Sharing of hand kinematic synergies across subjects in daily living activities

The motor system is hypothesised to use kinematic synergies to simplify hand control. Recent studies suggest that there is a large set of synergies, sparse in degrees of freedom, shared across subjects, so that each subject performs each action with a sparse combination of synergies. Identifying how synergies are shared across subjects can help in prostheses design, in clinical decision-making or in rehabilitation. Subject-specific synergies of healthy subjects performing a wide number of representative daily living activities were obtained through principal component analysis. To make synergies comparable between subjects and tasks, the hand kinematics data were scaled using normative range of motion data. To obtain synergies sparse in degrees of freedom a rotation method that maximizes the sum of the variances of the squared loadings was applied. Resulting synergies were clustered and each cluster was characterized by a core synergy and different indexes (prevalence, relevance for function and within-cluster synergy similarity), substantiating the sparsity of synergies. The first two core synergies represent finger flexion and were present in all subjects. The remaining core synergies represent coordination of the thumb joints, thumb-index joints, palmar arching or fingers adduction, and were employed by subjects in different combinations, thus revealing different subject-specific strategies

Level IV fugacity model by a continuous time control system.

A continuos time dynamic control system of the "Level IV Fugacity Model" is presented and applied to selected organic chemicals in evaluative environments system. In order to illustrate the numerical solution by this model a discretization is used to calculate the fugacity, concentration and fugacity settling time for a set of three insecticides applied in a hypothetical three compartmental environmental system. The model employs the fugacity concept and treats three bulk compartments: air, water and bottom sediment. Input to the model consists of a description of the environmental, the physical-chemical and reaction properties of the chemical, and emission rates. Expressions in matrix form are included for emissions, advections, reactions, and inter-phase transference. An algorithm to determine the values of the fugacities, concentrations and fugacity settling time is given

Estudios de caracterización cinemática de la mano sana en actividades de la vida diaria

El aumento de la esperanza de vida ha incrementado la prevalencia de enfermedades que afectan en gran medida a la capacidad manipulativa de la mano y por tanto al desarrollo de actividades de la vida diaria (AVD) necesarias para una vida independiente. Además, existe interés por evaluar más objetivamente la funcionalidad en el desarrollo de AVD, ya que en la práctica, los métodos de evaluación funcional de la mano son altamente subjetivos y poco orientados a las AVD. En esta comunicación se presentan diferentes estudios que desarrolla el grupo de Biomecánica y Ergonomía sobre caracterización cinemática del agarre humano en AVD. En un primer estudio se grabaron tareas representativas de los diferentes ámbitos de la vida personal (aseo, preparar comida, comer, limpieza y orden en casa, conducción, etc.) y se analizó la frecuencia de uso con cada mano de distintos tipos de agarre (de una clasificación con 9 categorías) en los diferentes ámbitos (Vergara et al. 2014). Posteriormente, con la ayuda de guantes instrumentados y goniómetros, en ambiente controlado de laboratorio, se han registrado los movimientos de las articulaciones de la mano y la muñeca en tareas representativas de las AVD, seleccionadas de acuerdo a la Clasificación Internacional de Funcionamiento, Discapacidad y Salud (CIF) de la OMS. Algunas se han realizado, además de con productos estándar, con productos adaptados comerciales. Se pretende además registrar a sujetos con algunas de las patologías de mano más frecuentes. El objetivo final de estos estudios es caracterizar la cinemática de la mano sana durante el desarrollo de AVD en base a patrones posturales, sus rangos y velocidades. Se establecerá una base de datos de ‘normalidad’ y se identificarán los parámetros cinemáticos que permitan evaluar más objetivamente la disfuncionalidad en sujetos lesionados o patológicos.A la Universitat Jaume I por la financiación del proyecto P1·1B2014-10 y al Ministerio de Economía y Competitividad y a la Comunidad Europea (fondos FEDER) por la financiación del proyecto DPI2014-52095-P

