Kinematic Model Of Hand Research Articles

Hand model with dependency constrained joints for applications in rehabilitation robotics

Aim: This work presents a method for developing a simplified but efficient model of the complex human hand kinematics with the aim of its implementation in rehabilitation robotics. Material and methods: The approach incorporates modularity by simplifying the available model comprising 24 degrees of freedom (DOFs) to 9 DOFs, with the introduction of additional joint coupling parameters specific to different grasp types. The effect of dependent joints to the ranges-of-motion (ROMs) of the model is investigated and compared to the anatomical one. The index, middle, ring and little finger solutions to forward and inverse kinematics problems are then acquired. The implementation of the model, based on the median male bones dimensions, is made available in the open-source Robot Operating System (ROS) framework. Results: By including additional four inclination angles per finger, the devised kinematic hand model encompasses also finger curvatures, resulting in significant positioning accuracy improvements compared to the conventional model. The used 3D spatial position improvement metrics are the mean absolute (MAE) and mean relative errors (MRE). The dependent joint position MAEs range from 0.22 to 0.34 cm, while MREs range from 2.8 and 3.5 %, whereas the highest absolute and relative errors during fingertip positioning can reach 0.5 cm and 10.5 %, respectively. Conclusion: The performed investigation allowed establishing that by modelling finger curvature and assuring the adaptability of the model to a variety of human hands and rehabilitation modalities through joint dependency, represents the best approach towards a relatively simple and applicable rehabilitation model with functional human-like hand movements.

MagIK: A Hand-Tracking Magnetic Positioning System Based on a Kinematic Model of the Hand

In this article, we present a hand-tracking system based on magnetic positioning. A single magnetic node is mounted on each fingertip and two magnetic nodes on the back side of the hand. A fixed array of receiving coils is used to detect the magnetic field, from which it is possible to infer the position and orientation of each magnetic node. A kinematic model of the whole hand has been developed. Starting from the positioning data of each magnetic node, the kinematic model can be used to calculate the position and flexion angle of each finger joint, plus the position and orientation of the hand in space. Relying on magnetic fields, the hand-tracking system can work also in non-line-of-sight conditions. The gesture reconstruction is validated by comparing it with a commercial hand-tracking system based on a depth camera. The system requires a small number of electronics to be mounted on the hand. This would allow building a light and comfortable data glove that could be used for several purposes: human-machine interface, sign language recognition, diagnostics, and rehabilitation.

Towards Understanding Complex Human Dexterous Manipulation Strategies: Kinematics of Gaiting-based Object Rotations.

This paper presents a novel method for tracking gaiting-based (changing contacts, reciprocal, cyclical) withinhand manipulation strategies of a human hand. We present a kinematic model that relies on data collected from 6-DOF magnetic sensors attached to 7 external sites on the hand. The sensors are calibrated by three procedures-sensor-to-fingertip, constrained fingertip workspace limits, and flat hand configuration. Subjects rotated two cubes of different sizes around the 3 object-centric axes, while a synchronized camera recorded the object motion. Hand motions were segmented and then averaged using dynamic time warping (DTW) to yield a representative time-series motion primitive for the given task. The hand movements of two subjects during cube rotation tasks were reconstructed using a 22-degree of freedom (DOF) hand kinematic model. Based on a qualitative evaluation of the joint movements, intrasubject correlations of joint angles were found.

A stochastic algorithm for automatic hand pose and motion estimation.

In this paper, a novel, robust, and simple method for automatically estimating the hand pose is proposed and validated. The method uses a multi-camera optoelectronic system and a model-based stochastic algorithm. The approach is marker-based and relies on an Unscented Kalman Filter. A hand kinematic model is introduced for constraining relative marker's positions and improving the algorithm robustness with respect to outliers and possible occlusions. The algorithm outputs are 3D coordinate measures of markers and hand joint angle values. To validate the proposed algorithm, a comparison with ground truths for angular and 3D coordinate measures is carried out. The comparative analysis shows the advantages of using the model-based stochastic algorithm with respect to standard processing software of optoelectronic cameras in terms of implementation simplicity, time consumption, and user effort. The accuracy is remarkable, with a difference of maximum 0.035r a d and 4m m with respect to angular and 3D Cartesian coordinates ground truths, respectively.

Kinematic Structures Description of Bionic Hand Based on VF Set

This paper presents a method for describing kinematic structure of bionic hand based on VF (virtual finger) set. At first, a 20 DOFs (degrees of freedom) human hand kinematic model is built, which is expressed by five fingers’ kinematic chains consisting of kinematic pairs and symbols that represent geometric relationships of kinematic pairs’ axes. Based on the concept of VF, the hand fingers are divided into two types: VFAA having adduction/abduction motion and VFFE having flexion/extension motion. The concept of VF set comprising VFAAs and VFFEs is defined, human hand and six basic grasp postures are described by VF set. Then, the structures corresponding VFAA and VFFE are given according to active and passive forms of finger joints, and VFFE Structure-Base comprising 20 conventional structures is built. Based on VF set and the structures of VFAA and VFFE, VF sets and kinematic structures of several classic bionic hands are given.

