JMIR Publications

ABSTRACT

Background:

Instrumented motion analysis has emerged as a promising tool to augment clinical assessment of motor symptoms in several disorders. However, to date a transition to wide application in clinical routine has not been achieved. A crucial step on this path is to implement standardized, clinically applicable tools that identify and control for quality concerns.

Objective:

The main goal of this study comprises the development and experimental deployment of a systematic quality control procedure for data collected with an instrumented motion analysis system.

Methods:

We developed a post-hoc quality control pipeline for a camera-based motion analysis system. The pipeline was evaluated using a large set of short motor task recordings of healthy controls (2010 recordings from 162 subjects) and people with multiple sclerosis (2682 recordings from 187 subjects). For each of these recordings, two raters independently applied the quality control pipeline. They provided an overall usability rating for each recording and identified technical and performance-related quality concerns that were quantified in subsequent analyses.

Results:

The approach developed here has proven user-friendly and applicable at a large scale. Raters’ decisions on recording usability were concordant in 71.5%-92.3% of cases, depending on the motor task. Furthermore, 39.6-85.1% of recordings were concordantly rated as being of satisfactory quality while in 5.0%-26.3%, both raters agreed to discard the recording.

Conclusions:

Results confirm the need of quality control for data from instrumented motion analysis despite standard test set-up, testing protocol and operator training. We present a quality control pipeline that seems feasible and useful for instant quality screening in the clinical setting. Results of the quality control process can be used to clean existing datasets, optimize quality assurance measures as well as foster the development of automated quality control approaches and therefore improve the overall reliability of kinematic datasets.