Quantitative Assessment of Friedreich Ataxia via Self-Drinking Activity

Multifeature quantitative motor assessment of upper limb ataxia including drawing and reaching

OBJECTIVE

Voluntary upper limb movements are an ecologically important yet insufficiently explored digital-motor outcome domain for trials in degenerative ataxia. We extended and validated the trial-ready quantitative motor assessment battery "Q-Motor" for upper limb movements with clinician-reported, patient-focused, and performance outcomes of ataxia.

METHODS

Exploratory single-center cross-sectional assessment in 94 subjects (46 cross-genotype ataxia patients; 48 matched controls), comprising five tasks measured by force transducer and/or position field: Finger Tapping, diadochokinesia, grip-lift, and-as novel implementations-Spiral Drawing, and Target Reaching. Digital-motor measures were selected if they discriminated from controls (AUC >0.7) and correlated-with at least one strong correlation (rho ≥0.6)-to the Scale for the Assessment and Rating of Ataxia (SARA), activities of daily living (FARS-ADL), and the Nine-Hole Peg Test (9HPT).

RESULTS

Six movement features with 69 measures met selection criteria, including speed and variability in all tasks, stability in grip-lift, and efficiency in Target Reaching. The novel drawing/reaching tasks best captured impairment in dexterity (|rho9HPT| ≤0.81) and FARS-ADL upper limb items (|rhoADLul| ≤0.64), particularly by kinematic analysis of smoothness (SPARC). Target hit rate, a composite of speed and endpoint precision, almost perfectly discriminated ataxia and controls (AUC: 0.97). Selected measures in all tasks discriminated between mild, moderate, and severe impairment (SARA upper limb composite: 0-2/>2-4/>4-6) and correlated with severity in the trial-relevant mild ataxia stage (SARA ≤10, n = 20).

INTERPRETATION

Q-Motor assessment captures multiple features of impaired upper limb movements in degenerative ataxia. Validation with key clinical outcome domains provides the basis for evaluation in longitudinal studies and clinical trial settings.

A Study of Upper-Limb Motion using Kinematic Measures for Clinical Assessment of Cerebellar Ataxia

Cerebellar Ataxia (CA) is a group of diseases affecting the cerebellum, which is responsible for movement coordination. It causes uncoordinated movements and can also impact balance, speech, and eye movements. There are no approved disease-modifying medications for CA, so clinical studies to assess potential treatments are crucial. These studies require robust, objective measurements of CA severity to reflect changes in the progression of the disease due to medication. In recent years, studies have used kinematic measures to evaluate CA severity, but the current method relies on subjective clinical observations and is insufficient for telehealth. There is a need for a non-intrusive system that can monitor people with CA regularly to better understand the disease and develop an automated assessment system. In this study, we analyzed kinematic measures of upper-limb movements during a ballistic tracking test, which primarily involves movements at the shoulder joint. We aimed to understand the challenges of identifying CA and evaluating its severity when measuring such movements. Statistical features of the kinematic signals were used to develop machine learning models for classification and regression. The Gradient Boosting Classifier model had a maximum accuracy of 74%, but the models had low specificity and performed poorly in regression, suggesting that kinematic measures from shoulder-dominated movements during ballistic tracking are not as viable for CA assessment as other measures.

A Novel Feature from Instrumented Utensils for Clinical Assessment of Friedreich Ataxia

Friedreich Ataxia (FRDA) is an inherited disorder that affects the cerebellum and other regions of the human nervous system. It causes impaired movement that affects quality and reduces lifespan. Clinical assessment of movement is a key part of diagnosis and assessment of severity. Recent studies have examined instrumented measurement of movement to support clinical assessments. This paper presents a frequency domain approach based on Average Band Power (ABP) estimation for clinical assessment using Inertial Measurement Unit (IMU) signals. The IMUs were attached to a 3D printed spoon and a cup. Participants used them to mimic eating and drinking activities during data collection. For both activities, the ABP of frequency components from individuals with FRDA clustered in 0 to 0.2Hz band. This suggests that the ABP of this frequency is affected by FRDA irrespective of the device or activity. The ABP in this frequency band was used to distinguish between FRDA and non-ataxic participants using the Area Under the Receiver-Operating-Characteristic Curve (AUC) which produced peak values greater than 0.8. The machine learning models (logistic regression and neural networks) produced accuracy greater than 80% with these features common to both devices.

Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-crafted Feature Extractor

Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA. Although these approaches achieved high accuracy, large scale deployment will require large clinics and raises privacy concerns. In this study, we propose an image transformation-based approach to leverage the advantages of state-of-the-art deep learning with federated learning in diagnosing CA. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored. Experimental results indicate that the recurrence plot yields the highest validation accuracy (86.69%) with MobileNetV2 model in diagnosing CA. The proposed scheme provides a practical solution with high diagnosis accuracy, removing the need for feature engineering and preserving data privacy for a large-scale deployment.