Hand Grasps Research Articles

Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals.

Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost motor function in individuals with disabilities. Several research studies suggest that the CNS may employ synergies or movement primitives to reduce the complexity of control rather than controlling each DoF independently, and the synergies can be used as an optimal control mechanism by the CNS in simplifying and achieving complex movements. Our group has previously demonstrated neural decoding of synergy-based hand movements and used synergies effectively in driving hand exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of hand grasps representative of the activities of daily living while their neural activities were recorded using electroencephalography (EEG). From half of the participants, hand kinematic synergies were derived, and a neural decoder was developed, based on the correlation between hand synergies and corresponding cortical activity, using multivariate linear regression. Using the synergies and the neural decoder derived from the first half of the participants and only cortical activities from the remaining half of the participants, their hand kinematics were reconstructed with an average accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in individuals with upper limb motor deficits, implications of the results in individuals with stroke and the limitations of the study were discussed.

Dynamical Synergies in Multidigit Hand Prehension.

Hand prehension requires a highly coordinated control of contact forces. The high dimensional sensorimotor system of the human hand although operates at ease, poses several challenges when replicated for prosthetic control. This study investigates how the dynamical synergies, coordinated spatial patterns of contact forces, contribute to the contact forces in a grasp, and whether the dynamical synergies could potentially serve as candidates for feedforward and feedback mechanisms. Ten right-handed subjects were recruited to grasp and hold mass-varied objects. The contact forces during this multidigit prehension were recorded using an instrumented grip glove. The dynamical synergies were derived using principal component analysis (PCA). The contact force patterns during the grasps were reconstructed using the first few synergies. The significance of the dynamical synergies and the current challenges and possible applications of the dynamical synergies were discussed along with the integration of the dynamical synergies into prosthetics and exoskeletons that can possibly enable near-natural control. This research presents dynamical synergies observed in contact forces during hand grasps. These dynamical synergies could help in improving feedforward force control and sensory feedback in hand prosthetics and exoskeletons.

First Demonstration of Functional Task Performance Using a Sonomyographic Prosthesis: A Case Study.

Ultrasound-based sensing of muscle deformation, known as sonomyography, has shown promise for accurately classifying the intended hand grasps of individuals with upper limb loss in offline settings. Building upon this previous work, we present the first demonstration of real-time prosthetic hand control using sonomyography to perform functional tasks. An individual with congenital bilateral limb absence was fitted with sockets containing a low-profile ultrasound transducer placed over forearm muscle tissue in the residual limbs. A classifier was trained using linear discriminant analysis to recognize ultrasound images of muscle contractions for three discrete hand configurations (rest, tripod grasp, index finger point) under a variety of arm positions designed to cover the reachable workspace. A prosthetic hand mounted to the socket was then controlled using this classifier. Using this real-time sonomyographic control, the participant was able to complete three functional tasks that required selecting different hand grasps in order to grasp and move one-inch wooden blocks over a broad range of arm positions. Additionally, these tests were successfully repeated without retraining the classifier across 3 hours of prosthesis use and following simulated donning and doffing of the socket. This study supports the feasibility of using sonomyography to control upper limb prostheses in real-world applications.

CNN-Based Hand Grasping Prediction and Control via Postural Synergy Basis Extraction.

The prediction of hand grasping and control of a robotic manipulator for hand activity training is of great significance to assist stroke patients to recover their biomechanical functions. However, the human hand and the figure joints have multiple degrees of freedom; therefore, it is complex to process and analyze all the collected data in hand modeling. To simplify the description of grasping activities, it is necessary to extract and decompose the principal components of hand actions. In this paper, the relationships among hand grasping actions are explored by extracting the postural synergy basis of hand motions, aiming to simplify hand grasping actions and reduce the data dimensions for robot control. A convolutional neural network (CNN)-based hand activity prediction method is proposed, which utilizes motion data to estimate hand grasping actions. The prediction results were then used to control a stimulated robotic model according to the extracted postural synergy basis. The prediction accuracy of the proposed method for the selected hand motions could reach up to 94% and the robotic model could be operated naturally based on patient’s movement intention, so as to complete grasping tasks and achieve active rehabilitation.

