Computer vision for kinematic metrics of the drinking task in a pilot study of neurotypical participants

Abstract

Assessment of the upper limb is critical to guiding the rehabilitation cycle. Drawbacks of observation-based assessment include subjectivity and coarse resolution of ordinal scales. Kinematic assessment gives rise to objective quantitative metrics, but uptake is encumbered by costly and impractical setups. Our objective was to investigate feasibility and accuracy of computer vision (CV) for acquiring kinematic metrics of the drinking task, which are recommended in stroke rehabilitation research. We implemented CV for upper limb kinematic assessment using modest cameras and an open-source machine learning solution. To explore feasibility, 10 neurotypical participants were recruited for repeated kinematic measures during the drinking task. To investigate accuracy, a simultaneous marker-based motion capture system was used, and error was quantified for the following kinematic metrics: Number of Movement Units (NMU), Trunk Displacement (TD), and Movement Time (MT). Across all participant trials, kinematic metrics of the drinking task were successfully acquired using CV. Compared to marker-based motion capture, no significant difference was observed for group mean values of kinematic metrics. Mean error for NMU, TD, and MT were - 0.12 units, 3.4 mm, and 0.15 s, respectively. Bland-Altman analysis revealed no bias. Kinematic metrics of the drinking task can be measured using CV, and preliminary findings support accuracy. Further study in neurodivergent populations is needed to determine validity of CV for kinematic assessment of the post-stroke upper limb.

Keywords: Biomechanics; Human pose estimation; Machine learning; Markerless motion capture; Neurorehabilitation; Upper extremity.

Fig. 1

Drinking task activity. Progression of the drinking task activity through five phases as demonstrated via still images extracted from a single trial for one participant.

Fig. 2

Laboratory setup for data collection. Featuring a 5-camera motion capture system and a dual camera computer vision system, simultaneous recordings of the drinking task activity were captured.

Fig. 3

Data acquisition. For each participant, time-series of 3D joint position data during the drinking task was acquired using both the computer vision system (CV) and a gold-standard marker-based motion capture system (MB-MoCap) (panel A). By post-processing this joint position data, relevant kinematic metrics could be obtained including a metric to quantify movement quality (panel B) and metrics to quantify movement compensation (panel C and panel D).

Fig. 4

Comparing methods for measuring post-stroke upper limb kinematics. Comparison of mean kinematic metrics reveals no significant difference between the computer vision system (CV) and marker-based motion capture system (MB-MoCap).

Fig. 5

Bland–Altman analysis. For each kinematic metric, Bland–Altman plots provide a comparison of potential bias in computer vision system, which is represented by comparison of the mean difference line (solid red line) to the line of equality (solid blue line). Additionally, the random error of the data is illustrated by the limits of agreement (dotted green lines) and confidence intervals for both mean difference and limits of agreement are shown in shaded bands (red band and green band, respectively). Due to considerable overlapping of data points when plotting NMU (left), a “jitter plot” option has been applied to reveal the individual data points.