Quantitative Assessment of Friedreich Ataxia via Self-Drinking Activity

Krishna, Ragil; Pathirana, Pubudu N.; Horne, Malcolm; Corben, Louise A.; Szmulewicz, David J.

doi:10.1109/jbhi.2021.3069007

Cited by 6 publications

(7 citation statements)

References 47 publications

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“…However, the second setting, which is the measurement of the severity of established CA (Section III-B, iii) and its progression, has received the most interest because objective measures would be helpful in clinical trials. It is important for ML approaches to distinguish between these settings because the feature sets that establish the presence of CA may differ from those features that change with increasing disease severity [22].…”

Section: B What Are the Signs Of Ca?mentioning

confidence: 99%

“…In addition to body worn sensors [34], [47], we have developed hardware in the forms of a cup [22], [83] and a spoon [80]- [82] to examine ataxia through routine daily movements (as opposed to the specific movements which are based on the cerebellar bedside examination). Inspired by devices of daily use, the primary focus is the objective assessment of CA while completing usual activities of daily living.…”

Section: A Assessment Via Routine Movement and Beyond Task Modelingmentioning

confidence: 99%

“…These devices have the potential for being used in the home, an option that a clinic setup is unable to deliver. Ecologically relevant activities such as using a spoon and cup are functionally relevant and demonstrate high performance; 88% segregation rate between individuals with and without ataxia, and 0.72 correlation with clinical scales [22], [80]. Nevertheless, this research may be viewed as limited, in that it is still adhering to specific, pre-defined tasks such as feeding and drinking.…”

Section: A Assessment Via Routine Movement and Beyond Task Modelingmentioning

confidence: 99%

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Technological Evolution in the Instrumentation of Ataxia Severity Measurement

et al. 2023

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Cerebellar ataxia is the poorly coordinated movement that results from injury or disease affecting the cerebellum. The diagnosis and assessment of ataxia are significantly challenging due to dependency on clinicians' experience and the attendant subjectivity of such a process. In recent years, neuroimaging and sensor-based approaches, supported by effective machine learning techniques have made advances in the pursuit of addressing these clinical challenges. In this work, we present an outline of approaches to applying machine learning to this clinical challenge. We first provide a fundamental clinical overview with practical problems and then from a machine learning perspective, outline possible approaches with which to address these clinical challenges. Also discussed are the limitations in existing methods, the provision of cross disciplinary approaches and the current state-of-the-art as a potential basis for future research.

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Section: B What Are the Signs Of Ca?mentioning

confidence: 99%

Section: A Assessment Via Routine Movement and Beyond Task Modelingmentioning

confidence: 99%

Section: A Assessment Via Routine Movement and Beyond Task Modelingmentioning

confidence: 99%

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Technological Evolution in the Instrumentation of Ataxia Severity Measurement

et al. 2023

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“…Compared to the gait and stance domain, digital‐motor assessment of upper limb movements has been much less studied in ataxias. Moreover, without a consensus on how to best measure (the construct of) “appendicular ataxia,” very different technologies, motor tasks, and conceptual frameworks have been used 9–14 . Within the International Classification of Functioning, Disability and Health (ICF) , appendicular ataxia is related to the “control of voluntary movement functions,” including coordination of “simple, complex, and visually directed movements,” which enable activities of domestic life and self‐care such as washing oneself, caring for body parts, toileting, dressing, eating, and drinking 15 .…”

Section: Introductionmentioning

confidence: 99%

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

Hermle,

Schubert,

Barallon

et al. 2024

Ann Clin Transl Neurol

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ObjectiveVoluntary 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.MethodsExploratory 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).ResultsSix 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).InterpretationQ‐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.

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“…The systems described above modeled motor tasks assessed using ataxia clinical scales. Sensors have also been placed in common devices (spoon [31], cup [32], and mouse [33]) to measure kinematic parameters that are then used to produce measurements that distinguish control subjects and individuals with ataxia.…”

Section: Introductionmentioning

confidence: 99%

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

Ngo

Nguyen

Pathirana

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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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 Manuscript

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Quantitative Assessment of Friedreich Ataxia via Self-Drinking Activity

Cited by 6 publications

References 47 publications

Technological Evolution in the Instrumentation of Ataxia Severity Measurement

Technological Evolution in the Instrumentation of Ataxia Severity Measurement

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

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

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