A natural human hand model

Background: Although various hand assist devices have been commercialized for people with paralysis, they are somewhat limited in terms of tool fixation and device attachment method. Hand exoskeleton robots allow users to grasp a wider range of tools but are heavy, complicated, and bulky owing to the presence of numerous actuators and controllers. The GRIPIT hand assist device overcomes the limitations of both conventional devices and exoskeleton robots by providing improved tool fixation and device attachment in a lightweight and compact device. GRIPIT has been designed to assist tripod grasp for people with spinal cord injury because this grasp posture is frequently used in school and offices for such activities as writing and grasping small objects. Methods: The main development objective of GRIPIT is to assist users to grasp tools with their own hand using a lightweight, compact assistive device that is manually operated via a single wire. GRIPIT consists of only a glove, a wire, and a small structure that maintains tendon tension to permit a stable grasp. The tendon routing points are designed to apply force to the thumb, index finger, and middle finger to form a tripod grasp. A tension-maintenance structure sustains the grasp posture with appropriate tension. Following device development, four people with spinal cord injury were recruited to verify the writing performance of GRIPIT compared to the performance of a conventional penholder and handwriting. Writing was chosen as the assessment task because it requires a tripod grasp, which is one of the main performance objectives of GRIPIT. Results: New assessment, which includes six different writing tasks, was devised to measure writing ability from various viewpoints including both qualitative and quantitative methods, while most conventional assessments include only qualitative methods or simple time measuring assessments. Appearance, portability, difficulty of wearing, difficulty of grasping the subject, writing sensation, fatigability, and legibility were measured to assess qualitative performance while writing various words and sentences. Results showed that GRIPIT is relatively complicated to wear and use compared to a conventional assist device but has advantages for writing sensation, fatigability, and legibility because it affords sufficient grasp force during writing. Two quantitative performance factors were assessed, accuracy of writing and solidity of writing. To assess accuracy of writing, we asked subjects to draw various figures under given conditions. To assess solidity of writing, pen tip force and the angle variation of the pen were measured. Quantitative evaluation results showed that GRIPIT helps users to write accurately without pen shakes even high force is applied on the pen.

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“…The grasp can fail when the three fingers fail to gather or when one finger applies higher contact force to the object than the others. [17,18].…”

Section: Methodsmentioning

confidence: 99%

Development and assessment of a hand assist device: GRIPIT

Kim

Lee

et al. 2017

J NeuroEngineering Rehabil

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“…Researchers worked on modelling various body parts, including face [ 12][ 13], neck [ 14], torso [ 15], hand [ 16], and leg [ 17]. In particular, many researchers [ 18] [ 31] constructed an upper extremity (UE) model for application in stroke rehabilitation to accurately track the three-dimensional orientation of the trunk, shoulder, elbow, and wrist during task performance.…”

Section: Virtual Human Modellingmentioning

confidence: 99%

Future Challenges of User Modelling for Accessibility

Mohamad

Kouroupetroglou²,

Kirisci

2014

Universal Access in Human-Computer Interaction. Design and Development Methods for Universal Access

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Abstract. User models are abstract representations of user properties including their needs, preferences, knowledge, as well as physical, cognitive, and behavioral characteristics. These characteristics are usually represented by variables. User models are instantiated by the declaration of these variables for a particular user or group of users. Such instances of user models are called user profiles. A user profile captures the kind of information about an individual user that is considered by an adaptive system to adapt to aspects of a certain situation and preferences of different users.Complimentarily the process of user modelling can be applied to enhance the accessibility of user interfaces by generating or adapting them according to the particular user needs and preferences represented in the user profiles. In spite of the different approaches in this area, further research and development is necessary, particularly in addressing the need for standards to support the interoperability and portability of user models across implementations. More specifically, one of the main challenges of user modelling is the lack of a common approach for integrating user profiles that support different user models within individual implementations, and for migrating profiles from one implementation to another. This can be attributed to the broad variety of user profiles and the incompatibilities that can occur among them. For example, differences in user profiles can occur due to differences in scope of the modelling, source of information for the modelling, time sensitivity and update methods of the model (static vs. dynamic model). In this paper a thorough review of the latest developments in the area of user modelling for accessibility is presented. Further, in the core of the paper future potentials and challenges that this technology has to face in order to gain significant traction and adoption from wider audiences is analyzed.

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“…The purpose was to analyse the deformation of the palm surface during the grasp of an object. Recent models incorporate the surface of the hand as a mesh object with more or less realism, obtained from the location of a number of landmarks of the hand or from digital 3D-scanning of the hand (Endo et al, 2007;Peña-Pitarch, 2007, van Nierop et al, 2008. The mesh is linked to a skeleton whose movement controls the deformation of the mesh with different types of algorithms.…”

Section: Hand Models In Ergonomicsmentioning

confidence: 99%