Development of a program to determine optimal settings for robot-assisted rehabilitation of the post-stroke paretic upper extremity: a simulation study

Abstract

Robot-assisted therapy can effectively treat upper extremity (UE) paralysis in patients who experience a stroke. Presently, UE, as a training item, is selected according to the severity of the paralysis based on a clinician's experience. The possibility of objectively selecting robot-assisted training items based on the severity of paralysis was simulated using the two-parameter logistic model item response theory (2PLM-IRT). Sample data were generated using the Monte Carlo method with 300 random cases. This simulation analyzed sample data (categorical data with three difficulty values of 0, 1, and 2 [0: too easy, 1: adequate, and 2: too difficult]) with 71 items per case. First, the most appropriate method was selected to ensure the local independence of the sample data necessary to use 2PLM-IRT. The method was to exclude items with low response probability (maximum response probability) within a pair in the Quality of Compensatory Movement Score (QCM) 1-point item difficulty curve, items with low item information content within a pair in the QCM 1-point item difficulty curve, and items with low item discrimination. Second, 300 cases were analyzed to determine the most appropriate model (one-parameter or two-parameter item response therapy) to be used and the most favored method to establish local independence. We also examined whether robotic training items could be selected according to the severity of paralysis based on the ability of a person (θ) in the sample data as calculated by 2PLM-IRT. Excluding items with low response probability (maximum response probability) in a pair in the categorical data 1-point item difficulty curve was effective in ensuring local independence. Additionally, to ensure local independence, the number of items should be reduced to 61 from 71, indicating that the 2PLM-IRT was an appropriate model. The ability of a person (θ) calculated by 2PLM-IRT suggested that seven training items could be estimated from 300 cases according to severity. This simulation made it possible to objectively estimate the training items according to the severity of paralysis in a sample of approximately 300 cases using this model.

Figure 1

Robot-assisted self-training. The figure illustrates robot-assisted self-training using the ReoGo-J robotic upper extremity rehabilitation device.

Figure 2

Training items of the ReoGo-J upper extremity rehabilitation device. The ReoGo-J has 17 training tasks. In this study, we select 8 training tasks that used frequently by therapist in past randomized controlled study .

Figure 3

Robotic assistance at the different modes produced by ReoGo-J for voluntary movement. Guided mode: fully dependent on robotic assistance to complete training. Initiated mode: Requires voluntary movement only at the beginning of training. For the remainder of the training, full dependence on robotic assistance is required. Step-Initiated mode: Requires only a few voluntary movements and robot-dependent movements alternately to complete training. Follow-assist mode: Required above a certain level of voluntary movement in training while receiving low level of robotic assistance continuously. Free mode: Uses voluntary movement to complete training without requiring robotic assistance.

Figure 4

Flowchart: statistical analyses. Flowchart illustrating statistical analyses used in this study.

Figure 5

Distribution chart of various data. (a) Variance of sample data in this study simulated by Monte Carlo method. (b) Confirmation of monotonicity between quality of movement scores and the ability of a person for each sample data. (c) Distribution of item information across 61 items.

Figure 6

Selection of the appropriate items for each ability of a person (θ) in sample data. In the severe (a), moderate (b), or mild group (c), seven items were selected that had a relatively high probability of scoring 1 on the QCM score. In the severe (d), moderate (e), or mild group (f), seven items were selected that had a relatively high item information function.