Hand model with dependency constrained joints for applications in rehabilitation robotics

Bazina, Tomislav; Schnurrer-Luke-Vrbanić, Tea; Črnjarić-Žic, Nelida; Zelenika, Saša; Kamenar, Ervin

doi:10.21860/medflum2022_284696

Cited by 1 publication

(1 citation statement)

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“…The first step of the approach involved decoupling each grasp into its constituent joint movements and focusing only on 16 finger joints performing flexion/extension. Due to the hand anatomy, these joints cover most of the hand's ranges of motion (ROM) [18]. Then, the dataset was prepared using summary statistics and joint anatomical ROM.…”

Section: Introductionmentioning

confidence: 99%

Reducing Hand Kinematics by Introducing Grasp-Oriented Intra-Finger Dependencies

Bazina,

Mauša,

Zelenika

et al. 2024

Robotics

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Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing efficient restorative therapies. These therapies aim to improve hand functionality with minimal therapist intervention. However, the human hand evolved for various precision and power gripping tasks, with its intricate anatomy featuring a large number of degrees of freedom—up to 31—which hinder its modeling in many rehabilitation scenarios. In the process of designing prosthetic devices, instrumented gloves, and rehabilitation devices, there is a clear need to obtain simplified rehabilitation-oriented hand models without compromising their representativeness across the population. This is where the concept of kinematic reduction, focusing on specific grasps, becomes essential. Thus, the objective of this study is to uncover the intra-finger dependencies during finger flexion/extension by analyzing a comprehensive database containing recorded trajectories for 23 different functional movements related to ADLs, involving 77 test subjects. The initial phase involves data wrangling, followed by correlation analysis aimed at selecting 116 dependency-movement relationships across all grasps. A regularized generalized linear model is then applied to select uncorrelated predictors, while a linear mixed-effect model, with reductions based on both predictor significance and effect size, is used for modeling the dependencies. As a final step, agglomerative clustering of models is performed to further facilitate flexibility in tradeoffs in hand model accuracy/reduction, allowing the modeling of finger flexion extensions using 5–15 degrees of freedom only.

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Section: Introductionmentioning

confidence: 99%