Hand Configuration Research Articles

Numerical harmonic modelling of low wattage LED lamps based on parameter estimation algorithms

This paper presents a numerical harmonic modeling approach for 18 low-wattage Light-Emitting Diode (LED) lamps. The proposed methodology utilizes meta-heuristic algorithms, namely Multi-Stage Ant Colony Optimization (MSACO) and Harris Hawk Optimization (HHO), to estimate optimal parameters for the equivalent circuits. Additionally, the study examines eight different configurations of Electromagnetic Interference (EMI) Filter circuits, including their respective parameters, which function as front-end circuits for the Full Bridge Rectifier (FBR) of the LED drivers. Through extensive investigations, the primary objective is to establish generalized and accurate models for various EMI filter circuits that can be universally applied, with validation through comparison against experimental results. Under both MSACO and HHO, the lowest average total error ranges were observed in the LC-Filter and CL-Filter configurations, with errors ranging from 21.302 % and 22.769 %, respectively. On the other hand, the L-Filter and R-only configurations exhibited the highest average total error ranges, with errors of 42.376 % and 44.648 %, respectively. Notably, this paper introduces a non-intrusive, non-invasive method for estimating optimal parameters based solely on measurements obtained from the input terminals of the LED lamps.

La referencia lingüística y la decolonización de los nombres propios en las lenguas de señas

The subject of the proper name is the subject of reference. Proper names are paradigmatic examples because they refer to unique entities in the world (uniqueness). In sign languages, there are gestural expressions that identify people in deaf communities and are distinguished from the spelling of their name given in Latin alphabet. Studies on this topic have described the signed proper name according to hand configurations and its other articulatory characteristics, according to some general principles about the significance of having the proper name in the community and how it functions. However, almost no mention is made of the basic concepts involved in the theoretical formulation of names and uniqueness. In this paper, we argue that descriptions of this topic are deliberately simplistic and reductionist, not because there is a desire to marginalize sign languages, but because we have been historically conditioned to act this way. From a decolonial critique and an anti-representationalist perspective of language, we show the controversies that shape the issue of proper names and propose an integrated view that advances in the deconstruction of polarizations and false dichotomies in the linguistic and identity discourse.

Furnace MILD combustion versus its open counterpart in hot coflow

The open jet flame in hot co-flow (JHC) has been frequently utilized for fundamental investigations of Moderate or Intense Low-oxygen Dilution (MILD) combustion due to its controllable conditions and relatively easy measurement capabilities. However, practical MILD combustion must take place within a combustor that is enclosed. Therefore, it is necessary to examine the similarity and disparity of combustion characteristics between two flame configurations. This issue is addressed currently. Specifically, we investigate the flow mixing, ignition, and combustion, as well as emission characteristics of non-premixed and premixed JHC and cylindrical furnace (FUR) flames at various values of the environmental temperature (Te) and central jet Reynolds number (Re). For the open non-premixed flames, we employ both previous single-tube JHC (SJHC) combustor and presently modified JHC (MJHC) one that uses the same nozzle configuration as the FUR burner. It is revealed that significant differences occur in flow, combustion and emission characteristics between the SJHC and FUR cases. On the other hand, both non-premixed and premixed MJHC configurations exhibit high similarity to the corresponding FUR cases in terms of upstream flow mixing and combustion features. Moreover, different CO and NOx emissions result from open JHC and close furnace flames due to different post-combustion configuration and residence time. Accordingly, future experiments on non-premixed MJHC and premixed JHC flames are highly recommended for better understanding practical MILD combustion.

Multimodal Robot Programming Interface Based On RGB-D Perception and Neural Scene Understanding Modules

In this paper, we propose a system for natural and intuitive interaction with the robot. Its purpose is to allow a person with no specialized knowledge and no training in robot programming to program a robotic arm.We utilize data from the RGB-D camera to segment the scene and detect objects. We also estimate the configuration of the operator's hand and the position of the visual marker to determine the intentions of the operator and the actions of the robot. To this end, we utilize trained neural networks and operations on the input point clouds. Also, voice commands are used to define or trigger the execution of the motion. Finally, we performed a set of experiments to show the properties of the proposed system.

