Upper limb movement characteristics of children and youth with dyskinetic cerebral palsy – A sensor approach

Abstract

Introduction:Children with dyskinetic cerebral palsy (DCP) suffer from involuntary movements, reflected in abnormal postures and jerky, fragmented movements (1). The severity of their motor abnormalities is currently evaluated with qualitative evaluation scales which are subjective and time-consuming. To objectify the deviating movement patterns of children with DCP, inertial sensors provide a userfriendly and cheap alternative. Inertial sensors have previously been successfully used to differentiate pathological and non-pathological populations (2-4). Research Question:Can inertial sensors be used to detect deviating movement patterns in participants with dyskinetic cerebral palsy during a reach forward task? Methods:Nine participants with DCP (mean age 17y 6m, range 11-25y) were included, with five bilateral participants leading to 14 DCP measurements. For comparison, 14 age-matched TD children were included (mean age 17y2m, range 12-23y). Participants were asked to perform 10 repetitions of a reach forward task with sensors attached on the sternum, scapula, upper arm, wrist and hand. Root mean square (RMS) and maximal values of acceleration, angular velocity, acceleration jerk and angular jerk were calculated from raw sensor measurements and sample entropy was calculated for the acceleration and angular velocity signal. Mann-Whitney-U tests were used to explore differences between TD participants and the participants with DCP.

Identifying Internal and External Shoulder Rotation Using a Kirigami-Based Shoulder Patch

“…These approaches require a welldefined movement pattern, and thus, prior work has focused mainly on lower extremity applications given the cyclical gait pattern. There has been limited use of SPM analyses to measure shoulder kinematics before this study [23][24][25][26][27] . While this SPM approach works well for comparing tightly controlled shoulder movements like in the current study, this may not always be the case with shoulder movements, given the vast workspace in which to move and generate force with the upper extremity 28 .…”

Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study

“…Although the population of Parkinson's disease and dyskinetic CP are not directly comparable, it indicates the potential of the proposed methodology for individuals with dyskinetic CP. A current study suggested that IMUs can be used as a mobile alternative for marker-based motion capture (omitting the need for an advanced movement laboratory) within upper extremity movements analysis of standardize movements in dyskinetic CP [14]. The proposed methodology goes one step further by using home-based collected IMUs data within unstandardized situations.…”

“…However, the collection of data for 3D kinematics requires advanced motion capture systems, which do not allow outsidelab measurements. To enable measurements at home and in daily life environments, for CP, there is an increasing interest in using simpler systems for data collection, such as videobased markerless motion tracking (e.g., OpenPose) [12] and Inertial Measurements Units (IMUs) [13,14]. These easily applicable measurement systems, combined with machine learning models trained by algorithms (e.g., traditional such as logistic regression, random forest, support vector machine, or deep learning algorithms) may significantly contribute to the early detection of CP [15] and the monitoring of daily life functions [12,13].…”