Reachable Workspace and Proximal Function Measures for Quantifying Upper Limb Motion

Abstract

There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations.

The Black Box of Technological Outcome Measures: An Example in Duchenne Muscular Dystrophy

“…FWS determines the ability to reach different targets close to the body with the hand (simulating the motions of some common activities of daily living). Custom software was developed at University of California to collect the Kinect skeletal motion data [13]. During each FWS trial, participants were asked to reach with their right hand towards seven upper extremity targets: belt buckle or stomach, back pocket, ipsilateral shoulder, contralateral shoulder, mouth, top of head, and back of head (Fig.…”

“…Patients were instructed not to use their trunk to assist with their motion. The rigid body model was utilized to define the position for each of the seven landmark targets as described previously [13]. A second order, 1 Hz low-pass Butterworth filter was used to smooth the estimated distance timeseries.…”

“…Leap Motion aROM has not yet been evaluated as an outcome measure in neuromuscular disorders. For the Kinect v2 sensor, we evaluated three assessment protocols: "Ability Captured Through Interactive Video Evaluation" (ACTIVE) [12], Functional Workspace (FWS) [13], and Kinect Trunk Compensation (KinectTC) [14]. ACTIVE was developed as an outcome measure for neuromuscular diseases through the assessment of the reaching ability of the arm, summarized as a volume of reach during performance of a game activity.…”

“…In the FWS protocol, the Kinect is used to assess the ability to touch seven upper extremity body points via a guided video. The methodology for the analysis of the motion has been validated with a standard marker-based motion capture in HCs [13]. The FWS assessment has not been studied previously in DMD patient population.…”

Nonlinear time delay estimation based model reference adaptive impedance control for an upper-limb human-robot interaction

“…A seated position is used to determine the amount of reachable workspace (RW), which illustrates the space that a healthy person could reach while she/ he is seated. The proposed RW by Matthew et al 6 was a 120 mm 3 100 mm 3 120 mm space. The game maps (a horizontal surface 980 mm 3 420 mm, or 620 mm 3 420 mm) for the Cellulo robots are proposed by Ozgur et al 7 Li et al developed a 3-D space (120 mm 3 120 mm 3 60 mm) for the CDULRR cable-driven robot, where a circle with a diameter of 300 mm is used for the experimental tests.…”