A three degrees of freedom switchable impedance myoelectric prosthetic wrist

Wrist mobility contributes significantly to the execution of upper limb motor tasks. Despite this, current prosthetic wrists are far less advanced than other artificial joints. Typically, prosthetic wrists offer limited degrees of freedom, if any, which forces users to execute compensatory movements during task performance. This addition increases weight and complexity, two unwelcome factors in upper limb prostheses. This article presents the design of a 3-degree-of-freedom friction-lockable prosthetic wrist actuated by a single motor. The design features adaptable behavior when unlocked, promoting a gentle interaction with the environment, and enables users to adjust the hand configuration during pre-grasping phases. The proposed system was tested, combined with a hand prosthesis, and compared to a commercial rotational wrist during the execution of functional movements. Experiments involved nine able-bodied subjects and one prosthesis user. Participants also performed the experiments with their biological wrist (the intact wrist for the prosthesis user) as a control. Results showed that the lockable wrist was used actively 20% more often than the commercial solution without compromising users’ execution time. Interaction tests reveal that compensatory movements are reduced when using the proposed design, resulting in closer resemblance to the control wrist’s performance. The average satisfaction and usability scores were significantly higher for the proposed wrist, indicating its potential acceptance. Finally, the system was validated in a set of activities of daily living performed by the prosthesis user. The study contributes to the development of more intuitive and adaptable prostheses that can improve the quality of life of amputees.

Measuring tactile sensitivity and mixed-reality-assisted exercise for carpal tunnel syndrome by ultrasound mid-air haptics.

Carpal tunnel syndrome (CTS) is the most common nerve entrapment neuropathy, which causes numbness and pain in the thumb, the index and middle fingers and the radial side of the ring finger. Regular hand exercises may improve the symptoms and prevent carpal tunnel surgery. This study applied a novel ultrasonic stimulation method to test tactile sensitivity in CTS and also a mixed-reality-assisted (MR-assisted) exercise program which measured hand movements and provided haptic feedback for rehabilitation. Twenty patients with mild unilateral CTS took part in the experiments. A mid-air haptics device (Ultrahaptics STRATOS Explore) was used to apply amplitude-modulated ultrasound waves (carrier frequency: 40 kHz) onto the skin to create tactile stimulation mechanically. Participants performed a two-alternative forced-choice task for measuring tactile thresholds at 250-Hz modulation frequency. They were tested at the index fingers and the thenar eminences of both hands. Additionally, 15 CTS patients used an MR-assisted program to do hand exercises with haptic feedback. Exercise performance was assessed by calculating errors between target and actual hand configurations. System Usability Scale (SUS) was adopted to verify the practical usability of the program. Thresholds at the thenar eminences of the affected and healthy hands were not significantly different. While the thresholds at the healthy index fingers could be measured, those of the affected fingers were all higher than the stimulation level produced by the maximum output from the ultrasound device. In the exercise program, a significant positive correlation (ρ = 0.89, p < 0.001) was found between the performance scores and the SUS scores, which were above the criterion value established in the literature. The results show that thenar tactile sensitivity is not affected in mild CTS as expected from the palmar cutaneous branch of the median nerve (PCBm), but index finger threshold is likely to be higher. Overall, this study suggests that mid-air haptics, with certain improvements, may be used as a preliminary test in the clinical setting. Moreover, the device is promising to develop gamified rehabilitation programs and for the treatment follow-up of CTS.

Sign Language Motion Generation from Sign Characteristics.

This paper proposes, analyzes, and evaluates a deep learning architecture based on transformers for generating sign language motion from sign phonemes (represented using HamNoSys: a notation system developed at the University of Hamburg). The sign phonemes provide information about sign characteristics like hand configuration, localization, or movements. The use of sign phonemes is crucial for generating sign motion with a high level of details (including finger extensions and flexions). The transformer-based approach also includes a stop detection module for predicting the end of the generation process. Both aspects, motion generation and stop detection, are evaluated in detail. For motion generation, the dynamic time warping distance is used to compute the similarity between two landmarks sequences (ground truth and generated). The stop detection module is evaluated considering detection accuracy and ROC (receiver operating characteristic) curves. The paper proposes and evaluates several strategies to obtain the system configuration with the best performance. These strategies include different padding strategies, interpolation approaches, and data augmentation techniques. The best configuration of a fully automatic system obtains an average DTW distance per frame of 0.1057 and an area under the ROC curve (AUC) higher than 0.94.

