Modeling upper limb clinical scales by robot-measured performance parameters

Smallest real differences for robotic measures of upper extremity function after stroke: Implications for tracking recovery

Introduction

Measurements from upper limb rehabilitation robots could guide therapy progression, if a robotic assessment’s measurement error was small enough to detect changes occurring on a time scale of a few days. To guide this determination, this study evaluated the smallest real differences of robotic measures, and of clinical outcome assessments predicted from these measures.

Methods

A total of nine older chronic stroke survivors took part in 12-week study with an upper-limb end-effector robot. Fourteen robotic measures were extracted, and used to predict Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and Action Research Arm Test (ARAT) scores using multilinear regression. Smallest real differences and intraclass correlation coefficients were computed for the robotic measures and predicted clinical outcomes, using data from seven baseline sessions.

Results

Smallest real differences of robotic measures ranged from 8.8% to 26.9% of the available range. Smallest real differences of predicted clinical assessments varied widely depending on the regression model (1.3 to 36.2 for FMA-UE, 1.8 to 59.7 for ARAT), and were not strongly related to a model’s predictive performance or to the smallest real differences of the model inputs. Models with acceptable predictive performance as well as low smallest real differences were identified.

Conclusions

Smallest real difference evaluations suggest that using robotic assessments to guide therapy progression is feasible.

Upper-limb kinematic reconstruction during stroke robot-aided therapy

The paper proposes a novel method for an accurate and unobtrusive reconstruction of the upper-limb kinematics of stroke patients during robot-aided rehabilitation tasks with end-effector machines. The method is based on a robust analytic procedure for inverse kinematics that simply uses, in addition to hand pose data provided by the robot, upper arm acceleration measurements for computing a constraint on elbow position; it is exploited for task space augmentation. The proposed method can enable in-depth comprehension of planning strategy of stroke patients in the joint space and, consequently, allow developing therapies tailored for their residual motor capabilities. The experimental validation has a twofold purpose: (1) a comparative analysis with an optoelectronic motion capturing system is used to assess the method capability to reconstruct joint motion; (2) the application of the method to healthy and stroke subjects during circle-drawing tasks with InMotion2 robot is used to evaluate its efficacy in discriminating stroke from healthy behavior. The experimental results have shown that arm angles are reconstructed with a RMSE of 8.3 × 10(-3) rad. Moreover, the comparison between healthy and stroke subjects has revealed different features in the joint space in terms of mean values and standard deviations, which also allow assessing inter- and intra-subject variability. The findings of this study contribute to the investigation of motor performance in the joint space and Cartesian space of stroke patients undergoing robot-aided therapy, thus allowing: (1) evaluating the outcomes of the therapeutic approach, (2) re-planning the robotic treatment based on patient needs, and (3) understanding pathology-related motor strategies.

Mechanisms of motor recovery in chronic and subacute stroke patients following a robot-aided training

The aim of this article is to propose a methodology for analyzing different recovery mechanisms in subacute and chronic patients through evaluation of biomechanical parameters. Twenty-five post-stroke subjects, eight subacute and seventeen chronic, participated in the study. A 2-DoF robotic system was used for upper limb training. Two clinical scales were used for assessment. Forces and velocities at the robot's end-effector during the execution of upper limb planar reaching movements were measured. Clinical outcome measures show a significant decrease in motor impairment after the treatment both in chronic and subacute patients (MSS-SE, p<0.001; FM, p<0.05). Movement velocity increases after the robot-aided treatment in both groups. Mean values of forces exerted by subacute patients are lower than those observed in chronic patients, both at the beginning and at the end of robotic treatment, as in the latter the pathological pattern is already structured. Our results demonstrate that the monitoring of the forces exerted on the end-effector during robot-aided treatment can identify the specific motor recovery mechanisms at different stages. If the pathological pattern is not yet structured, rehabilitative interventions should be addressed toward the use of motor re-learning procedures; on the other hand, if the force analysis shows a strong pathological pattern, mechanisms of compensation should be encouraged.