Abstract
Subjective clinical rating scales represent the gold-standard for diagnosis of motor function following stroke. In practice however, they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution. Automated systems have been proposed for empirical quantification but have not significantly impacted clinical practice. We address translational challenges in this arena through: (1) implementation of a novel sensor suite combining inertial measurement and mechanomyography (MMG) to quantify hand and wrist motor function; and (2) introduction of a new range of signal features extracted from the suite to supplement predicted clinical scores. The wearable sensors, signal features, and machine learning algorithms have been combined to produce classified ratings from the Fugl-Meyer clinical assessment rating scale. Furthermore, we have designed the system to augment clinical rating with several sensor-derived supplementary features encompassing critical aspects of motor dysfunction (e.g. joint angle, muscle activity, etc.). Performance is validated through a large-scale study on a post-stroke cohort of 64 patients. Fugl-Meyer Assessment tasks were classified with 75% accuracy for gross motor tasks and 62% for hand/wrist motor tasks. Of greater import, supplementary features demonstrated concurrent validity with Fugl-Meyer ratings, evidencing their utility as new measures of motor function suited to automated assessment. Finally, the supplementary features also provide continuous measures of sub-components of motor function, offering the potential to complement low accuracy but well-validated clinical rating scales when high-quality motor outcome measures are required. We believe this work provides a basis for widespread clinical adoption of inertial-MMG sensor use for post-stroke clinical motor assessment.
Highlights
S TROKE is the second largest cause of death globally and the second biggest cause of years lost prematurely or living with disability [1]
The first novel contribution of this paper is the development of a system of motor function post-stroke which combines a classified clinical rating score with several sensor-derived supplementary features
Classification performance was found to be broadly comparable to the wider literature which testing a much less controlled environment
Summary
S TROKE is the second largest cause of death globally and the second biggest cause of years lost prematurely or living with disability [1]. The standard clinical rating scales of motor function for stroke are widely used for monitoring subject improvement and defining rehabilitation requirements. These rating scales form the gold standard method of quantifying motor function in research applications. One of the most commonly used and widely validated [5] standard clinical rating scales is the Fugl-Meyer Assessment (FMA) [6]. Each motor component/task is assigned a qualitative rating depending on how well it was performed with a range from 0 to 2. This range covers no movement/function (0), partial movement/function (1), and full movement/function (2)
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