Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review

Abstract

Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.

Keywords: Parkinson’s disease; ataxia; dystonia; huntington’s disease; inertial measurement unit (IMU); stroke; tremor; upper extremity.

FIGURE 1

Flowchart of article selection.

FIGURE 2

Number of studies included per goal population.

FIGURE 3

Infographic of sensor types and set-up. (A): Studies including a unilateral sensor-set-up. Studies are randomly split in left and right side of the body to improve interpretation. (B): Studies including a bilateral sensor set-up. Arrows have not been drawn to the contralateral side of the body to improve interpretation. (C): Sensor placement on the hand and wrist. Hands are randomly split to improve interpretation. Sensor placements on the lower limb are not presented, but can be found in Supplementary Table S1. IMU = inertial measurement unit; GYR = gyroscope; ACC = accelerometer.

FIGURE 4

Statistical method used in the included studies. The sum does not add up to 101 because multiple studies used more than one methodology.

FIGURE 5

Clinical application. The sum does not add up to 101 because multiple studies used more than one methodology.