Developing an Instrumented Measure of Upper Limb Function in Friedreich Ataxia.

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. The aim of this study was to evaluate the AIM-S as a sensitive and functionally relevant clinical outcome for use in clinical trials. A prospective longitudinal study evaluated the capacity of the AIM-S to detect change in upper limb function over 48 weeks. Friedreich ataxia clinical severity, performance on the Nine-Hole Peg Test and Box and Block Test and responses to a purpose-designed questionnaire regarding acceptability of AIM-S were recorded. Forty individuals with Friedreich ataxia and 20 control participants completed the baseline assessment. Thirty individuals with Friedreich ataxia completed the second assessment. The sensitivity of the AIM-S to detect deterioration in upper limb function was greater than other measures. Patient-reported outcomes indicated the AIM-S reflected a daily activity and was more enjoyable to complete than other assessments. The AIM-S is a more accurate, less variable measure of upper limb function in Friedreich ataxia than existing measures. The AIM-S is perceived by individuals with Friedreich ataxia to be related to everyday life and will permit individuals who are non-ambulant to be included in future clinical trials.

A comprehensive scheme for the objective upper body assessments of subjects with cerebellar ataxia.

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. Ataxia is identified and assessed by clinicians observing subjects perform standardised movement tasks that emphasise ataxic movements. Our aim in this paper was to use data recorded from motion sensors worn while subjects performed these tasks, in order to make an objective assessment of ataxia that accurately modelled the clinical assessment. METHODS: Inertial measurement units and a Kinect© system were used to record motion data while control and ataxic subjects performed four instrumented version of upper extremities tests, i.e. finger chase test (FCT), finger tapping test (FTT), finger to nose test (FNT) and dysdiadochokinesia test (DDKT). Kinematic features were extracted from this data and correlated with clinical ratings of severity of ataxia using the Scale for the Assessment and Rating of Ataxia (SARA). These features were refined using Feed Backward feature Elimination (the best performing method of four). Using several different learning models, including Linear Discrimination, Quadratic Discrimination Analysis, Support Vector Machine and K-Nearest Neighbour these extracted features were used to accurately discriminate between ataxics and control subjects. Leave-One-Out cross validation estimated the generalised performance of the diagnostic model as well as the severity predicting regression model. RESULTS: The selected model accurately ([Formula: see text]) predicted the clinical scores for ataxia and correlated well with clinical scores of the severity of ataxia ([Formula: see text], [Formula: see text]). The severity estimation was also considered in a 4-level scale to provide a rating that is familiar to the current clinically-used rating of upper limb impairments. The combination of FCT and FTT performed as well as all four test combined in predicting the presence and severity of ataxia. CONCLUSION: Individual bedside tests can be emulated using features derived from sensors worn while bedside tests of cerebellar ataxia were being performed. Each test emphasises different aspects of stability, timing, accuracy and rhythmicity of movements. Using the current models it is possible to model the clinician in identifying ataxia and assessing severity but also to identify those test which provide the optimum set of data. Trial registration Human Research and Ethics Committee, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia (HREC Reference Number: 11/994H/16).

Quantitative assessment of cerebellar ataxia, through automated limb functional tests.

BACKGROUND: Cerebellar damage can often result in disabilities affecting the peripheral regions of the body. These include poor and inaccurate coordination, tremors and irregular movements that often manifest as disorders associated with balance, gait and speech. The severity assessment of Cerebellar ataxia (CA) is determined by expert opinion and is likely to be subjective in nature. This paper investigates automated versions of three commonly used tests: Finger to Nose test (FNT), test for upper limb Dysdiadochokinesia Test (DDK) and Heel to Shin Test (HST), in evaluating disability due to CA. METHODS: Limb movements associated with these tests are measured using Inertial Measurement Units (IMU) to capture the disability. Kinematic parameters such as acceleration, velocity and angle are considered in both time and frequency domain in three orthogonal axes to obtain relevant disability related information. The collective dominance in the data distributions of the underlying features were observed though the Principal Component Analysis (PCA). The dominant features were combined to substantiate the correlation with the expert clinical assessments through Linear Discriminant Analysis. Here, the Pearson correlation is used to examine the relationship between the objective assessments and the expert clinical scores while the performance was also verified by means of cross validation. RESULTS: The experimental results show that acceleration is a major feature in DDK and HST, whereas rotation is the main feature responsible for classification in FNT. Combining the features enhanced the correlations in each domain. The subject data was classified based on the severity information based on expert clinical scores. CONCLUSION: For the predominantly translational movement in the upper limb FNT, the rotation captures disability and for the DDK test with predominantly rotational movements, the linear acceleration captures the disability but cannot be extended to the lower limb HST. The orthogonal direction manifestation of ataxia attributed to sensory measurements was determined for each test. TRIAL REGISTRATION: Human Research and Ethics Committee, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia (HREC Reference Number: 11/994H/16).