A fist bump in a political meeting? The influence of social context on affordance selection

Historically, understanding human cognition such as action processing has been a challenging issue in cognitive neuropsychology and the more we know about cognition, the more we shape it as a complex, multi-determined phenomenon that is embedded in a social context. The present study aimed at understanding how the social context could influence affordance selection. We hypothesized that affordance selection would be modulated by social context and that a given hand configuration would be considered appropriate or not, as a function of the presence or absence of social context. Twenty-six healthy participants were asked to judge the appropriateness of three variants of 10 hand-object interactions based on photographs presented with or without a visual, social context. In our results, hand configurations were intrinsically acceptable or not, but this effect was modulated by the social context. A three-step model of the influence of social context on affordance selection was proposed, according to which selection depends on social norms, in the form of social knowledge and social context analysis.

Learning Human-to-Robot Dexterous Handovers for Anthropomorphic Hand

Human-robot interaction plays an important role in robots serving human production and life. Object handover between humans and robotics is one of the fundamental problems of human-robot interaction. The majority of current work uses parallel-jaw grippers as the end-effector device, which limits the ability of the robot to grab miscellaneous objects from human and manipulate them subsequently. In this paper, we present a framework for human-to-robot dexterous handover using an anthropomorphic hand. The framework takes images captured by two cameras to complete handover scene understanding, grasp configurations prediction, and handover execution. To enable the robot to generalize to diverse delivered objects with miscellaneous shapes and sizes, we propose an anthropomorphic hand grasp network (AHG-Net), an end-to-end network that takes the single-view point clouds of the object as input and predicts the suitable anthropomorphic hand configurations with 5 different grasp taxonomies. To train our model, we build a large-scale dataset with 1M hand grasp annotations from 5K single-view point clouds of 200 objects. We implement a handover system using a UR5 robot arm and HIT-DLR II anthropomorphic robot hand based on our presented framework, which can not only adapt to different human givers but generalize to diverse novel objects with various shapes and sizes. The generalizability, reliability, and robustness of our method are demonstrated on 15 different novel objects with arbitrary handover poses from frontal and lateral positions, a system ablation study, a grasp planner comparison, and a user study on 6 participants delivering 15 objects from two benchmark sets.

Agricultural intensity interacts with landscape arrangement in driving ecosystem services

Agricultural intensification has enhanced productivity but also led to enormous ecosystem service and biodiversity losses. Strategic spatial landscape design could counteract this trend, but, the scientific understanding of how ecosystem services respond to agricultural practices on one hand and land use composition and configuration on the other is not complete. This study aims to methodically explore how the effect of landscape layout settings on ecosystem services depends on the intensity of agricultural practices in their surroundings. Using the Netherlands as a case study, we used spatial regression models to analyze how agricultural management intensity affects the relationship between spatial composition and configuration metrics and ecosystem service indicators. We found that the effect of large shares of agricultural land use on species richness, pollination and landscape appreciation was increasingly negative with amplified intensity of agricultural practices. With higher agricultural intensity in the surroundings, the positive effects of well-connected natural vegetation on species richness were impaired. In contrast, the negative effects of high-intensity agriculture on pollination service were be buffered well through high shares of natural grassland vegetation. Water-quality related indicators were less affected by variation in spatial metrics and agricultural intensity. The main interactions between agricultural intensity and the spatial metrics were robust at varying scales. Our analysis suggests that both low- and high-intensity agriculture can have a place in future sustainable agricultural systems, provided they are integrated in the appropriate spatial layout. Explicitly addressing farming practices in connection to local spatial settings can improve both landscape planning and ecosystem service modelling.

Chord skill: learning optimized hand postures and bimanual coordination

This reaction time study tested the hypothesis that in the case of finger movements skilled motor control involves the execution of learned hand postures. After delineating hypothetical control mechanisms and their predictions an experiment is described involving 32 participants who practiced 6 chord responses. These responses involved the simultaneous depression of one, two or three keys with either four right-hand fingers or two fingers of both hands. After practicing each of these responses for 240 trials, the participants performed the practiced and also novel chords with the familiar and with the unfamiliar hand configuration of the other practice group. The results suggest that participants learned hand postures rather than spatial or explicit chord representations. Participants practicing with both hands also developed a bimanual coordination skill. Chord execution was most likely slowed by interference between adjacent fingers. This interference seemed eliminated with practice for some chords but not for others. Hence, the results support the notion that skilled control of finger movements is based on learned hand postures that even after practice may be slowed by interference between adjacent fingers.

Stronger neural response to canonical finger-number configurations in deaf compared to hearing adults revealed by FPVS-EEG.

