Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol

Abstract

BackgroundSuccessful hand-object interactions require precise hand-eye coordination with continual movement adjustments. Quantitative measurement of this visuomotor behaviour could provide valuable insight into upper limb impairments. The Gaze and Movement Assessment (GaMA) was developed to provide protocols for simultaneous motion capture and eye tracking during the administration of two functional tasks, along with data analysis methods to generate standard measures of visuomotor behaviour. The objective of this study was to investigate the reproducibility of the GaMA protocol across two independent groups of non-disabled participants, with different raters using different motion capture and eye tracking technology.MethodsTwenty non-disabled adults performed the Pasta Box Task and the Cup Transfer Task. Upper body and eye movements were recorded using motion capture and eye tracking, respectively. Measures of hand movement, angular joint kinematics, and eye gaze were compared to those from a different sample of twenty non-disabled adults who had previously performed the same protocol with different technology, rater and site.ResultsParticipants took longer to perform the tasks versus those from the earlier study, although the relative time of each movement phase was similar. Measures that were dissimilar between the groups included hand distances travelled, hand trajectories, number of movement units, eye latencies, and peak angular velocities. Similarities included all hand velocity and grip aperture measures, eye fixations, and most peak joint angle and range of motion measures.DiscussionThe reproducibility of GaMA was confirmed by this study, despite a few differences introduced by learning effects, task demonstration variation, and limitations of the kinematic model. GaMA accurately quantifies the typical behaviours of a non-disabled population, producing precise quantitative measures of hand function, trunk and angular joint kinematics, and associated visuomotor behaviour. This work advances the consideration for use of GaMA in populations with upper limb sensorimotor impairment.

A Modular Adjustable Transhumeral Prosthetic Socket for Evaluating Myoelectric Control

“…Shoulder movement patterns are shown in Fig 7 , with normative reference data for comparison [33] . Compared to normative movement patterns, both sockets demonstrated similar patterns of shoulder flexion/extension throughout the functional task.…”

Myoelectric prosthesis users and non-disabled individuals wearing a simulated prosthesis exhibit similar compensatory movement strategies

“…The Pasta Box Task, developed by Valevicius et al [ 46 ], validated by Williams et al [ 51 ], and used in prior prosthesis user studies [ 2 , 50 ], mimics the actions of reaching for a kitchen item and moving it to shelves of different heights – thereby including common prosthesis assessment requirements. In this task, the participant is required to perform three movements: Movement 1 – moving a pasta box from a lower side table immediately to their right (height: 30 inches) to a shelf in front of them (height: 43 inches); Movement 2 – moving the pasta box to a second shelf at a higher height across the body (height: 48 inches); and Movement 3 – moving the pasta box back to the starting position on the side table.…”

“…An 8-camera Optitrack Flex 13 motion capture system (Natural Point, OR, USA) was used to capture the three-dimensional trajectories of motion capture markers affixed to the MP participants at a sampling frequency of 120 Hz. While these data were collected at a later date and at a different site than the SP participant data, it should be noted that the reproducibility of the protocol and kinematic results across different motion capture technologies have been previously confirmed [ 51 ]. A rigid plate holding four motion capture markers was affixed to the back of each MP participant’s myoelectric hand, along with individual markers on their index finger and thumb as shown in Fig.…”

Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture

“…3) object at specific locations and times during the interaction task. Eye gaze behaviour is well understood for this task, as described in Lavoie et al [17] and Williams et al [32]. Following the same procedure as in this earlier work, each task trial was segmented into specific movements and movement-phases (Reach, Grasp, Transport and Release) using detailed procedures described elsewhere [17].…”