Stiffness map of the grasping contact areas of the human hand

The elasticity and damping of the soft tissues of the hand contribute to dexterity while grasping and also help to stabilise the objects in manipulation tasks. Although some previous works have studied the force-displacement response of the fingertips, the responses in all other regions of the hand that usually participate in grasping have not been analysed to date. In this work we performed experimental measurements in 20 subjects to obtain a stiffness map of the different grasping contact areas of the human hand. A force-displacement apparatus was used to simultaneously measure force and displacement at 39 different points on the hand at six levels of force ranging from 1 N to 6 N. A non-linear force-displacement response was found for all points, with stiffness increasing with the amount of force applied. Mean stiffness for the different points and force levels was within the range from 0.2 N/mm to 7.7 N/mm. However, the stiffness range and variation with level of force were found to be different from point to point. A total of 13 regions with similar stiffness behaviours were identified. The stiffness in the fingertips increased linearly with the amount of force applied, while in the palm it remained more constant for the range of forces considered. It is hypothesised that the differences in the stiffness behaviour from one region to another allow these regions to play different roles during grasping.

Relevance of grasp types to assess functionality for personal autonomy

Study Design Cross-sectional research design. Introduction Current assessment of hand function is not focused on evaluating the real abilities required for autonomy. Purpose of the Study To quantify the relevance of grasp types for autonomy to guide hand recovery and its assessment. Methods Representative tasks of the International Classification of Functioning, Disability and Health activities in which the hands are directly involved were recorded. The videos were analyzed to identify the grasps used with each hand, and their relevance for autonomy was determined by weighting time with the frequency of appearance of each activity in disability and dependency scales. Relevance is provided globally and distinguished by hand (right-left) and bimanual function. Significant differences in relevance are also checked. Results The most relevant grasps are pad-to-pad pinch (31.9%), lumbrical (15.4%), cylindrical (12%), and special pinch (7.3%) together with the nonprehensile (18.6%) use of the hand. Lumbrical grasp has higher relevance for the left hand (19.9% vs 12%) while cylindrical grasp for the right hand (15.3% vs 7.7%). Relevancies are also different depending on bimanual function. Discussion Different relative importance was obtained when considering dependency vs disability scales. Pad-to-pad pinch and nonprehensile grasp are the most relevant grasps for both hands, whereas lumbrical grasp is more relevant for the left hand and cylindrical grasp for the right one. The most significant difference in bimanual function refers to pad-to-pad pinch (more relevant for unimanual actions of the left hand and bimanual actions of the right). Conclusions The relative importance of each grasp type for autonomy and the differences observed between hand and bimanual action should be used in medical and physical decision-making.This research was funded by the Universitat Jaume I through projects P1·1B2013-33 and P1-1B2014-10, and by the Spanish Ministry of Research and Innovation and the European Union (European Regional Development Funds) through project DPI2014-52095-P

Interdependency of the maximum range of flexion–extension of hand metacarpophalangeal joints

Mobility of the fingers metacarpophalangeal (MCP) joints depends on the posture of the adjacent ones. Current Biomechanical hand models consider fixed ranges of movement at joints, regardless of the posture, thus allowing for non-realistic postures, generating wrong results in reach studies and forward dynamic analyses. This study provides data for more realistic hand models. The maximum voluntary extension (MVE) and flexion (MVF) of different combinations of MCP joints were measured covering their range of motion. Dependency of the MVF and MVE on the posture of the adjacent MCP joints was confirmed and mathematical models obtained through regression analyses (RMSE 7.7°).We are grateful to the Spanish Ministry of Economy and Competitiveness for funding through its project DPI2014-52095-P, as well as to the Universitat Jaume I through its project P1-1B2013-33, in which this research is partially included. We also thank the graduate student Lourdes Perez Valiente for her collaboration in data collection

Evaluating a Kinematic Data Glove with Pressure Sensors to Automatically Differentiate Free Motion from Product Manipulation

