A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm

TL;DR

The bimanual arm reaching test with a robot (BARTR) is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse and shows promise for determining the efficacy of interventions designed to reduce paretic arm non use and enhance functional recovery after stroke.

Abstract

An over-reliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the Bimanual Arm Reaching Test with a Robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that utilizes a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: it is valid, reliable, and simple to use.

Figures (6)

• Figure 1: Example reaching trial with the BARTR apparatus. The participant places hands on the home position device. The socially assistive robot (SAR, on the left) describes the mechanics of the BARTR, and the robot arm (on the right) moves the button to different target locations in front of the participant. A reaching trial begins when the button lights up, and the SAR cues the participant to move.
• Figure 2: Normative data collected from neurotypical participants. The normative data consists of hand choice in the spontaneous condition (A) and average time for participants to reach with their left hand (B) and their right hand (C). Lighter colors indicate high probability of participants choosing their right hand (A) or faster times to reach (B, C)
• Figure 3: Comparisons of data collected from two participants. P23 was right-dominant affected, and showed lower right arm use (A) as well as longer reaching times (C). P31 was left non-dominant affected and showed more balanced right arm use (B) and faster reaching times (D). Raw data is shown for arm choice data, with a red 'x' denoting right hand reaches, and an orange triangle denoting left hand reaches (A,B).
• Figure 4: Evaluations of the proposed metric. We demonstrate the Bimanual Arm Reaching Task with a Robot (BARTR) metrics validity through its correlation with clinical measurements of nonuse through a non-parametric Spearman correlation, $r(13)=.693$, $p=.016$ (A). We demonstrate reliability with the absolute agreement of BARTR scores across three sessions through the intraclass correlation coefficient, $ICC(1,k)=.908$, $p<.001$ (B). We demonstrate its ease of use through usability ratings of the system, showing that the average rating is above 72.6 through a non-parametric Wilocoxon signed-rank test, $Z=16.0$, $p=.040$ (C).
• Figure 5: Qualitative responses from participants. We show overall perceptions of each of the four factors of trust kellmeyer2018social that each participant mentioned.