Assessment of Shoulder Range of Motion Using a Wireless Inertial Motion Capture Device-A Validation Study.

(1) Background: Measuring joint range of motion has traditionally occurred with a universal goniometer or expensive laboratory based kinematic analysis systems. Technological advances in wearable inertial measurement units (IMU) enables limb motion to be measured with a small portable electronic device. This paper aims to validate an IMU, the 'Biokin', for measuring shoulder range of motion in healthy adults; (2) Methods: Thirty participants completed four shoulder movements (forward flexion, abduction, and internal and external rotation) on each shoulder. Each movement was assessed with a goniometer and the IMU by two testers independently. The extent of agreement between each tester's goniometer and IMU measurements was assessed with intra-class correlation coefficients (ICC) and Bland-Altman 95% limits of agreement (LOA). Secondary analysis compared agreement between tester's goniometer or IMU measurements (inter-rater reliability) using ICC's and LOA; (3) Results: Goniometer and IMU measurements for all movements showed high levels of agreement when taken by the same tester; ICCs > 0.90 and LOAs < ±5 degrees. Inter-rater reliability was lower; ICCs ranged between 0.71 to 0.89 and LOAs were outside a prior defined acceptable LOAs (i.e., > ±5 degrees); (4) Conclusions: The current study provides preliminary evidence of the concurrent validity of the Biokin IMU for assessing shoulder movements, but only when a single tester took measurements. Further testing of the Biokin's psychometric properties is required before it can be confidently used in routine clinical practice and research settings.

Measurement of Axial Rigidity and Postural Instability Using Wearable Sensors.

Axial Bradykinesia is an important feature of advanced Parkinson's disease (PD). The purpose of this study is to quantify axial bradykinesia using wearable sensors with the long-term aim of quantifying these movements, while the subject performs routine domestic activities. We measured back movements during common daily activities such as pouring, pointing, walking straight and walking around a chair with a test system engaging a minimal number of Inertial Measurement (IM) based wearable sensors. Participants included controls and PD patients whose rotation and flexion of the back was captured by the time delay between motion signals from sensors attached to the upper and lower back. PD subjects could be distinguished from controls using only two sensors. These findings suggest that a small number of sensors and similar analyses could distinguish between variations in bradykinesia in subjects with measurements performed outside of the laboratory. The subjects could engage in routine activities leading to progressive assessments of therapeutic outcomes.

Quantification of Axial Abnormality Due to Cerebellar Ataxia with Inertial Measurements.

Cerebellar Ataxia (CA) leads to deficiencies in muscle movement and lack of coordination that is often manifested as gait and balance disabilities. Conventional CA clinical assessments are subjective, cumbersome and provide less insight into the functional capabilities of patients. This cross-sectional study investigates the use of wearable inertial sensors strategically positioned on the front-chest and upper-back locations during the Romberg and Trunk tests for objective assessment of human postural balance due to CA. The primary aim of this paper is to quantify the performance of postural stability of 34 patients diagnosed with CA and 22 healthy subjects as controls. Several forms of entropy descriptions were considered to uncover characteristics of movements intrinsic to CA. Indeed, correlation with clinical observation is vital in ascertaining the validity of the inertial measurements in addition to capturing unique features of movements not typically observed by the practicing clinician. Both of these aspects form an integral part of the underlying objective assessment scheme. Uncertainty in the velocity contained a significant level of information with respect to truncal instability and, based on an extensive clustering and discrimination analysis, fuzzy entropy was identified as an effective measure in characterising the underlying disability. Front-chest measurements demonstrated a strong correlation with clinical assessments while the upper-back measurements performed better in classifying the two cohorts, inferring that the standard clinical assessments are relatively influenced by the frontal observations. The Romberg test was confirmed to be an effective test of neurological diagnosis as well as a potential candidate for objective assessment resulting in a significant correlation with the clinical assessments. In contrast, the Trunk test is observed to be relatively less informative.

