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

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

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. 

Synergy-Based Sensor Reduction for Recording the Whole Hand Kinematics

Simultaneous measurement of the kinematics of all hand segments is cumbersome due to sensor placement constraints, occlusions, and environmental disturbances. The aim of this study is to reduce the number of sensors required by using kinematic synergies, which are considered the basic building blocks underlying hand motions. Synergies were identified from the public KIN-MUS UJI database (22 subjects, 26 representative daily activities). Ten synergies per subject were extracted as the principal components explaining at least 95% of the total variance of the angles recorded across all tasks. The 220 resulting synergies were clustered, and candidate angles for estimating the remaining angles were obtained from these groups. Different combinations of candidates were tested and the one providing the lowest error was selected, its goodness being evaluated against kinematic data from another dataset (KINE-ADL BE-UJI). Consequently, the original 16 joint angles were reduced to eight: carpometacarpal flexion and abduction of thumb, metacarpophalangeal and interphalangeal flexion of thumb, proximal interphalangeal flexion of index and ring fingers, metacarpophalangeal flexion of ring finger, and palmar arch. Average estimation errors across joints were below 10% of the range of motion of each joint angle for all the activities. Across activities, errors ranged between 3.1% and 16.8%

A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models

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

Characterisation of Grasp Quality Metrics

Robot grasp quality metrics are used to evaluate, compare and select robotic grasp configurations. Many of them have been proposed based on a diversity of underlying principles and to assess different aspects of the grasp configurations. As a consequence, some of them provide similar information but other can provide completely different assessments. Combinations of metrics have been proposed in order to provide global indexes, but these attempts have shown the difficulties of merging metrics with different numerical ranges and even physical units. All these studies have raised the need of a deeper knowledge in order to determine independent grasp quality metrics which enable a global assessment of a grasp, and a way to combine them. This paper presents an exhaustive study in order to provide numerical evidence for these issues. Ten quality metrics are used to evaluate a set of grasps planned by a simulator for 7 different robot hands over a set of 126 object models. Three statistical analysis, namely, variability, correlation and sensitivity, are performed over this extensive database. Results and graphs presented allow to set practical thresholds for each quality metric, select independent metrics, and determine the robustness of each metric,providing a reliability indicator under pose uncertainty. The results from this paper are intended to serve as guidance for practical use of quality metrics by researchers on grasp planning algorithms