Project description:Various neurological conditions, such as stroke or spinal cord injury, result in an impaired control of the hand. One method of restoring this impairment is through functional electrical stimulation (FES). However, traditional FES techniques often lead to quick fatigue and unnatural ballistic movements. In this study, we sought to explore the capabilities of a non-invasive proximal nerve stimulation technique in eliciting various hand grasp patterns. The ulnar and median nerves proximal to the elbow joint were activated transcutanously using a programmable stimulator, and the resultant finger flexion joint angles were recorded using a motion capture system. The individual finger motions averaged across the three joints were analyzed using a cluster analysis, in order to classify the different hand grasp patterns. With low current intensity (<5?mA and 100?µs pulse width) stimulation, our results show that all of our subjects demonstrated a variety of consistent hand grasp patterns including single finger movement and coordinated multi-finger movements. This study provides initial evidence on the feasibility of a proximal nerve stimulation technique in controlling a variety of finger movements and grasp patterns. Our approach could also be developed into a rehabilitative/assistive tool that can result in flexible movements of the fingers.
| S-EPMC5707381 | biostudies-literature