A Non-Contact Measurement System for the Range of Motion of the Hand.

An accurate and standardised tool to measure the active range of motion (ROM) of the hand is essential to any progressive assessment scenario in hand therapy practice. Goniometers are widely used in clinical settings for measuring the ROM of the hand. However, such measurements have limitations with regard to inter-rater and intra-rater reliability and involve direct physical contact with the hand, possibly increasing the risk of transmitting infections. The system proposed in this paper is the first non-contact measurement system utilising Intel Perceptual Technology and a Senz3D Camera for measuring phalangeal joint angles. To enhance the accuracy of the system, we developed a new approach to achieve the total active movement without measuring three joint angles individually. An equation between the actual spacial position and measurement value of the proximal inter-phalangeal joint was established through the measurement values of the total active movement, so that its actual position can be inferred. Verified by computer simulations, experimental results demonstrated a significant improvement in the calculation of the total active movement and successfully recovered the actual position of the proximal inter-phalangeal joint angles. A trial that was conducted to examine the clinical applicability of the system involving 40 healthy subjects confirmed the practicability and consistency in the proposed system. The time efficiency conveyed a stronger argument for this system to replace the current practice of using goniometers.

Optimal sensor arrangements in Angle of Arrival (AoA) and range based localization with linear sensor arrays.

This paper investigates the linear separation requirements for Angle-of-Arrival (AoA) and range sensors, in order to achieve the optimal performance in estimating the position of a target from multiple and typically noisy sensor measurements. We analyse the sensor-target geometry in terms of the Cramer-Rao inequality and the corresponding Fisher information matrix, in order to characterize localization performance with respect to the linear spatial distribution of sensors. Here in this paper, we consider both fixed and adjustable linear sensor arrays.

An Enhanced Classification Framework for Limited IoHT Time Series Data Using Ensemble Deep Learning and Image Encoding.

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. Although some studies have explored this area, it is still an understudied and undervalued topic. Thus, there is a crucial need to address this gap and provide insights into designing effective architectures for time series classification with limited data, specifically in the context of healthcare-related time series data for rare diseases. The goal of this study is to investigate the possibility of making accurate predictions with a smaller time series dataset by using an Ensemble DL architecture. This framework is composed of a deep CNN model and transfer learning approaches like ResNet and MobileNet. The ensemble model proposed in this study was supplied with 3D images that were generated from time series data by using Recurrence Plot (RP), Gramian Angular Field (GAF), and Fuzzy Recurrence Plot (FRP) as the transformation techniques. The proposed method has shown promising classification accuracy, even when applied to a small dataset, and surpassed the performance of other state-of-the-art methods when tested on the ECG5000 dataset.Clinical relevance- The proposed deep learning architecture is capable of effectively handling limited clinical time series datasets, enabling the construction of robust models and accurate predictions.

A Multi-Activity Fusion Approach for Gender Recognition based on Human Activity.

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. To create a model for gender recognition, we carried out an extensive study based on supervised machine learning. This study identifies a collection of sensor locations and behaviours to better precisely classify gender. Gender classification based on single activity was performed using Random Forest Classifier (RFC) and Support Vector Machines (SVM). Maximum accuracy of 92.06% was gained using Random Forest Classifier for the sensor located at the ankle when walking. Multi-activity based gender classification outperformed former by achieving an accuracy of 94.13% using RFC. This was for the activity combination of Romberg test eyes open, Single leg stance eyes open and Staircase up and down.

Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks.

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. The selection of features was accomplished using a combination of mass univariate analysis and elastic net regularization for the binary outcome, while for the ordinal outcome, Spearman's rank-order correlation criterion was employed. The algorithm was developed and evaluated using recordings from 126 participants: 65 individuals with CA and 61 controls (i.e., individuals without ataxia or neurotypical). For Ataxic Speech diagnosis, the reduced feature set yielded an area under the curve (AUC) of 0.97 (95% CI 0.90-1), the sensitivity of 97.43%, specificity of 85.29%, and balanced accuracy of 91.2% in the test dataset. The mean AUC for severity estimation was 0.74 for the test set. The high C-indexes of the prediction nomograms for identifying the presence of Ataxic Speech (0.96) and estimating its severity (0.81) in the test set indicates the efficacy of this algorithm. Decision curve analysis demonstrated the value of incorporating acoustic features from two repeated C-V syllable paradigms. The strong classification ability of the specified speech features supports the framework's usefulness for identifying and monitoring Ataxic Speech.