Combined Vision and Wearable Sensors-based System for Movement Analysis in Rehabilitation.

Traditional rehabilitation sessions are often a slow, tedious, disempowering and non-motivational process, supported by clinical assessment tools, i.e. evaluation scales that are prone to subjective rating and imprecise interpretation of patient's performance. Poor patient motivation and insufficient accuracy are thus critical factors that can be improved by new sensing / processing technologies. We aim to develop a portable and affordable system, suitable for home rehabilitation, which combines vision-based and wearable sensors. We introduce a novel approach for examining and characterizing the rehabilitation movements, using quantitative descriptors. We propose new Movement Performance Indicators (MPIs) that are extracted directly from sensor data and quantify the symmetry, velocity, and acceleration of the movement of different body/hand parts, and that can potentially be used by therapists for diagnosis and progress assessment. First, a set of rehabilitation exercises is defined, with the supervision of neurologists and therapists for the specific case of Parkinson's disease. It comprises full-body movements measured with a Kinect device and fine hand movements, acquired with a data glove. Then, the sensor data is used to compute 25 Movement Performance Indicators, to assist the diagnosis and progress monitoring (assessing the disease stage) in Parkinson's disease. A kinematic hand model is developed for data verification and as an additional resource for extracting supplementary movement information. Our results show that the proposed Movement Performance Indicators are relevant for the Parkinson's disease assessment. This is further confirmed by correlation of the proposed indicators with clinical tapping test and UPDRS clinical scale. Classification results showed the potential of these indicators to discriminate between the patients and controls, as well as between the stages that characterize the evolution of the disease. The proposed sensor system, along with the developed approach for rehabilitation movement analysis have a significant potential to support and advance traditional rehabilitation therapy. The main impact of our work is two-fold: (i) the proposition of an approach for supporting the therapists during the diagnosis and monitoring evaluations by reducing subjectivity and imprecision, and (ii) offering the possibility of the system to be used at home for rehabilitation exercises in between sessions with doctors and therapists.

Toward a realistic optoelectronic-based kinematic model of the hand: representing the transverse metacarpal arch reduces accessory rotations of the metacarpophalangeal joints

A kinematic model representing the versatility of the human hand is needed to evaluate biomechanical function and predict injury risk in the workplace. We improved upon an existing optoelectronic-based kinematic hand model with grouped metacarpals by defining segmented metacarpals and adding the trapeziometacarpal joint of the thumb. Eight participants performed three static postures (neutral pose, cylinder grip, cap grip) to evaluate kinematic performance of three different models, with one, two, and four metacarpal segment(s). Mean distal transverse metacarpal arch angles in the four-segment metacarpal model were between 22.0° ± 3.3° (neutral pose) and 32.1° ± 3.7° (cap grip). Representation of the metacarpals greatly influenced metacarpophalangeal joint rotations. Both the two- and four-segment metacarpal models displayed significantly lower metacarpophalangeal joint ‘supination’ angles (than the one-segment model) for the fourth and fifth fingers. However, the largest reductions were for the four- versus one-segment models, with mean differences ranging from 9.3° (neutral pose) to 17.0° (cap grip) for the fourth finger and 16.3° (neutral pose) to 33.0° (cylinder grip) for the fifth finger. MCP joint abduction/adduction angles of the fourth and fifth fingers also decreased with segmentation of the metacarpals, although the lowest magnitudes generally occurred in the four-segment model. Overall, the four-segment metacarpal model produced the lowest accessory rotations in non-dominant axes, and best matched previous radiological studies that found MCP joint pronation/supination angles were typically less than 10°. The four-segment metacarpal model, with improved anatomic fidelity, will better serve future studies of detailed actions of the hand in clinical or work applications.

On the generation of a variety of grasps

In everyday life, people use a large diversity of hand configurations while reaching out to grasp an object. They tend to vary their hands position/orientation around the object and their fingers placement on its surface according to the object properties such as its weight, shape, friction coefficient and the task they need to accomplish. Taking into account these properties, we propose a method for generating such a variety of good grasps that can be used for the accomplishment of many different tasks. Grasp synthesis is formulated as a single constrained optimization problem, generating grasps that are feasible for the hand’s kinematics by minimizing the norm of the joint torque vector of the hand ensuring grasp stability. Given an object and a kinematic hand model, this method can easily be used to build a library of the corresponding object possible grasps. We show that the approach is adapted to different representations of the object surface and different hand kinematic models.

