Hand Grasps Research Articles

Pressure Sensors for Evaluating Hand Grasp and Pinch.

This study addresses the need for highly sensitive tools to evaluate hand strength, particularly grasp and pinch strength, which are vital for diagnosing and rehabilitating conditions affecting hand function. Current devices like the Jamar dynamometer and Martin Vigorimeter, although reliable, fail to measure extremely low force or pressure values required for individuals with severe hand impairments. This research introduces a novel device, a modified Martin Vigorimeter, utilizing an ultra-soft latex chamber and differential pressure measurement to detect minute pressure changes, thus significantly enhancing sensitivity. The device offers a cost-effective solution, making advanced hand strength evaluation more accessible for clinical and research applications. Future research should validate its accuracy across diverse populations and settings, exploring its broader implications for hand rehabilitation and occupational health.

Withdraw-to-eat movements of the Platyrrhine Sapajus libidinosus to the changing affordance of tubers with eating

The evolution of hand ability in primates is staged, with the visual control of hand shaping likely originating in Platyrrhine primates, members of a monophyletic primate suborder whose stem group diverged from catarrhines about 40 million years ago. Platyrrhine hand shaping may have evolved in relation to visually mediated inhand food manipulation and hand withdraw to the mouth for eating movements. The present study examines this hypothesis in the Platyrrhine Sapajus libidinosus, the bearded capuchin monkey. These monkeys include the tuber raiz de macaco, or monkey’s root, in their diet, a food item that poses challenges in handling, peeling and eating. Analysis of video recordings show that the capuchins used vision to render the tuber into fibers and roots that could be chewed and ingested. Visual engagement was initiated as each rendering/eating sequence began and visual disengage, with a blink and head raise, occurred just before a tuber reached the mouth. Two-handed withdraws mainly brought intact tubers to the incisors for peeling and shredding and one-handed withdraws mainly brought tuber fibers and roots that protrude from the hand to the premolars for chewing. The capuchins made use of novel power hand grasps, featuring the digit tips pressing food items against the interdigit pads and palm. They displayed individual but not group preference in pulling and holding the tubers with one hand and manipulating the tuber at the mouth with the other hand. The results are discussed in relation to the idea that the evolution of the visual and cognitive skills associated with inhand food handling to the changing affordance of food facilitates its management by the mouth. This visuomanual skill could be applied to other functions including tool use.

Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification

The kinematics of the human hand exhibit complex and diverse characteristics unique to each individual. Various techniques such as vision-based, ultrasonic-based, and data-glove-based approaches have been employed to analyze human hand movements. However, a critical challenge remains in efficiently analyzing and classifying hand grasp types based on time-series kinematic data. In this paper, we propose a novel sparse coding feature extraction technique based on dictionary learning to address this challenge. Our method enhances model accuracy, reduces training time, and minimizes overfitting risk. We benchmarked our approach against principal component analysis (PCA) and sparse coding based on a Gaussian random dictionary. Our results demonstrate a significant improvement in classification accuracy: achieving 81.78% with our method compared to 31.43% for PCA and 77.27% for the Gaussian random dictionary. Furthermore, our technique outperforms in terms of macro-average F1-score and average area under the curve (AUC) while also significantly reducing the number of features required.

HAND GRASPS RECOGNITION USING SINGLE-CHANNEL TRANSIENT-STATE SURFACE ELECTROMYOGRAPHY

The need to perform large hand-grasping strategies in daily life has promoted the development of robotic hands and prosthetic hands that mimic natural ones. This work presents a system for the detection of 10 basic hand grasps in healthy subjects based on single-channel transient-state Surface Electromyography (sEMG) signals. The studied grasps are large diameter, ring, knife index finger, writing tripod, power sphere, precision sphere, prismatic pinch, lateral, extension type, and parallel extension grasp. Signals were collected from 10 healthy volunteers using three sEMG electrodes connected to an Electromyography (EMG) shield using MATLAB as data acquisition software. The signals were classified using three different classifiers, i.e. [Formula: see text]-Nearest Neighbors ([Formula: see text]-NN), Diagonal Linear Discriminant Analysis (dl-DA), and Random Forest (RF). The used features were mean absolute value, integrated absolute value, variance, root mean square value, waveform length, Willison amplitude, slope sign change, zero crossing, autoregressive coefficients, and mean frequency. The results show that good classification accuracy is achieved when classifying a reduced number of grasps using a single-channel transient-state sEMG. The average accuracy for the different classifiers varied from 41.5% to 53.3% when classifying 10 grasps and from 78.4% to 91.9% when classifying five grasps using two features as a trainer. It is found that transient-state sEMG signals are efficient in the detection of different hand grasps, reducing the time to generate control signals and the number of channels.