Biomechanical Analysis of the Effect of the Finger Extensor Mechanism on Hand Grasping Performance.

Quantifying the effect of routing and topology of the inter-connected finger extensor mechanism on hand grasping performances is a long-standing research problem for the better clinical diagnosis, surgical planning and biomimetic hand development. However, it is technically demanding to measure the hand performance parameters such as the contact forces and contact area during hand manipulation. It is also difficult to replicate human hand performance through the physical hand model due to its sophisticated musculotendinous structure. In this study, an experimental validated subject-specific finite element (FE) human hand model was used for the first time to quantify the influence of different tendon topologies and material properties on hand grasping quality. It is found that the grasping quality is reduced by 15.94% and 8.54% if there are no extensor hood and lateral band respectively, and the former plays a more important role in transmitting forces and maintaining grasping qualities than the latter. Excluding extensor hood in the topology causes more reductions in hand contact pressure and contact area than omitting lateral band. 7.5% of the grasping quality is lost due to a softened tendon with half of its original Young's Modulus. Hardened extensor tendon does increase the grasping quality, but the enhancing effect tends to level off once the tendon Young's Modulus is increased by more than 50%. These results prove that the lateral band and extensor hood are critical components for maintaining grasping quality. The dexterity and grasping quality of robotic and prosthetic hands could be improved by integrating these two components. There is also no need to use very stiff tendon material as it won't help to effectively enhance the grasping quality.

Designing for Arthritis and Activities of Daily Living: A Taxonomic Review of Hand Grasp Techniques in a Domestic Context

Designing for Arthritis and Activities of Daily Living: A Taxonomic Review of Hand Grasp Techniques in a Domestic Context

Developmental Relationships between Alternating Bimanual Hand Grasping and Self-recognition in Early Childhood: On the Significance of the Model Presentation and Gender Differences

Developmental Relationships between Alternating Bimanual Hand Grasping and Self-recognition in Early Childhood: On the Significance of the Model Presentation and Gender Differences

Quantitative Investigation of Hand Grasp Functionality: Hand Joint Motion Correlation, Independence, and Grasping Behavior.

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.

Quantitative Investigation of Hand Grasp Functionality: Thumb Grasping Behavior Adapting to Different Object Shapes, Sizes, and Relative Positions.

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.

Generalizability of Hand Kinematic Synergies derived using Independent Component Analysis.

In this paper, hand synergies were derived using independent component analysis (ICA) and compared against synergies derived from our previous methods using principal component analysis (PCA). For ICA, we used two algorithms - Infomax and entropy bound minimization (EBM). For all the methods, the synergies were extracted from rapid hand grasps. The extracted synergies were then tested for generalizability in reconstructing natural hand grasps and American Sign Language (ASL) postures that were different from rapid grasps. The results indicate that the synergies derived from ICA were able to generalize only marginally better when compared to those from PCA. Among the two ICA methods, Infomax performed slightly better in yielding lower reconstruction error while EBM performed better in sparse selection of synergies. The implications and future scope were discussed.

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

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.

Grasp Behavior Analysis Using Muscle and Postural Hand Synergies for Smartphones

Smartphones are currently among the most common handheld devices. Previous studies on such handheld touchscreen devices focused on thumb operations or reach zones by measuring individual muscle or joint angles. However, they were limited to thumb operations and did not consider grasping. In this study, we investigated the grasp types of touchscreen devices and other objects included in an existing grasp taxonomy. To this end, principal component analysis and latent profile analysis clustering were used for extracting and grouping muscle and postural synergies. Fourteen healthy subjects performed up to 15 hand grasps, including that with a smartphone. Electromyography (EMG) data were measured on six muscles in the forearm and the hand, and joint angles were measured for 22 joints in the hand. The first two muscle synergies from the EMG data and the first three postural synergies from the kinematic data were found to account for over 60% of the overall grasping. In terms of the synergies, the grasp for handheld touchscreen devices showed unique characteristics in terms of muscle and postural synergies compared to other objects. The obtained results may aid in understanding of grasping behaviors for handheld touchscreen devices in various applications.

