GaMA: A quantitative outcome metric for upper limb sensory-motor function

Rationale: The movement of our eyes to specific locations is intimately tied to the demands of a task.  Visual attention is an integral component of motor performance expected to change with accurate sensory feedback and intuitive motor control. Hand function impairment also leads to proximal body compensations, which can lead to poor performance and long-term complications.  

Method: GaMA combines motion tracking and eye tracking during functional real-world tasks. This metric quantifies the motion of the prosthesis (or hand and arm), compensatory body movements and visual gaze behavior during standard tasks. Metrics output include upper limb and hand kinematic measures as well as measures of visual attention. 

Applicability: This assessment metric is relevant to many conditions of upper limb sensory-motor impairment, so long as the individual is able to grasp and move objects.

Funding

Functional Metrics for Humans with Bi-Directionally Integrated Prosthetic Limbs

• Investigators: JS Hebert, CS Chapman, AH Vette, PM Pilarski
• Previous funding: Hand Proprioception & Touch Interfaces (HAPTIX), Defense Advanced Research Projects Agency (DARPA). Subcontracted through Dr. Paul Marasco, Cleveland Clinic.

Publications

Validation Papers:

1. Valevicius AM, Jun PY, Hebert JS, Vette AH. Use of optical motion capture for the analysis of normative upper body kinematics during functional upper limb tasks: A systematic review. Journal of Electromyography and Kinesiology. 2018, Feb; 40: 1–15. https://doi.org/10.1016/j.jelekin.2018.02.011. This review paper critically assessed kinematic model characteristics, the performed functional tasks, kinematic outcomes, and reported validity and reliability as background work prior to developing our protocols.

2. Boser QA, Valevicius AM, Lavoie EB, Chapman CS, Pilarski PM, Hebert JS, Vette AH. Cluster-Based Upper Body Marker Models for Three-Dimensional Kinematic Analysis: Comparison with an Anatomical Model and Reliability Analysis. Journal of Biomechanics. 2018, Apr; 72: 228–34. https://doi.org/10.1016/j.jbiomech.2018.02.028. We compared the use of upper body cluster marker models with an anatomical model for optical motion capture during the performance of two functional tasks used in GaMA, including between-session reliability.

3. Valevicius AM, Boser QA, Lavoie EB, Murgatroyd G, Chapman CS, Pilarski PM, Vette AH, Hebert JS. Characterization of normative hand movements during two functional upper limb tasks. PLoS ONE. 2018, Jun; 13(6): e0199549. https://doi.org/10.1371/journal.pone.0199549. We present the two standardized upper limb task protocols used in GaMA, and quantitatively characterize the kinematics of normative hand movement using optical motion capture.

4. Lavoie EB, Valevicius AM, Boser QA, Kovic O, Vette AH, Pilarski PM, Hebert JS, Chapman CS. Using synchronized eye and motion tracking to determine high-precision eye movement patterns during object interaction tasks. Journal of Vision. 2018, Jun; 18(6): 1–20.https://doi.org/10.1167/18.6.18. This paper established normative values for visual attention during the functional GaMA tasks, using head mounted eye tracking and optical motion capture with kinematic segmentation.

5. Valevicius AM, Boser QA, Lavoie EB, Chapman CS, Pilarski PM, Hebert JS, Vette AH. Characterization of normative angular joint kinematics during two functional upper limb tasks. Gait & Posture. 2019, Jan; 69(2019): 176-186. https://doi.org/10.1016/j.gaitpost.2019.01.037. GaMA is used to quantitatively characterize the kinematics of normative joint movement kinematics during the standardized task protocols, including test-retest reliability.

6. Williams HE et al. Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol. Preprint 10.1101/681437 posted on bioRxiv: http://biorxiv.org/cgi/content/short/681437v1 .  This paper demonstrated the reproducibility of the GaMA testing protocol at a different site in a second normative population, with new technology and a different rater.                              

7. Hebert JS, Boser QA, Valevicius AM, et al. Quantitative Eye Gaze and Movement Differences in Visuomotor Adaptations to Varying Task Demands Among Upper-Extremity Prosthesis Users. JAMA Netw Open. 2019;2(9):e1911197. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2749780.  The different visuomotor adaptations required of prosthesis users to perform the two functional GaMA tasks are presented, emphasizing the importance of task selection when measuring visuomotor behaviours..

8. Valevicius AM, Boser QA, Chapman CS, Pilarski PM, Vette AH, Hebert JS. Compensatory strategies of body-powered prosthesis users reveal primary reliance on trunk motion and relation to skill level. Clinical Biomechanics. 2019; in press. https://doi.org/10.1016/j.clinbiomech.2019.12.002. This paper used GaMA analysis to identify that typical kinematic compensations required to perform the functional task are more apparent in prosthesis users with lower rated skill level.

9. Williams HE*, Chapman CS, Pilarski PM, Vette AH, Hebert JS. (2021). Myoelectric Prosthesis Users and Non-Disabled Individuals Wearing A Simulated Prosthesis Exhibit Similar Compensatory Movement Strategies.  J NeuroEngineering Rehabil 18, 72 (2021). https://doi.org/10.1186/s12984-021-00855-x. This study suggests that the use of a simulated prosthetic device performing functional GaMA tasks is a reasonable approximation of compensatory movements employed by a low- to moderately-skilled transradial myoelectric prosthesis user.