The linguistic counting system of deaf signers consists of a manual counting format that uses specific structures for number words. Interestingly, the number signs from 1 to 4 in the Belgian sign languages correspond to the finger-montring habits of hearing individuals. These hand configurations could therefore be considered as signs (i.e., part of a language system) for deaf, while they would simply be number gestures (not linguistic) for hearing controls. A Fast Periodic Visual Stimulation design was used with electroencephalography recordings to examine whether these finger-number configurations are differently processed by the brain when they are signs (in deaf signers) as compared to when they are gestures (in hearing controls). Results showed that deaf signers show stronger discrimination responses to canonical finger-montring configurations compared to hearing controls. A second control experiment furthermore demonstrated that this finding was not merely due to the experience deaf signers have with the processing of hand configurations, as brain responses did not differ between groups for finger-counting configurations. Number configurations are therefore processed differently by deaf signers, but only when these configurations are part of their language system.

Robotic hand synergies for in-hand regrasping driven by object information

We develop a conditional generative model to represent dexterous grasp postures of a robotic hand and use it to generate in-hand regrasp trajectories. Our model learns to encode the robotic grasp postures into a low-dimensional space, called Synergy Space, while taking into account additional information about the object such as its size and its shape category. We then generate regrasp trajectories through linear interpolation in this low-dimensional space. The result is that the hand configuration moves from one grasp type to another while keeping the object stable in the hand. We show that our model achieves higher success rate on in-hand regrasping compared to previous methods used for synergy extraction, by taking advantage of the grasp size conditional variable.

Simultaneous Estimation of Hand Configurations and Finger Joint Angles Using Forearm Ultrasound

With the advancement in computing and robotics, it is necessary to develop fluent and intuitive methods for interacting with digital systems, augmented/virtual reality (AR/VR) interfaces, and physical robotic systems. Hand motion recognition is widely used to enable these interactions. Hand configuration classification and metacarpophalangeal (MCP) joint angle detection is important for a comprehensive reconstruction of hand motion. Surface electromyography (sEMG) and other technologies have been used for the detection of hand motions. Forearm ultrasound images provide a musculoskeletal visualization that can be used to understand hand motion. Recent work has shown that these ultrasound images can be classified using machine learning to estimate discrete hand configurations. Estimating both hand configuration and MCP joint angles based on forearm ultrasound has not been addressed in the literature. In this paper, we propose a convolutional neural network (CNN) based deep learning pipeline for predicting the MCP joint angles. The results for the hand configuration classification were compared by using different machine learning algorithms. Support vector classifier with different kernels, multi-layer perceptron, and the proposed CNN have been used to classify the ultrasound images into 11 hand configurations based on activities of daily living. Forearm ultrasound images were acquired from 6 subjects instructed to move their hands according to predefined hand configurations. Motion capture data was acquired to get the finger angles corresponding to the hand movements at different speeds (0.5 Hz, 1 Hz, & 2 Hz) for the index, middle, ring, and pinky fingers. Average classification accuracy of 82.7 ± 9.7% for the proposed CNN and over 80% for SVC for different kernels was observed on a subset of the dataset. An average RMSE of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.35^{\circ }\pm 1.3$ </tex-math></inline-formula> ° was obtained between the predicted and the true MCP joint angles. A low latency (6.25 - 9.1 Hz) pipeline has been proposed for estimating both MCP joint angles and hand configuration aimed at real-time control of human-machine interfaces.

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.

HAKA: HierArchical Knowledge Acquisition in a sign language tutor

Communication between people from different communities can sometimes be hampered by the lack of knowledge of each other's language. A large number of people needs to learn a language in order to ensure a fluid communication or want to do it just out of intellectual curiosity. To assist language learners' needs tutor tools have been developed. In this paper we present a tutor for learning the basic 42 hand configurations of the Spanish Sign Language, as well as more than one hundred of common words. This tutor registers the user image from an off-the-shelf webcam and challenges her to perform the hand configuration she chooses to practice. The system looks for the configuration, out of the 42 in its database, closest to the configuration performed by the user, and shows it to her, to help her to improve through knowledge of her errors in real time. The similarities between configurations are computed using Procrustes analysis. A table with the most frequent mistakes is also recorded and available to the user. The user may advance to choose a word and practice the hand configurations needed for that word. Sign languages have been historically neglected and deaf people still face important challenges in their daily activities. This research is a first step in the development of a Spanish Sign Language tutor and the tool is available as open source. A multidimensional scaling analysis of the clustering of the 42 hand configurations induced by Procrustes similarity is also presented.