The Neural Representation of Force across Grasp Types in Motor Cortex of Humans with Tetraplegia.

Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two human participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp. Demixed principal component analysis (dPCA) was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: (1) force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); (2) grasp type affected force representation within multiunit neural features and offline force classification accuracy; and (3) grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type.

A new framework for classification of multi-category hand grasps using EMG signals

Electromyogram (EMG) signals have had a great impact on many applications, including prosthetic or rehabilitation devices, human-machine interactions, clinical and biomedical areas. In recent years, EMG signals have been used as a popular tool to generate device control commands for rehabilitation equipment, such as robotic prostheses. This intention of this study was to design an EMG signal-based expert model for hand-grasp classification that could enhance prosthetic hand movements for people with disabilities. The study, thus, aimed to introduce an innovative framework for recognising hand movements using EMG signals. The proposed framework consists of logarithmic spectrogram-based graph signal (LSGS), AdaBoost k-means (AB-k-means) and an ensemble of feature selection (FS) techniques. First, the LSGS model is applied to analyse and extract the desirable features from EMG signals. Then, to assist in selecting the most influential features, an ensemble FS is added to the design. Finally, in the classification phase, a novel classification model, named AB-k-means, is developed to classify the selected EMG features into different hand grasps. The proposed hybrid model, LSGS-based scheme is evaluated with a publicly available EMG hand movement dataset from the UCI repository. Using the same dataset, the LSGS-AB-k-means design model is also benchmarked with several classifications including the state-of-the-art algorithms. The results demonstrate that the proposed model achieves a high classification rate and demonstrates superior results compared to several previous research works. This study, therefore, establishes that the proposed model can accurately classify EMG hand grasps and can be implemented as a control unit with low cost and a high classification rate.

A Modular Brain-Computer Interface that Restores Hand Grasp Designed for Independent, At-Home Use

A Modular Brain-Computer Interface that Restores Hand Grasp Designed for Independent, At-Home Use

Hand gesture recognition using surface electromyography.

Surface electromyography has become one of the popular methods for recognizing hand gestures. In this paper, the performance of four classification methods on sEMG signals have been investigated. These methods are developed by combinations of two feature extraction methods, including Mean Absolute Value and Short-Time Fourier Transform, and two classifiers, including Support Vector Machine and Convolutional Neural Network. These classification methods achieved an accuracy over 97 % on the NinaPro dataset 1. In addition, a new dataset, which includes the Activities of Daily Living, was proposed and an accuracy over 98 % was obtained by applying the presented classification methods.This methodology can provide the basis for a robust quantitative technique to evaluate hand grasps of stroke patients in performing activities of daily living that in turn can lead to a more efficient rehabilitation regimen.

Towards Clustering Hand Grasps of Individuals with Spinal Cord Injury in Egocentric Video.

Cervical spinal cord injury (cSCI) can cause paralysis and impair hand function. Existing assessments in clinical settings do not reflect an individual's performance in their daily environment. Videos from wearable cameras (egocentric video) provide a novel avenue to analyze hand function in non-clinical settings. Due to the large amounts of video data generated by this approach, automated analysis methods are necessary. We propose to employ an unsupervised learning process to produce a summary of the grasping strategies used in an egocentric video. To this end, an approach was developed consisting of hand detection, pose estimation, and clustering algorithms. The performance of the method was examined with external evaluation indicators and internal evaluation indicators for an uninjured and injured participant, respectively. The results demonstrated that a Gaussian mixture model obtained the highest accuracy in terms of the maximum match, 0.63, and the Rand index, 0.26, for the uninjured participant, and a silhouette score of 0.13 for the injured participant.

