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Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating of ataxia (SARA)) to classify EOA and DCD patients and typically developing (CTRL) children with better explainability than previous classification approaches. We collected data from 18 EOA, 14 DCD and 29 CTRL children, while executing both SARA gait tests. Inertial measurement units were used to acquire movement data, and a gait model was employed to derive meaningful features. We used a random forest classifier on 36 extracted features, leave-one-out-cross-validation and a synthetic oversampling technique to distinguish between the three groups. Classification accuracy, probabilities of classification and feature relevance were obtained. The mean classification accuracy was 62.9% for EOA, 85.5% for DCD and 94.5% for CTRL participants. Overall, the random forest algorithm correctly classified 82.0% of the participants, which was slightly better than clinical assessment (73.0%). The classification resulted in a mean precision of 0.78, mean recall of 0.70 and mean F1 score of 0.74. The most relevant features were related to the range of the hip flexion–extension angle for gait, and to movement variability for tandem gait. Our results suggest that classification, employing features representing different aspects of movement during gait and tandem gait, may provide an insightful tool for the differential diagnoses of EOA, DCD and typically developing children. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy (NIRS) is a non-invasive technique for measuring regional tissue haemoglobin (Hb) concentrations and oxygen saturation (rSO2). It may be used to monitor cerebral perfusion and oxygenation in patients at risk of cerebral ischemia or hypoxia, for example, during cardiothoracic or carotid surgery. However, extracerebral tissue (mainly scalp and skull tissue) influences NIRS measurements, and the extent of this influence is not clear. Thus, before more widespread use of NIRS as an intraoperative monitoring modality is warranted, this issue needs to be better understood. We therefore conducted a systematic review of published in vivo studies of the influence of extracerebral tissue on NIRS measurements in the adult population. Studies that used reference techniques for the perfusion of the intra- and extracerebral tissues or that selectively altered the intra- or extracerebral perfusion were included. Thirty-four articles met the inclusion criteria and were of sufficient quality. In 14 articles, Hb concentrations were compared directly with measurements from reference techniques, using correlation coefficients. When the intracerebral perfusion was altered, the correlations between Hb concentrations and intracerebral reference technique measurements ranged between |r| = 0.45–0.88. When the extracerebral perfusion was altered, correlations between Hb concentrations and extracerebral reference technique measurements ranged between |r| = 0.22–0.93. In studies without selective perfusion modification, correlations of Hb with intra- and extracerebral reference technique measurements were generally lower (|r| < 0.52). Five articles studied rSO2. There were varying correlations of rSO2 with both intra- and extracerebral reference technique measurements (intracerebral: |r| = 0.18–0.77, extracerebral: |r| = 0.13–0.81). Regarding study quality, details on the domains, participant selection and flow and timing were often unclear. We conclude that extracerebral tissue indeed influences NIRS measurements, although the evidence (i.e., correlation) for this influence varies considerably across the assessed studies. These results are strongly affected by the study protocols and analysis techniques used. Studies employing multiple protocols and reference techniques for both intra- and extracerebral tissues are therefore needed. To quantitatively compare NIRS with intra- and extracerebral reference techniques, we recommend applying a complete regression analysis. The current uncertainty regarding the influence of extracerebral tissue remains a hurdle in the clinical implementation of NIRS for intraoperative monitoring. The protocol was pre-registered in PROSPERO (CRD42020199053). [ABSTRACT FROM AUTHOR]

Overlapping phenotypic features between Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) can complicate the clinical distinction of these disorders. Clinical rating scales are a common way to quantify movement disorders but in children these scales also rely on the observer's assessment and interpretation. Despite the introduction of inertial measurement units for objective and more precise evaluation, special hardware is still required, restricting their widespread application. Gait video recordings of movement disorder patients are frequently captured in routine clinical settings, but there is presently no suitable quantitative analysis method for these recordings. Owing to advancements in computer vision technology, deep learning pose estimation techniques may soon be ready for convenient and low-cost clinical usage. This study presents a framework based on 2D video recording in the coronal plane and pose estimation for the quantitative assessment of gait in movement disorders. To allow the calculation of distance-based features, seven different methods to normalize 2D skeleton keypoint data derived from pose estimation using deep neural networks applied to freehand video recording of gait were evaluated. In our experiments, 15 children (five EOA, five DCD and five healthy controls) were asked to walk naturally while being videotaped by a single camera in 1280 × 720 resolution at 25 frames per second. The high likelihood of the prediction of keypoint locations (mean = 0.889, standard deviation = 0.02) demonstrates the potential for distance-based features derived from routine video recordings to assist in the clinical evaluation of movement in EOA and DCD. By comparison of mean absolute angle error and mean variance of distance, the normalization methods using the Euclidean (2D) distance of left shoulder and right hip, or the average distance from left shoulder to right hip and from right shoulder to left hip were found to better perform for deriving distance-based features and further quantitative assessment of movement disorders. [ABSTRACT FROM AUTHOR]

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life. [ABSTRACT FROM AUTHOR]