A Bayesian Network Approach for Friedreich Ataxia Severity Classification using Probability Modelling.

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. Bayesian Networks (BNs) provide a structure to adopt both subjective and objective measures to give a severity value while addressing these issues. The BN presented in this paper uses a hybrid learning approach, which utilises both subjective clinical assessments as well as instrumented measurements of disordered upper body movement of individuals with FRDA. The final model's estimates gave a 0.93 Pearson correlation with low error, 9.42 root mean square error and 7.17 mean absolute error. Predicting the clinical scales gave 94% accuracy for Upright Stability and Lower Limb Coordination and 67% accuracy for Functional Staging, Upper Limb Coordination and Activities of Daily Living.Clinical relevance- Due to the nature of rare diseases conventional machine learning is difficult. Most clinical trials only generate small datasets. This approach allows the combination of expert knowledge with instrumented measures to develop a clinical decision support system for the prediction of severity.

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.

Modified Recurrence Quantification Analysis for Objective Assessment of Cerebellar Ataxia.

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. However, this approach is subjective as it relies on the clinician's experience, and can vary between clinicians. To address this subjectivity, some researchers have developed automated assessment methods using signal processing and data-driven approaches, such as supervised machine learning. These methods still rely on subjective ground truth and can perform poorly in real-world scenarios. This research proposed an alternative approach that uses signal processing to modify recurrence plots and compare the severity of ataxia in a person with CA to a control cohort. The highest correlation score obtained was 0.782 on the back sensor with the feet-apart and eyes-open test. The contributions of the research include modifying the recurrence plot as a measurement tool for assessing CA severity, proposing a new approach to assess severity by comparing kinematic data between people with CA and a control reference group, and identifying the best subtest and sensor position for practical use in CA assessments.

Blockchained Federated Learning for Privacy and Security Preservation: Practical Example of Diagnosing Cerebellar Ataxia.

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. Federated learning is an effective distributed solution to exchange only the ML model weight rather than the raw data. However, FL is also vulnerable to network attacks from malicious devices. In this study, we depict the concept of blockchained FL with individual's validators. We simulate the proposed approach with real-world dataset collected from kinematic sensors of CA individuals with four geographically separated clinics. Experimental results show the blockchained FL maintains competitive accuracy of 89.30%, while preserving both privacy and security.

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.

Eigen decomposition solution to the one-dimensional time-dependent photon transport equation.

The time-dependent one-dimensional photon transport (radiative transfer) equation is widely used to model light propagation through turbid media with a slab geometry, in a vast number of disciplines. Several numerical and semi-analytical techniques are available to accurately solve this equation. In this work we propose a novel efficient solution technique based on eigen decomposition of the vectorized version of the photon transport equation. Using clever transformations, the four variable integro-differential equation is reduced to a set of first order ordinary differential equations using a combination of a spectral method and the discrete ordinates method. An eigen decomposition approach is then utilized to obtain the closed-form solution of this reduced set of ordinary differential equations.

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey.

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. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Objective Assessment of Progression and Disease Characterization of Friedreich Ataxia via an Instrumented Drinking Cup: Preliminary Results.

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. In this feasibility study, an Ataxia Instrumented Measurement-Cup (AIM-C) is proposed to quantify the disease progression of 10 participants (mean age 39 years, onset of disease 16.3 years) in longitudinal timepoints. The device consists of a sensing system with the provision of extracting both kinetic and kinematic information while engaging in an activity closely associated with activities of daily living (ADL). A common functional task of simulated drinking was used to capture features that possesses disease progression information as well as certain other features which intrinsically correlate with commonly used clinical scales such as the modified Friedreich Ataxia Rating Scale (mFARS), the Functional Staging of Ataxia score and the ADL scale. Frequency and time-frequency domain features allowed the longitudinal assessment of participants with FRDA. Furthermore, both kinetic and kinematic measures captured clinically relevant features and correlated 85% with clinical assessments.