An overall grasp evaluation function to assess and optimize prosthetic hands

In this study, we create an evolution function that assesses prosthetic hands in terms of their ability to grasp various objects like a human hand. It is required to evaluate the reachable workspace of prosthetic hands and grasp ability to assess it completely. The first one can be done by using forward kinematics. The Volume of the Grasp Wrench space, one of the grasp qualities indexes, is used to measure the second one. Randomization and grasp taxonomy are used to generalize the grasp quality to indicate the prosthetic hand functionality. Afterward, the created evaluation function is used to specify the importance of each finger and DOF. the results show that the most significant finger is the Thumb. The most crucial DOFs are the abduction movement of the Thumb's CMC joint and the Index's MCP joint. Finally, optimized hand configuration is created, considering limitations which are a number of actuators and stationery DOFs. Optimization has two steps. In the first step, the stationary joints and the joints that should be actuated independently from each other are chosen. In the second step, the remaining DOFs are specified to be coupled with which actuator using the Taguchi method.

Assessing an Automated Tool to Quantify Variation in Movement and Location: A Case Study of American Sign Language and Ghanaian Sign Language

Signs in sign languages have been mainly analyzed as composed of three formational elements: hand configuration, location, and movement. Researchers compare and contrast lexical differences and similarities among different signs and languages based on these formal elements. Such measurement requires extensive manual annotation of each feature based on a predefined process and can be time consuming because it is based on abstract representations that usually do not take into account the individual traits of different signers. This study showcases a newly developed tool named DistSign, used here to measure and visualize variation based on the wrist trajectory in the lexica of two sign languages, namely American Sign Language (ASL) and Ghanaian Sign Language (GSL), which are assumed to be historically related (Edward 2014). The tool utilizes the pretrained pose estimation framework OpenPose to track the body joints of different signers. Subsequently, the Dynamic Time Warping (DTW) algorithm, which measures the similarity between two temporal sequences, is used to quantify variation in the paths of the dominant hand’s wrist across signs. This enables one to efficiently identify cognates across languages, as well as false cognates. The results show that the DistSign tool can recognize cognates with a 60 percent accuracy, using a semiautomated method that utilizes the Levenshtein distance metric as a baseline.

Stretchy Electrochemical Harvesters for Binarized Self-Powered Strain Gauge-Based Static Motion Sensors.

The human monitoring system has motivated the search for new technology, leading to the development of a self-powered strain sensor. We report on the stretchable and soft stretchy electrochemical harvester (SECH) bilayer for a binarized self-powered strain gauge in dynamic and static motion. The active surface area participating in the electrochemical reaction was enhanced after stretching the SECH in the electrolyte, leading to an increase in the electrochemical double-layer capacitance. A change in the capacitance induced a change in the electrical potential of the bilayer, generating electrical energy. The SECH overcomes several challenges of the previous mechano-electrochemical harvester: The harvester had high elasticity (50%), which satisfied the required strain during human motion. The harvester was highly soft (modulus of 5.8 MPa), 103 times lower than that of the previous harvester. The SECH can be applied to a self-powered strain gauge, capable of measuring stationary deformation and low-speed motion. The SECH created a system to examine the configuration of the human body, as demonstrated by the human monitoring sensor from five independent SECH assembled on the hand. Furthermore, the sensing information was simplified through the binarized signal. It can be used to assess the hand configuration for hand signals and sign language.

A method of multimodal machine sign language translation for natural human-computer interaction

This paper aims to investigate the possibility of robustness enhancement as applied to an automatic system for isolated signs and sign languages recognition, through the use of the most informative spatiotemporal visual features. The authors present a method for the automatic recognition of gestural information, based on an integrated neural network model, which analyses spatiotemporal visual features: 2D and 3D distances between the palm and the face; the area of the hand and the face intersection; hand configuration; the gender and the age of signers. A 3DResNet-18-based neural network model for hand configuration data extraction was elaborated. Deepface software platform neural network models were embedded in the method in order to extract gender and age-related data. The proposed method was tested on the data from the multimodal corpus of sign language elements TheRuSLan, with the accuracy of 91.14 %. The results of this investigation not only improve the accuracy and robustness of machine sign language translation, but also enhance the naturalness of human-machine interaction in general. Besides that, the results have application in various fields of social services, medicine, education and robotics, as well as different public service centers.