A finger motion model for reach and grasp

This study aimed to develop a model that describes human finger motion for simulation of reach and grasp for selected objects and tasks. Finger joint angles and timing of their changes were measured for six subjects as they reached 20–40 cm and grasped cylindrical handles (1.3–10.2 cm D) of varying orientation (vertical/axial). The empirical results from multiple regression analyses served as inputs to allow a fourth order polynomial to predict motion of each finger joint. The proposed model showed good fit with observations, with high coefficients of determination from 0.54 to 1 and reasonable errors from 0.04° to 5.44° for all conditions considered. The proposed finger motion model was implemented in an existing kinematic hand model to employ a contact algorithm for refined prediction of grip posture and to illustrate its predictive power by graphically displaying the opening and closing of the hand. Relevance to industry Finger joint motions during reach and grasp are needed for prediction of (1) tendon excursions for study of work-related musculoskeletal disorders, (2) required space for the hand, (3) finger locations on work objects, and (4) hand grip postures and strength.

Skin and Bone Surfaces for a Three-Dimensional Kinematic Hand Model

Skin and Bone Surfaces for a Three-Dimensional Kinematic Hand Model

Skin and Bone Surfaces for a Three-Dimensional Kinematic Hand Model

We have previously described the development of a 20 link, 25 degrees-of-freedom three-dimensional kinematic model of the hand (Buchholz and Armstrong 1992, Choi and Armstrong 2006). Each link corresponds to a segment of the hand. We also showed how this model can be used to predict 1) hand posture and finger placement using contact algorithms and 2) the space required for the hand to reach for and grasp work objects. The present model uses an array of points based on truncated cones to describe the skin surface. This study aims to develop models for describing the surface of the hand that are congruent with the anatomic structure of the hand. These models will improve the model predictions of finger placement and posture, of the space required by the hand, and the hand image. In addition, we also aim to develop models that describe the bones in the hand that will make it possible to study tendon excursions, loads and injuries. Graphic files that describe the surfaces of the hand and the bones were generated from multiple computed tomography images of adult human hands. The centers of flexor and extensor tendons were also identified in the images and located with respect to the above surfaces. These anatomical features were then scaled and added to the link structure of the biomechanical hand model.

Measurements of Wrist and Finger Postures: A Comparison of Goniometric and Motion Capture Techniques

A marker-based kinematic hand model to quantify finger postures was developed and compared to manual goniometric measurements. The model was implemented with data collected from static postures of five subjects. The metacarpal phalangeal (MCP) and proximal interphalangeal (PIP) joints were positioned in flexion of approximately 30, 60, and 90 degrees for 5 subjects. Wrist flexion/extension and ulnar/radial deviations were also examined. The model-based angles for the MCP and PIP joints were not statistically equivalent to the goniometric measurements, with differences of -1.8 degrees and +3.5 degrees, respectively. Differences between the two measurement methods for the MCP and PIP were found to be a function of the posture (i.e., 150, 120, or 90 degree blocks) used. Wrist measurements differed by -4.0 degrees for ulnar/radial deviation and +5.2 degrees for flexion/extension. Much of the difference between the model and goniometric measurements is believed due to inaccuracies in the goniometric measurements. The proposed model is useful for future investigations of finger-intensive activities by supplying accurate and unbiased measures of joint angles.

A Cognitive Architecture for Robotic Hand Posture Learning

This paper deals with the design and implementation of a visual control of a robotic system composed of a dexterous hand and video camera. The aim of the proposed system is to reproduce the movements of a human hand in order to learn complex manipulation tasks or to interact with the user. A novel algorithm for robust and fast fingertips localization and tracking is presented. A suitable kinematic hand model is adopted to achieve a fast and acceptable solution to an inverse kinematics problem. The system is part of a cognitive architecture for posture learning that integrates the perceptions by a high-level representation of the scene and of the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces, and to perform complex interactions with the human operator.

Derivation of Centers and Axes of Rotation for Wrist and Fingers in a Hand Kinematic Model: Methods and Reliability Results

In the field of 3D reconstruction of human motion from video, model-based techniques have been proposed to increase the estimation accuracy and the degree of automation. The feasibility of this approach is strictly connected with the adopted biomechanical model. Particularly, the representation of the kinematic chain and the assessment of the corresponding parameters play a relevant role for the success of the motion assessment. In this paper, the focus is on the determination of the kinematic parameters of a general hand skeleton model using surface measurements. A novel method that integrates nonrigid sphere fitting and evolutionary optimization is proposed to estimate the centers and the functional axes of rotation of the skeletal joints. The reliability of the technique is tested using real movement data and simulated motions with known ground truth 3D measurement noise and different ranges of motion (RoM). With respect to standard nonrigid sphere fitting techniques, the proposed method performs 10-50% better in the best condition (very low noise and wide RoM) and over 100% better with physiological artifacts and RoM. Repeatability in the range of a couple of millimeters, on the localization of the centers of rotation, and in the range of one degree, on the axis directions is obtained from real data experiments.