Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) share several phenotypical characteristics, which can be clinically hard to distinguish. To combine quantified movement information from three tests obtained from inertial measurements units (IMUs), to improve the classification of EOA and DCD patients and healthy controls compared to using a single test. Using IMUs attached to the upper limbs, we collected data from EOA, DCD and healthy control children while they performed the three upper limb tests (finger to nose, finger chasing and fast alternating movements) from the Scale for the Assessment and Rating of Ataxia (SARA) test. The most relevant features for classification were extracted. A random forest classifier with 300 trees was used for classification. The area under the receiver operating curve (ROC-AUC) and precision-recall plots were used for classification performance assessment. The most relevant discerning features concerned smoothness and velocity of movements. Classification accuracy on group level was 85.6% for EOA, 63.5% for DCD and 91.2% for healthy control children. In comparison, using only the finger to nose test for classification 73.7% of EOA and 53.4% of DCD patients and 87.2% of healthy controls were accurately classified. For the ROC/precision recall plots the AUC was 0.96/0.89 for EOA, 0.92/0.81 for DCD and 0.97/0.94 for healthy control children. Using quantified movement information from all three SARA-kinetic upper limb tests improved the classification of all diagnostic groups, and in particular of the DCD group compared to using only the finger to nose test.

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.

Background: A lack of consensus exists in linking demographic, behavioral, and cognitive characteristics to biological stages of dementia, defined by the ATN (amyloid, tau, neurodegeneration) classification incorporating amyloid, tau, and neuronal injury biomarkers.Methods: Using a random forest classifier we investigated whether 27 demographic, behavioral, and cognitive characteristics allowed distinction between ATN‐defined groups with the same cognitive profile. This was done separately for three cognitively unimpaired (CU) (112 A‐T‐N‐; 46 A+T+N+/−; 65 A‐T+/‐N+/−) and three mild cognitive impairment (MCI) (128 A‐T‐N‐; 223 A+T+N+/−; 94 A‐T+/‐N+/−) subgroups.Results: Classification‐balanced accuracy reached 39% for the CU and 52% for the MCI subgroups. Logical Delayed Recall (explaining 16% of the variance), followed by the Alzheimer's Disease Assessment Scale 13 (14%) and Everyday Cognition Informant (10%), were the most relevant characteristics for classification of the MCI subgroups. Race and ethnicity, marital status, and Everyday Cognition Patient were not relevant (0%).Conclusions: The demographic, behavioral, and cognitive measures used in our model were not informative in differentiating ATN‐defined CU profiles. Measures of delayed memory, general cognition, and activities of daily living were the most informative in differentiating ATN‐defined MCI profiles; however, these measures alone were not sufficient to reach high classification performance.

Long-term tremor recording is particularly useful for the assessment of overall severity and therapeutic interventions in tremor patients. The purpose of this paper is to investigate the optimal number of days needed to obtain reliable estimates of tremor percentage, tremor frequency variability and tremor intensity in tremor patients using long-term tremor recordings. Participants were 18 years or older and were diagnosed with tremor by a movement disorders specialist. Participants wore an accelerometer on the wrist of the most affected arm during 30 consecutive days. Tremor presence, frequency variability and intensity were calculated per day. We used reliability analysis to determine the minimum number of days needed to obtain reliable estimates of these tremor characteristics. Data from 36 adult organic (OrgT) and functional tremor (FT) patients (24 males; mean age 63.9 ± 11.9 years; 15 FT) were analyzed. Using five hours per day, one day of measurement is enough, except for tremor frequency variability in the OrgT group, where three days are needed and for tremor intensity where two days are always needed. Visual analysis suggested that reliability can be increased considerably by using data from three days instead of one day even when using six hours of data per day. Three days with at least three hours of tremor data provide estimates of tremor percentage, frequency variability and intensity with good to excellent reliability, both for organic and functional tremor.

The Romberg test is widely used for static postural balance assessment, but the effect of foot position on center of pressure (CoP) parameters remains unclear. This study compared CoP parameters in 47 participants aged 13 to 71 years, with two foot positions: a comfortable stance and feet together. This broad age range was used to capture significant diversity in physical development, balance, and health conditions, ensuring results representative of variability in a general population. Participants performed the test with eyes open (EO) and closed (EC). CoP parameters such as mean velocity, range, standard deviation, area of the 95% confidence ellipse, mean frequency, and Romberg ratio were measured. Paired t-tests were used to evaluate the effect of vision and foot position. The results showed that both vision and foot position significantly influenced postural stability, with greater sway in the EC and feet together conditions, especially in the mediolateral direction. For example, mean velocity increased from 10.02 ± 4.09 mm/s in EO-comfortable stance to 21.75 ± 8.93 mm/s in EC-feet together. These findings highlight the importance of standardizing foot position and considering the effect of vision when assessing postural stability.

Purpose Early detection of cognitive decline is essential for timely diagnosis and treatment. This study aimed to evaluate whether upper-limb movement dynamics derived from wrist-mounted inertial sensors during a fine motor task can accurately differentiate among healthy aging, mild cognitive impairment (MCI), and dementia (DEM). Methods The study recruited young and older participants. Cognitive status was established through clinical evaluation together with concordance across three standardized neurocognitive assessments (MMSE, MoCA, and NEUROPSI). Upper-limb dynamics during the Grooved Pegboard Test (GPT) were recorded using a wrist-mounted triaxial BioHarness accelerometer. Time- and frequency-domain features were extracted from the accelerometry signals, and uncorrelated linear discriminant analysis (ULDA) was applied for supervised dimensionality reduction. Classification performance was evaluated using support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) classifiers using accuracy and area under the receiver operating characteristic curve (AUC-ROC). Results The study included 109 participants distributed into four groups: 30 young healthy subjects (YNG), 22 cognitively intact older adults (CIO), 27 participants with MCI, and 30 with DEM. Prior to dimensionality reduction, the SVM classifier achieved the strongest performance, reaching a macro accuracy of 0.88 ± 0.013, demonstrating meaningful discrimination using the original feature representation. ULDA-based projection further improved class separability across all evaluated models. RF + ULDA and SVM + ULDA achieved macro accuracies of 0.97, whereas KNN + ULDA achieved 0.94. Conclusion Fine motor dynamics measured during the GPT contain discriminative information associated with progressive cognitive decline and may support objective, wearable-sensor-based cognitive screening. Trial registration The Local Ethics Committee of the General Hospital of Mexico approved the study, “Dr. Eduardo Liceaga” (protocol code DI/23/110-B/03/12).