A packing problem approach to grasping with a two-finger gripper with application in forest harvesting

Robotic Hand Grasping is a challenging task that is studied by different researchers, with contributions made to topics such as grasping quality measuring, synthesis, and mathematical models. However, these works usually consider grasping of only one object at time, which is not applicable when the hand must carry multiple objects. Multiple objects grasping problem can be approached as a packing problem, which consists in filling a container with objects of a determined geometry, using the space available in the most efficient way. The aim of this work is to obtain a design methodology for a two-finger gripper that optimizes the grasping capacity of slender cylindrical multiple objects, applying packing problem concepts. This case is of practical importance in grapple claws used in forest harvesting for picking logs. A mathematical model is presented that allows to obtain an optimized geometric configuration of the grapple claw for maximum grasping capacity and packing density for given numbers and sizes of logs. This model is analytically solved for the maximum radius of a set of logs that can be grasped. Alternatively, for a number of logs with a given diameter, the geometric dimensions of an optimized grapple claw can be obtained. The solutions are evaluated using packing density and Active Force Closure (AFC) as quality metrics, to show the benefits of adding them as design criteria in the design multiple objects grapple claws.

Hand Grasp Classification in Egocentric Video After Cervical Spinal Cord Injury.

The hand function of individuals with spinal cord injury (SCI) plays a crucial role in their independence and quality of life. Wearable cameras provide an opportunity to analyze hand function in non-clinical environments. Summarizing the video data and documenting dominant hand grasps and their usage frequency would allow clinicians to quickly and precisely analyze hand function. We introduce a new hierarchical model to summarize the grasping strategies of individuals with SCI at home. The first level classifies hand-object interaction using hand-object contact estimation. We developed a new deep model in the second level by incorporating hand postures and hand-object contact points using contextual information. In the first hierarchical level, a mean of 86% ±1.0% was achieved among 17 participants. At the grasp classification level, the mean average accuracy was 66.2 ±12.9%. The grasp classifier's performance was highly dependent on the participants, with accuracy varying from 41% to 78%. The highest grasp classification accuracy was obtained for the model with smoothed grasp classification, using a ResNet50 backbone architecture for the contextual head and a temporal pose head. We introduce a novel algorithm that, for the first time, enables clinicians to analyze the quantity and type of hand movements in individuals with spinal cord injury at home. The algorithm can find applications in other research fields, including robotics, and most neurological diseases that affect hand function, notably, stroke and Parkinson's.

The Platyrrhine Primate Cebus imitator Uses Gaze to Manipulate and Withdraw Food to the Mouth

Orienting a food item held in the hand to withdraw and optimally place it in the mouth for eating (withdraw-to-eat) is mediated by vision in catarrhine anthropoids and by nonvisual strategies in strepsirrhine primates. The present study asks whether vision contributes to the withdraw-to-eat movements in a platyrrhine anthropoid Cebus imitator, a member of a monophyletic primate suborder whose stem group diverged from catarrhines about 40 million years ago. Cebus imitator’s gaze and hand use for foraging for fruit is examined in its fine branch niche, the terminal branches of trees. Video of reach, grasp and withdraw-to-eat movements with associated gaze were examined frame-by-frame to assess food manipulation and its sensory control. Cebus imitator uses vision and touch to reach for and grasp food items with precision and whole hand grasps. They use vision to orient food items held in-hand into a precision grip and their withdraw-to-eat is assisted with a vertically oriented hand. The conjoint use of vision, a precision grasp, and hand posture and a central representation of object control likely originated in stem anthropoids and was derived from the staged evolution of the visual manipulation of food and other objects.

