Assessing upper extremity motor function in practice of virtual activities of daily living

Abstract

A study was conducted to investigate the criterion validity of measures of upper extremity (UE) motor function derived during practice of virtual activities of daily living (ADLs). Fourteen hemiparetic stroke patients employed a Virtual Occupational Therapy Assistant (VOTA), consisting of a high-fidelity virtual world and a Kinect™ sensor, in four sessions of approximately one hour in duration. An unscented Kalman Filter-based human motion tracking algorithm estimated UE joint kinematics in real-time during performance of virtual ADL activities, enabling both animation of the user's avatar and automated generation of metrics related to speed and smoothness of motion. These metrics, aggregated over discrete sub-task elements during performance of virtual ADLs, were compared to scores from an established assessment of UE motor performance, the Wolf Motor Function Test (WMFT). Spearman's rank correlation analysis indicates a moderate correlation between VOTA-derived metrics and the time-based WMFT assessments, supporting the criterion validity of VOTA measures as a means of tracking patient progress during an UE rehabilitation program that includes practice of virtual ADLs.

Figure 1

User performing a virtual ADL activity.

Figure 2

Examples of elements of the VOTA Meal Preparation activity: (a) virtual OT introduces patient to the VOTA virtual kitchen, (b) Bring Second Slice to Toaster sub-task performed in “activity mode,” and (c) Put Pan in Sink sub-task.

Figure 3

VOTA is organized a hierarchy of activities, tasks, and sub-tasks that are categorized as motor (MOTORSCORE), cognitive (COGSCORE), or unscored elements.

Figure 4

UE motion can be defined as a series of Euler angle rotations applied sequentially about Axes 1 to 4. The illustrated pose represents all four angles set to zero.

Figure 5

UE tracking data captured during virtual ADL performance is parsed by sub-task and used to generate motor performance metrics that are then aggregated over an entire session.

Figure 6

Examples of Euler angles and rates generated by real-time UE tracking of a stroke patient during performance of three different virtual ADL sub-tasks. (a) Bring Pan To Stove generates active shoulder extension/flexion, shoulder internal rotation, and elbow flexion. (b) Bring Second Slice To Toaster produces significant active shoulder external rotation. (c) Put Pan In Sink elicits active shoulder horizontal abduction with active shoulder external rotation and elbow flexion.

Figure 7

Mean subject-specific VOTA sub-task duration for the Meal Preparation activity. Circles denote the estimated mean duration, and vertical lines denote the 95% confidence interval for mean task duration.