Work ability and percentage of hours worked related to limitations in patients with upper extremity musculoskeletal disorders: a cross-sectional cohort study

BackgroundThe aim of this study was to assess the relationship between self-reported work ability and hours worked at the current time in Upper Extremity Musculoskeletal Disorders (UEMSD) patients. To further investigate this relationship, the association of work ability and working hours with several limitations in daily and working life were explored.MethodsIn this cross-sectional cohort study, a questionnaire was sent out to members of the UEMSD patient organisation, containing self-reported work ability, questions on working hours and limitations in work due to UEMSD. Limitations were measured with the Disabilities of Arm Shoulder and Hand questionnaire, ShortForm-36 subscales, and common hand grasps or grips. Work ability was measured with the work ability score, while worked hours were operationalised as the percentage of hours worked compared to fulltime. The correlation between worked hours and work ability was tested with the Pearson correlation coefficient. Variance in work ability and the hours worked were explained by limitations and assessed with two linear regression analyses.ResultsBased on data of 794 respondents a moderate correlation was found between work ability and worked hours r = 0.46; 95% CI [0.40, 0.53]. Models including limitations explained 52 and 21% of total variance in work ability and worked hours, respectively. Variance in both can be explained by the degree of difficulties performing daily activities at work, limitations in daily activities as a consequence of health issues and the ability to perform a precision grip. Additionally, work ability can be explained by limitations at work and other daily activities due to physical health issues, while the percentage of hours can additionally be explained by the ability to grasp a large object with one hand, the ability to use a keyboard, and the subject’s gender.ConclusionsThe number of worked hours does not fully match the work ability. Although they share three predictors, work ability and worked hours seem to be based on different aspects. Compared to work hours, work ability is more strongly related to limitations in daily activities and work. Taking self-reported work ability into account can improve the fit between work limitations and work hours.

Tactile Feedback in Closed-Loop Control of Myoelectric Hand Grasping: Conveying Information of Multiple Sensors Simultaneously via a Single Feedback Channel.

The appropriate sensory information feedback is important for the success of an object grasping and manipulation task. In many scenarios, the need arises for multiple feedback information to be conveyed to a prosthetic hand user simultaneously. The multiple sets of information may either (1) directly contribute to the performance of the grasping or object manipulation task, such as the feedback of the grasping force, or (2) simply form additional independent set(s) of information. In this paper, the efficacy of simultaneously conveying two independent sets of sensor information (the grasp force and a secondary set of information) through a single channel of feedback stimulation (vibrotactile via bone conduction) to the human user in a prosthetic application is investigated. The performance of the grasping task is not dependent to the second set of information in this study. Subject performance in two tasks: regulating the grasp force and identifying the secondary information, were evaluated when provided with either one corresponding information or both sets of feedback information. Visual feedback is involved in the training stage. The proposed approach is validated on human-subject experiments using a vibrotactile transducer worn on the elbow bony landmark (to realize a non-invasive bone conduction interface) carried out in a virtual reality environment to perform a closed-loop object grasping task. The experimental results show that the performance of the human subjects on either task, whilst perceiving two sets of sensory information, is not inferior to that when receiving only one set of corresponding sensory information, demonstrating the potential of conveying a second set of information through a bone conduction interface in an upper limb prosthetic task.

Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures.

The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class. Four state-of-the-art classifiers namely feed forward artificial neural network, cascaded feed forward artificial neural network, deep learning neural network and support vector machine are selected for this work to classify the finger movements and hand grasps using the extracted time-domain features. The experimental results show that the artificial neural network classifier is stabilized at 6 epochs for finger movement dataset and at 4 epochs for hand grasps dataset with low mean square error. However, the support vector machine classifier attains the maximum accuracy of 97.3077% for finger movement dataset and 98.875% for hand grasp dataset which is significantly greater than feed forward artificial neural network, cascaded feed forward artificial neural network and deep learning neural network classifiers.