SEMAR (An Analysis of God's Form of Togetherness with Javanese Society)

This paper aims to find out by analytically describing Semar as a form of God's togetherness with Javanese society. In order to reach the desired goal, one question was asked: how is Semar a form of God's togetherness with the Javanese people? The method, or way to obtain Semar as a form of God's togetherness with Javanese society, is carried out using hermeneutics, semiotics, dialectics and referentics in accordance with existing forms, where Semar has words or language, symbols and behavior. Semar's approach as a form of togetherness between God and Javanese society is a makrifat philosophy, namely a human understanding of God. The result of this writing is that Semar is a form of God's togetherness with the Javanese people, his surah and sirah always chant: 1. God, 2. goodness, and 3. humans. Chanting God, in Surahs: 1). the gender is neither male nor female, 2). parents, 3). around him. In sirah: 1). namur kawula, 2). calm, 3). if the weapon farts. Chanting goodness, in the surah: 1). hands are accusing, 2). left hand grasps. In sirah: 1). namur kawula, 2). memayu hayuning Bawana, 3). patience, 4). wearing a pole. Chanting humans, a. in the surah: 1). with a white tip pointing upwards, 2). wearing a Kawung batik sarong, 3). wearing earrings, necklaces, bracelets and anklets. In sirah: 1). If you start saying: lae-lae mbegeg ugeg-ugeg sadilita heml-heml.

Student Competition (Technology Innovation) ID 1970388

Background The evaluation of hand function after spinal cord injury (SCI) is conducted in clinical settings, which may not accurately reflect hand function in the real world, thereby limiting the efficacy assessment of new treatments. Wearable cameras, also known as egocentric video, are a novel method to evaluate hand function in non-clinical environments. Nonetheless, manual processing of vast quantities of complex video data is difficult, highlighting the need for automated data analysis. The objective of this study was to automatically identify distinct hand postures in egocentric video using unsupervised machine learning. Methods Seventeen participants with cervical SCI recorded activities of daily living in a home simulation laboratory. A hand pose estimation algorithm was applied on detected hands to determine 2D joint locations, which were lifted to 3D coordinates. The resulting hand posture information was subjected to a number of clustering techniques. Hand grasps were manually labelled into four categories for evaluation purposes: power, precision, intermediate, and non-prehensile. Results K-Means clustering consistently exhibited the highest Silhouette score, which reflects the presence of discrete clusters in the data. When comparing with manual annotations, Spectral Clustering applied to a feature space consisting of 2D pose estimation with confidence scores yield the best performance as quantified by maximum match (0.48), Fowlkes-Mallows score (0.46), and normalized mutual information (0.22). Conclusions This is the first attempt to develop an unsupervised, data-driven hand taxonomy for individuals with SCI using wearable technology. The findings suggest that the method is capable of grouping similar hand grasps.

Hand Grasp Motion Intention Recognition Based on High-Density Electromyography in Chronic Stroke Patients.

Stroke is a debilitating condition that leads to a loss of motor function, inability to perform daily life activities, and ultimately worsening quality of life. Robot-based rehabilitation is a more effective method than conventional rehabilitation but needs to accurately recognize the patient's intention so that the robot can assist the patient's voluntary motion. This study focuses on recognizing hand grasp motion intention using high-density electromyography (HD-EMG) in patients with chronic stroke. The study was conducted with three chronic stroke patients and involved recording HD-EMG signals from the muscles involved in hand grasp motions. The adaptive onset detection algorithm was used to accurately identify the start of hand grasp motions accurately, and a convolutional neural network (CNN) was trained to classify the HD-EMG signals into one of four grasping motions. The average true positive and false positive rates of the grasp onset detection on three subjects were 91.6% and 9.8%, respectively, and the trained CNN classified the grasping motion with an average accuracy of 76.3%. The results showed that using HD-EMG can provide accurate hand grasp motion intention recognition in chronic stroke patients, highlighting the potential for effective robot-based rehabilitation.