When studying hand kinematics, it is key to differentiate between free motion and manipulation. This differentiation can be achieved using pressure sensors or through visual analysis in the absence of sensors. Certain data gloves, such as the CyberGlove II, allow recording hand kinematics with good accuracy when properly calibrated. Other gloves, such as the Virtual Motion Glove 30 (VMG30), are also equipped with pressure sensors to detect object contact. The aim of this study is to perform a technical validation to evaluate the feasibility of using virtual reality gloves with pressure sensors such as the VMG30 for hand kinematics characterization during product manipulation, testing its accuracy for motion recording when compared with CyberGlove as well as its ability to differentiate between free motion and manipulation using its pressure sensors in comparison to visual analysis. Firstly, both data gloves were calibrated using a specific protocol developed by the research group. Then, the active ranges of motion of 16 hand joints angles were recorded in three participants using both gloves and compared using repeated measures ANOVAs. The detection capability of pressure sensors was compared to visual analysis in two participants while performing six tasks involving product manipulation. The results revealed that kinematic data recordings from the VMG30 were less accurate than those from the CyberGlove. Furthermore, the pressure sensors did not provide additional precision with respect to the visual analysis technique. In fact, several pressure sensors were rarely activated, and the distribution of pressure sensors within the glove was questioned. Current available gloves such as the VMG30 would require design improvements to fit the requirements for kinematics characterization during product manipulation. The pressure sensors should have higher sensitivity, the pressure sensor’s location should comprise the palm, glove fit should be improved, and its overall stiffness should be reduced

Growth and optical properties of GaN/AlN quantum wells

We demonstrate the growth of GaN/AlN quantum well structures by plasma-assisted molecular-beam epitaxy by taking advantage of the surfactant effect of Ga. The GaN/AlN quantum wells show photoluminescence emission with photon energies in the range between 4.2 and 2.3 eV for well widths between 0.7 and 2.6 nm, respectively. An internal electric field strength of $9.2\pm 1.0$ MV/cm is deduced from the dependence of the emission energy on the well width.Comment: Submitted to AP

Hand posture prediction using neural networks within a biomechanical model

This paper proposes the use of artificial neural networks (ANNs) in the framework of a biomechanical hand model for grasping. ANNs enhance the model capabilities as they substitute estimated data for the experimental inputs required by the grasping algorithm used. These inputs are the tentative grasping posture and the most open posture during grasping. As a consequence, more realistic grasping postures are predicted by the grasping algorithm, along with the contact information required by the dynamic biomechanical model (contact points and normals). Several neural network architectures are tested and compared in terms of prediction errors, leading to encouraging results. The performance of the overall proposal is also shown through simulation, where a grasping experiment is replicated and compared to the real grasping data collected by a data glove device. 

Estimation of the Abduction/Adduction Movement of the Metacarpophalangeal Joint of the Thumb

Thumb opposition is essential for grasping, and involves the flexion and abduction of the carpometacarpal and metacarpophalangeal joints of the thumb. The high number of degrees of freedom of the thumb in a fairly small space makes the in vivo recording of its kinematics a challenging task. For this reason, along with the very limited independence of the abduction movement of the metacarpophalangeal joint, many devices do not implement sensors to measure such movement, which may lead to important implications in terms of the accuracy of thumb models. The aims of this work are to examine the correlation between thumb joints and to obtain an equation that allows thumb metacarpophalangeal abduction/adduction movement to be estimated from the other joint motions of the thumb, during the commonest grasps used during activities of daily living and in free movement. The correlation analysis shows that metacarpophalangeal abduction/adduction movement can be expressed mainly from carpometacarpal joint movements. The model thus obtained presents a low estimation error (6.29°), with no significant differences between grasps. The results could benefit most fields that do not typically include this joint movement, such as virtual reality, teleoperation, 3D modeling, prostheses, and exoskeletons