Publications by authors named "Pubudu Pathirana"

Objective: Rehabilitation is thought to reduce ataxia severity in individuals with hereditary cerebellar ataxia (HCA). This multicenter, randomized controlled superiority trial aimed to examine the efficacy of a 30-week goal-directed rehabilitation program compared with 30 weeks of standard care on function, ataxia, health-related quality of life, and balance in individuals with an HCA.

Methods: Individuals with an autosomal dominant or recessive ataxia (aged ≥15 years) were enrolled at 5 sites in Australia.

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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.

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Recent studies have illuminated the potential of harnessing the power of Deep Learning (DL) and the Internet of Health Things (IoHT) to detect a variety of disorders, particularly among patients in the middle to later stages of the disease. The utilization of time series data has proven to be a valuable asset in this endeavour. However, the development of effective DL architectures for time series classification with limited data remains a critical gap in the field.

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Due to its advantages in numerous industries, including healthcare, sports, rehabilitation, and wearable electronics, gender recognition has garnered a lot of attention in the last ten years. The gender recognition method described in this study uses a wearable sensor device with inertial measurement units to record a variety of activities. The system consists of five sensors that are mounted to the upper and lower bodies while performing seven standing, walking, and climbing exercises that are meant to replicate daily activity.

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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.

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Friedreich ataxia (FRDA) requires an objective measure of severity to overcome the shortcoming of clinical scales when applied to trials for treatments. This is hindered due to the rarity of the disease resulting in small datasets. Further, the published quantitative measures for ataxia do not incorporate or underutilise expert knowledge.

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Cerebellar Ataxia (CA) is a neurological condition that affects coordination, balance and speech. Assessing its severity is important for developing effective treatment and rehabilitation plans. Traditional assessment methods involve a clinician instructing a person with ataxia to perform tests and assigning a severity score based on their performance.

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The assessment of speech in Cerebellar Ataxia (CA) is time-consuming and requires clinical interpretation. In this study, we introduce a fully automated objective algorithm that uses significant acoustic features from time, spectral, cepstral, and non-linear dynamics present in microphone data obtained from different repeated Consonant-Vowel (C-V) syllable paradigms. The algorithm builds machine-learning models to support a 3-tier diagnostic categorisation for distinguishing Ataxic Speech from healthy speech, rating the severity of Ataxic Speech, and nomogram-based supporting scoring charts for Ataxic Speech diagnosis and severity prediction.

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Cerebellar ataxia (CA) refers to the incoordination of movements of the eyes, speech, trunk, and limbs caused by cerebellar dysfunction. Conventional machine learning (ML) utilizes centralised databases to train a model of diagnosing CA. Despite the high accuracy, these approaches raise privacy concern as participants' data revealed in the data centre.

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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.

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Cerebellar ataxia (CA) is defined by disrupted coordination of movement suffering from disease of the cerebellum. It reflects fragmented movements of the eyes, vocal, upper limbs, balance, gait, and lower limbs. This study aims to use a motion sensor to form a simple yet effective CA quantitative assessment framework.

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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic.

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The monitoring of disease progression in certain neurodegenerative conditions can significantly be quantified with the help of objective assessments. The severity assessment of diseases like Friedreich ataxia (FRDA) are usually based on different subjective measures. The ability of a participant with FRDA to perform standard neurological tests is the most common way of assessing disease progression.

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Cerebellar dysfunction results in impairments in co-ordination or 'ataxia'. Bedside examination of cerebellar function has changed little since the early nineteenth century with the exception being the oculomotor examination which has become instrumented. Otherwise, competence and confidence in performing the clinical assessment relies heavily on the skill and experience of the clinician.

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Effective monitoring of the progression of neurodegenerative conditions can be significantly improved by objective assessments. Clinical assessments of conditions such as Friedreich's Ataxia (FA), currently rely on subjective measures commonly practiced in clinics as well as the ability of the affected individual to perform conventional tests of the neurological examination. In this study, we propose an ataxia measuring device, in the form of a pressure canister capable of sensing certain kinetic and kinematic parameters of interest to quantify the impairment levels of participants particularly when engaged in an activity that is closely associated with daily living.

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Article Synopsis
  • SARS-CoV-2 is a dangerous virus responsible for the COVID-19 pandemic, making it important to understand its evolution and transmission to inform effective response strategies.
  • The paper examines genomic mutations in the virus, specifically focusing on the well-studied D614G mutation in the spike protein, which appears to have little effect on the virus's protein structure.
  • An extensive dataset of over 6,300 viral genome sequences was created, highlighting stable genes that could be promising targets for vaccine and drug development.
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Upper limb function for people with Friedreich ataxia determines capacity to participate in daily activities. Current upper limb measures available do not fully capture impairments related to Friedreich ataxia. We have developed an objective measure, the Ataxia Instrumented Measure-Spoon (AIM-S), which consists of a spoon equipped with a BioKin wireless motion capture device, and algorithms that analyse these signals, to measure ataxia of the upper limb during the pre-oral phase of eating.

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Background: Cerebellar ataxia refers to the disturbance in movement resulting from cerebellar dysfunction. It manifests as inaccurate movements with delayed onset and overshoot, especially when movements are repetitive or rhythmic. Identification of ataxia is integral to the diagnosis and assessment of severity, and is important in monitoring progression and improvement.

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Cerebellar ataxia (CA) refers to the disordered movement that occurs when the cerebellum is injured or affected by disease. It manifests as uncoordinated movement of the limbs, speech, and balance. This study is aimed at the formation of a simple, objective framework for the quantitative assessment of CA based on motion data.

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Cerebellar Ataxia is a neurological disorder without an approved treatment. Patients will have impaired and uncoordinated motor functionality making them unable to complete their day-to-day activities. Ataxia clinics are established around the world to facilitate research and rehabilitate patients.

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Human Activity Recognition (HAR) is a central unit to understand and predict human behavior. HAR has been used to estimate the levels of a sedentary, monitor lifestyle habits, track the levels of people's health, or build a recommendation system. Many researchers have utilized the inertial measurement unit as an input tool to explore the HAR land.

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Cerebellar ataxia (CA) refers to the impaired balance and coordination resulting from injury or degeneration of the cerebellum. Testing balance is one of the simplest means of assessing CA. This study compares instrumented assessment and clinical assessment scales of the balance test called Romberg's test.

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Friedreich ataxia (FRDA), the most common of the inherited ataxias, is a degenerative disease that progressively affects walking and other functions leading to significant impairment associated with a shortened lifespan. It is important to monitor the progression of ataxia over periods of time for clinical and therapeutic interventions. This study was aimed at investigating the use of our instrumented measurement scheme of utilizing a motion detecting spoon in a self-feeding activity to quantify the longitudinal effect of FRDA on upper limb function.

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Cerebellar ataxia (CA) results from injury to or disease of the cerebellum. It describes the resulting motor dysfunction, characterised by inaccuracy, incoordination and delay in initiation of movement, tremor, and imbalance. Assessment of ataxia to diagnose and monitor progress is by clinical observance of the performance of standard motor tasks.

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The progression of neurodegenerative conditions can be effectively monitored and improved by using objective assessments. The conditions such as Friedreich Ataxia (FA) are clinically assessed by means of subjective measures commonly practised in clinics. Here, we propose a device capable of measuring ataxia, in the form of a `cup' capable of sensing certain kinematic parameters of interest while engaging in an activity that is closely related to daily living.

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