Hand Grasp Pose Prediction Based on Motion Prior Field.

Shared control of bionic robot hands has recently attracted much research attention. However, few studies have performed predictive analysis for grasp pose, which is vital for the pre-shape planning of robotic wrists and hands. Aiming at shared control of dexterous hand grasp planning, this paper proposes a framework for grasp pose prediction based on the motion prior field. To map the hand-object pose to the final grasp pose, an object-centered motion prior field is established to learn the prediction model. The results of motion capture reconstruction show that, with the input of a 7-dimensional pose and cluster manifolds of dimension 100, the model performs best in terms of prediction accuracy (90.2%) and error distance (1.27 cm) in the sequence. The model makes correct predictions in the first 50% of the sequence during hand approach to the object. The outcomes of this study enable prediction of the grasp pose in advance as the hand approaches the object, which is very important for enabling the shared control of bionic and prosthetic hands.

FunctionalGrasp: Learning Functional Grasp for Robots via Semantic Hand-Object Representation

Successful grasp is an important and long-standing issue for robots to interact with the real world. Most recent studies have devoted more attention to stable grasp rather than functional grasp, which cannot guarantee task-oriented postgrasp manipulation. To achieve human- like functional grasp, a semantic representation of functional hand-object interaction is introduced without labeling 3D hand poses, and a novel coarse-to-fine grasp generation network is designed to model this hand-object interaction. First, a coarse grasp is generated by combining the global hand pose and hand grasp type. Then, the fine pose will be optimized by guiding each finger to focus on the corresponding functional region of the object. Experimental results demonstrate the effectiveness of our method in achieving functional grasps for dexterous hands in the absence of high-DoF grasp poses annotation of the hand. The project website is: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhangyb1008/FunctionalGrasp</uri> .

Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models.

Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several other variabilities. We hypothesise that this challenge can be addressed by introducing uncertainty-aware models because the rejection of uncertain movements has previously been demonstrated to improve the reliability of sEMG-based hand gesture recognition. With a particular focus on a very challenging benchmark dataset (NinaPro Database 6), we propose a novel end-to-end uncertainty-aware model, an evidential convolutional neural network (ECNN), which can generate multidimensional uncertainties, including vacuity and dissonance, for robust long-term hand grasp recognition. To avoid heuristically determining the optimal rejection threshold, we examine the performance of misclassification detection in the validation set. Extensive comparisons of accuracy under the non-rejection and rejection scheme are conducted when classifying 8 hand grasps (including rest) over 8 subjects across proposed models. The proposed ECNN is shown to improve recognition performance, achieving an accuracy of 51.44% without the rejection option and 83.51% under the rejection scheme with multidimensional uncertainties, significantly improving the current state-of-the-art (SoA) by 3.71% and 13.88%, respectively. Furthermore, its overall rejection-capable recognition accuracy remains stable with only a small accuracy degradation after the last data acquisition over 3 days. These results show the potential design of a reliable classifier that yields accurate and robust recognition performance.

Decoding of multiple wrist and hand movements using a transient EMG classifier.

The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the transient EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of ~96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of ~89%. Importantly, for each amputee, it produced at least one acceptable combination of wrist-hand movements (i.e., with accuracy > 85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to ~80%), they were not as good with amputees (accuracy up to ~35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.

Simplified Configuration Design of Anthropomorphic Hand Imitating Specific Human Hand Grasps

How to design an anthropomorphic hand imitating specific human hand grasps with as few actuators as possible is still a challenge. This letter presents a method for obtaining a simplified configuration of anthropomorphic hand imitating specific human hand grasps based on the motion analyses of the human hand. A participation matrix which characterizes a human hand grasp on joint motion level is constructed according to the motion participation of each finger joint. By adding all participation matrices of expected human hand grasps together a total participation matrix can be derived, and through mathematical processing a simplified anthropomorphic hand configuration can be obtained. Following the proposed method, a simplified anthropomorphic hand configuration that imitates six basic human hand grasps was obtained. A series of grasp experiments with the anthropomorphic hand prototype were conducted to validate the grasping capability as well as the proposed simplified configuration design method. This method can help to obtain a reasonably simplified configuration of an anthropomorphic hand when expected human hand grasps are definite.

