Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

Abstract

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.

Figure 1

Robotic and VR-based rehabilitation.

Figure 2

Components of the GH joint angles estimation system: (a) human kinematic model, (b) exoskeleton kinematic model, (c) marker-based optical motion capture system, and (d) hybrid GH joint angles estimation system.

Figure 3

(a) Schematic diagram of the hybrid GH joint angles estimation system and (b) high-level operation of the system.

Figure 4

Schematic diagram of the iterative estimation of the pose of the markers.

Figure 5

Estimation of disk coordinates in color image. (a) Simulated RBG image, (b) result of the color segmentation (zoomed image), and (c) result of the blob extraction (zoomed image).

Figure 6

Schematic diagram of the iterative estimation of the pose of the cameras.

Figure 7

Schematic diagram of the iterative estimation of the pose of the markers w.r.t. the exoskeleton CS.

Figure 8

Schematic diagram of the iterative estimation of the upper arm pose.

Figure 9

Coordinate systems for the upper arm pose estimation.

Figure 10

GH joint movements: (a) shoulder flexion-extension (SFE), (b) shoulder horizontal abduction-adduction (SAbAd), and (c) shoulder internal rotation (SIR).

Figure 11

Sensitivity analysis steps.

Figure 12

Sensitivity analysis. Coordinate systems of reference for the translations of (a) marker m₀ and (b) marker m₁.

Figure 13

Box plots of estimation errors in markers position and upper arm position and orientation for all movement datasets.

Figure 14

Results of the sensitivity analysis with the SAdAd movement dataset (q_j: m₀-x movement/m₀-y movement/m₀-z movement/m₁-x movement/m₁-y movement/m₁-z movement).

Figure 15

Results of the sensitivity analysis with the SFE movement dataset (q_j: m₀-x movement/m₀-y movement/m₀-z movement/m₁-x movement/m₁-y movement/m₁-z movement).

Figure 16

Results of the sensitivity analysis with the SIR movement dataset (q_j: m₀-x movement/m₀-y movement/m₀-z movement/m₁-x movement/m₁-y movement/m₁-z movement).

Figure 17

Results of the sensitivity analysis with the COMB movement dataset (q_j: m₀-x movement/m₀-y movement/m₀-z movement/m₁-x movement/m₁-y movement/m₁-z movement).