3,052 research outputs found

    A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia

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    Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics

    A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia.

    Get PDF
    Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics

    Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

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    Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

    A preliminary investigation of the use of inertial sensing technology for the measurement of hip rotation asymmetry in horse riders

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    This study investigated the use of inertial sensing technology as an indicator of asymmetry in horse riders, evidenced by discrepancies in the angle of external rotation of the hip joint. Twelve horse and rider combinations were assessed with the rider wearing the XsensTM MVN inertial motion capture suit. Asymmetry (left vs right) was revealed in mean hip external rotation of all riders, with values ranging from 1° to 27°, and 83% showed greater external rotation of the right hip. This study represents novel use of inertial sensing equipment in its application to the measurement of rider motion patterns. The technique is non-invasive, is capable of recording rider hip rotation asymmetry whilst performing a range of movements unhindered and was found to be efficient and practical, with potential to further advance the analysis of horse and rider interactions

    Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data

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    In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap632+632+and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead tosim96sim 96%correct classification rates with less than 10% of the original features

    Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study

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    Background: Classic motion abnormalities in Parkinson's disease (PD), such as tremor, bradykinesia, or rigidity, are well-covered by standard clinical assessments such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, PD includes motor abnormalities beyond the symptoms and signs as measured by UPDRS, such as the lack of anticipatory adjustments or compromised movement smoothness, which are difficult to assess clinically. Moreover, PD may entail motor abnormalities not yet known. All these abnormalities are quantifiable via motion capture and may serve as biomarkers to diagnose and monitor PD. Objective: In this pilot study, we attempted to identify motion features revealing maximum contrast between healthy subjects and PD patients with deep brain stimulation (DBS) of the nucleus subthalamicus (STN) switched off and on as the first step to develop biomarkers for detecting and monitoring PD patients' motor symptoms. Methods: We performed 3D gait analysis in 7 out of 26 PD patients with DBS switched off and on, and in 25 healthy control subjects. We computed feature values for each stride, related to 22 body segments, four time derivatives, left–right mean vs. difference, and mean vs. variance across stride time. We then ranked the feature values according to their distinguishing power between PD patients and healthy subjects. Results: The foot and lower leg segments proved better in classifying motor anomalies than any other segment. Higher degrees of time derivatives were superior to lower degrees (jerk > acceleration > velocity > displacement). The averaged movements across left and right demonstrated greater distinguishing power than left–right asymmetries. The variability of motion was superior to motion's absolute values. Conclusions: This small pilot study identified the variability of a smoothness measure, i.e., jerk of the foot, as the optimal signal to separate healthy subjects' from PD patients' gait. This biomarker is invisible to clinicians' naked eye and is therefore not included in current motor assessments such as the UPDRS. We therefore recommend that more extensive investigations be conducted to identify the most powerful biomarkers to characterize motor abnormalities in PD. Future studies may challenge the composition of traditional assessments such as the UPDRS

    Inertial sensors-based lower-limb rehabilitation assessment: A comprehensive evaluation of gait, kinematic and statistical metrics

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    Analysis of biomechanics is frequently used in both clinical and sporting practice in order to assess human motion and their performance of defined tasks. Whilst camera-based motion capture systems have long been regarded as the ‘Gold-standard’ for quantitative movement-based analysis, their application is not without limitations as regards potential sources of variability in measurements, high cost, and practicality of use for larger patient/subject groups. Another more practical approach, which presents itself as a viable solution to biomechanical motion capture and monitoring in sporting and patient groups, is through the use of small-size low-cost wearable Micro-ElectroMechanical Systems (MEMs)-based inertial sensors. The clinical aim of the present work is to evaluate rehabilitation progress following knee injuries, identifying a number of metrics measured via a wireless inertial sensing system. Several metrics in the time-domain have been considered to be reliable for measuring and quantifying patient progress across multiple exercises in different activities. This system was developed at the Tyndall National Institute and is able to provide a complete and accurate biomechanics assessment without the constraints of a motion capture laboratory. The results show that inertial sensors can be used for a quantitative assessment of knee joint mobility, providing valuable information to clinical experts as regards the trend of patient progress over the course of rehabilitation

    Wearable inertial sensors as a tool for quantitative assessment of progress during rehabilitation

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    Biomechanics analysis is frequently used in both clinical and sporting practice in order to assess human motion and performance of defined tasks. Whilst camera-based motion systems have long been regarded as the ‘Goldstandard’ for quantitative movement-based analysis, their application is not without limitations as regards potential sources of variability in measurements, high costs, and practicality of use for larger patient/subject groups. Another more practical approach, which presents itself as a viable solution to biomechanical motion capture and monitoring in sporting and patient groups, is through the use of small-size low-cost wearable Micro-ElectroMechanical Systems (MEMs)- based inertial sensors. The clinical aim of the present work is to evaluate gait during rehabilitation following knee injuries and to identify gait abnormalities through a wireless inertial sensing system. This system was developed at the Tyndall National Institute to meet clinician-defined needs, and is able to provide a complete biomechanics assessment without the constraints of a motion capture laboratory. The derived motion parameter outcomes can be analyzed by clinicians and sport scientists to study the overall patients’ condition and provide accurate medical feedback as to their rehabilitative progress. Detection of atypical movement characteristics is possible by comparing the performance and variability in motion characteristics in the patient’s affected and unaffected lower-limbs. The work is ongoing, and to date the system has been tested on only one impaired subject, additional clinical trials are currently being planned with an enhanced number of injured subjects. This will provide a more robust statistical analysis of the data in the study. The present feasibility study proved that inertial sensors can be used for a quantitative assessment of knee joint mobility, and gait mechanics during the rehabilitation program of injured subjects and can provide valuable information to clinical experts as regards patient rehabilitation
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