Dynamic Modelling of Hand Grasping and Wrist Exoskeleton: An EMG-based Approach

Dynamic Modelling of Hand Grasping and Wrist Exoskeleton: An EMG-based Approach

Biomechanical analysis of the effect of finger joint configuration on hand grasping performance: rigid vs flexible.

Human finger joints are conventionally simplified as rigid joints in robotic hand design and biomechanical hand modelling, due to their anatomic and morphologic complexity. However, our understanding of the effect of the finger joint configuration on the resulting hand performance is still primitive. In this study, we systematically investigate the grasping performance of the hands with the conventional rigid joints and the biomechanical flexible joints based on a computational human hand model. The measured muscle electromyography (EMG) and hand kinematic data during grasping are used as inputs for the grasping simulations. The results show that the rigid joint configuration currently used in most robotic hands leads to large reductions in hand contact force, contact pressure and contact area, compared to the flexible joint configuration. The grasping quality could be reduced up to 40% and 36% by the rigid joint configuration in terms of algebraic properties of grasping matrix and finger force limit respectively. Further investigation reveals that these reductions are caused by the weak rotational stiffness of the rigid joint configuration. This study implies that robotic/prosthetic hand performance could be improved by exploiting flexible finger joint design. Hand contact parameters and grasping performance may be underestimated by the rigid joint simplification in human hand modelling.

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller.

Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.

SEMG-Based Minimally Supervised Regression Using Soft-DTW Neural Networks for Robot Hand Grasping Control

One of the major challenges in robotics consists in developing successful control strategies for robotic grasping devices. In this scenario, one of the most interesting approaches regards the exploitation of surface electromyography(sEMG). In this work, we propose a novel sEMG-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimally supervised</i> regression approach capable of performing nonlinear fitting without the necessity for point-by-point training data labelling. The proposed method exploits a differentiable version of the Dynamic Time Warping (DTW) similarity – referred to as soft-DTW divergence – as loss function for a flexible neural network architecture. This is a different paradigm with respect to state-of-the-art approaches in which sEMG-based control of robot hands is mainly realized using supervised or unsupervised machine learning based regression. An experimental session was carried out involving 10 healthy subjects in an offline experiment for systematic and statistical evaluations, and an online experiment for the evaluation of the control of a robot hand. The reported results demonstrate that the proposed soft-DTW neural network can be trained by means of a labelling that does not require to be temporally aligned with the sEMG training dataset, while reporting performances comparable with a standard mean square error(MSE)-based neural network. Also, the subjects were able to successfully control a robot hand for grasping motions and tasks with error levels comparable to state-of-the-art regression approaches.

Characterizing Pediatric Hand Grasps During Activities of Daily Living to Inform Robotic Rehabilitation and Assistive Technologies.

Hand function plays a critical role in how we interact with our physical environment. Hand motor impairments in children can compromise many facets of their daily life including physical independence and social interactions. For adults, there has been an emergence of mechatronic rehabilitation systems to improve hand mobility, strength, and dexterity; assistive technologies such as exoskeletons to drive impaired digits; and highly dexterous upper limb prostheses. Although similar devices are on the clinical horizon for children, childhood play, motor development, and daily activities mean they use their hands in fundamentally different ways than adults. It is imperative that devices for this population facilitate their unique needs; yet it is not completely known which hand movements may be of the highest priority during daily tasks or rehabilitation to best foster functional independence. Here, we evaluated and categorized the hand activity of two children in their home environments. Small wearable video cameras were attached to the children as they performed daily tasks and the video footage was analyzed to obtain the frequency and duration of their hand grasp movements. It was found that 7 common grasps accounted for 90% or greater of the children's hand activity in duration and frequency. This suggests, that like adults, a repertoire of common hand grasps may be prioritized by rehabilitative or assistive devices to ensure effective outcomes in performing daily activities.