Design and validation of low-cost assistive glove for hand assessment and therapy during activity of daily living-focused robotic stroke therapy

Abstract

Hand and arm impairment is common after stroke. Robotic stroke therapy will be more effective if hand and upper-arm training is integrated to help users practice reaching and grasping tasks. This article presents the design, development, and validation of a low-cost, functional electrical stimulation grasp-assistive glove for use with task-oriented robotic stroke therapy. Our glove measures grasp aperture while a user completes simple-to-complex real-life activities, and when combined with an integrated functional electrical stimulator, it assists in hand opening and closing. A key function is a new grasp-aperture prediction model, which uses the position of the end-effectors of two planar robots to define the distance between the thumb and index finger. We validated the accuracy and repeatability of the glove and its capability to assist in grasping. Results from five nondisabled subjects indicated that the glove is accurate and repeatable for both static hand-open and -closed tasks when compared with goniometric measures and for dynamic reach-to-grasp tasks when compared with motion analysis measures. Results from five subjects with stroke showed that with the glove, they could open their hands but without it could not. We present a glove that is a low-cost solution for in vivo grasp measurement and assistance.Abbreviations: 3-D = three-dimensional, ADL = activity of daily living, ADLER = Activities of Daily Living Exercise Robot, ANOVA = analysis of variance, DOF = degree of freedom, FES = functional electrical stimulation, GUI = graphical user interface, MCP = metacarpophalangeal, MMT = manual muscle test, PIP = proximal interphalangeal, SD = standard deviation, UL Model = Bilateral Upper-Limb Kinematic Model.

Evaluation of Hand Motion Capture Protocol Using Static Computed Tomography Images: Application to an Instrumented Glove

“…Consequently, the loss of hand function is very debilitating and often requires extensive rehabilitation. Hand motion capture protocols can be used to record hand kinematics that are useful for the functional evaluation of the pathological hand and the follow-up evaluation of its rehabilitation [2][3][4]. In addition, knowledge of hand kinematics obtained through motion capture can be used to understand human motor control strategies, to analyze sporting techniques, or in the ergonomic evaluation of handheld tool use [5][6][7].…”

Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes

“…The versatility of the human hand is possible thanks to the complex kinematics of the system: 25 degrees of freedom (DoFs) controlled by muscles, tendons and ligaments (Brand and Hollister 1999). Measurement of complex hand movements is useful for numerous applications, including functional assessment of the pathological hand and its rehabilitation (Chiu et al 2000;Nathan, Johnson, and McGuire 2009;Oess, Wanek, and Curt 2012), analysis of sporting techniques and ergonomics of tools, the study of human motor control strategies, and robotics (Griffin et al 2000;Grinyagin, Biryukova, and Maier 2005;Sanchez-Margallo et al 2010;Tripp et al 2006). Different methods can be used to measure hand movement, but most of them fail when applied to the simultaneous measurement of all hand DoFs while performing functional ADL.…”

Design Method for a Reconfigurable Mechanism for Finger Rehabilitation

“…Out of the generated solution possibilities, the solution described below was selected due to the short link lengths and close base point locations. The values for the structural variables of the four-bar linkage that were obtained in the selected single-trajectory solution are displayed below in (6). The variable names refer to those on the diagram of the four bar linkage shown previously in Figure 2 …”

“…Many devices currently exist which were either designed or can be used for rehabilitation of the hand [6]- [17]. The majority of this research focus has been on the robot design, with less attention given to evaluating control strategies for their ability to provide functional recovery.…”

“…Of the glove designs, few allow for full tactile feedback for the palm, and many do not allow for tactile feedback for the fingertips either. Most also do not have drive systems that are backdrivable or have fast response [6], [7]. Of the exoskeleton designs, many have multiple degrees-of-freedom, and are therefore very dynamically complicated [8]- [13].…”