Low-Cost, 3-Dimension, Office-Based Inertial Sensors for Automated Tremor Assessment: Technical Development and Experimental Verification

New opportunities for antioxidants in amelioration of neurodegenerative diseases

This comprehensive review elucidates the critical role of antioxidants to mitigate oxidative stress, a common denominator in an array of neurodegenerative disorders. Oxidative stress-induced damage has been linked to the development of diseases such as Alzheimer's, Parkinson's, Huntington's disease and amyotrophic lateral sclerosis. This article examines a wide range of scientific literature and methodically delineates the several methods by which antioxidants exercise their neuroprotective benefits. It also explores into the complex relationship between oxidative stress and neuroinflammation, focusing on how antioxidants can alter signaling pathways and transcription factors to slow neurodegenerative processes. Key antioxidants, such as vitamins C and E, glutathione, and polyphenolic compounds, are tested for their ability to combat reactive oxygen and nitrogen species. The dual character of antioxidants, which operate as both direct free radical scavengers and regulators of cellular redox homeostasis, is investigated in terms of therapeutic potential. Furthermore, the study focuses on new antioxidant-based therapy techniques and their mechanisms including Nrf-2, PCG1α, Thioredoxin etc., which range from dietary interventions to targeted antioxidant molecules. Insights into ongoing clinical studies evaluating antioxidant therapies in neurodegenerative illnesses offer an insight into the translational potential of antioxidant research. Finally, this review summarizes our present understanding of antioxidant processes in neurodegenerative illnesses, providing important possibilities for future study and treatment development.

Old problems, new solutions: harnessing technology and innovation in Parkinson's disease-evidence and experiences from Thailand

The prevalence of Parkinson's disease (PD) is increasing rapidly worldwide, but there are notable inequalities in its distribution and in the availability of healthcare resources across different world regions. Low- and middle-income countries (LMICs), including Thailand, bear the highest burden of PD so there is an urgent need to develop effective solutions that can overcome the many regional challenges associated with delivering high-quality, and equitable care to a diverse population with limited resources. This article describes the evolution of healthcare delivery for PD in Thailand, as a case example of a LMIC. The discussions reflect the author's presentation at the Yoshikuni Mizuno Lectureship Award given during the 8th Asian and Oceanian Parkinson's Disease and Movement Disorders Congress in March 2023 for which he was the 2023 recipient. The specific challenges faced in Thailand are reviewed along with new solutions that have been implemented to improve the knowledge and skills of healthcare professionals nationally, the delivery of care, and the outcomes for PD patients. Technology and innovation have played an important role in this process with many new tools and devices being implemented in clinical practice. Without any realistic prospect of a curative therapy in the near future that could halt the current PD pandemic, it will be necessary to focus on preventative lifestyle strategies that can help reduce the risk of developing PD such as good nutrition (EAT), exercise (MOVE), good sleep hygiene (SLEEP), and minimizing environmental risks (PROTECT), which should be initiated and continued (REPEAT) as early as possible.

Upper limb intention tremor assessment: opportunities and challenges in wearable technology

BACKGROUND

Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.

METHODOLOGY & RESULTS

This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

CONCLUSIONS

Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

Quantifying Motor Impairment in Movement Disorders

Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor. Many applications are being developed for use on smartphones. The benefits include not just more accurate disease quantification, but also consistency of data for longitudinal studies, accurate stratification of patients for entry into trials, and the possibility of automated data capture for remote follow-up. In this mini review, we will look at movement disorders with a particular focus on Parkinson's disease, describe some of the limitations of existing clinical evaluation tools, and illustrate the ways in which objective metrics have already been successful.

Capturing nighttime symptoms in Parkinson disease: Technical development and experimental verification of inertial sensors for nocturnal hypokinesia

Although nocturnal hypokinesia represents one of the most common nocturnal disabilities in Parkinson disease (PD), it is often a neglected problem in daily clinical practice. We have developed a portable ambulatory motion recorder (the NIGHT-Recorder), which consists of 16-bit triaxial integrated microelectromechanical system inertial sensors that are specifically designed to measure movements, register the position of the body with respect to gravity, and provide information on rotations on the longitudinal axis while lying in bed. The signal processing uses the forward derivative method to identify rolling over and getting out of bed as primary indicators. The prototype was tested on six PD pairs to measure their movements for one night. Using predetermined definitions, 134 movements were captured consisting of rolling over 115 times and getting out of bed 19 times. Patients with PD rolled over significantly fewer times than their spouses (p = 0.03), and the position change was significantly smaller in patients with PD (p = 0.03). Patients with PD rolled over at a significantly slower speed (p = 0.03) and acceleration (p = 0.03) than their spouses. In contrast, patients with PD got out of bed significantly more often than their spouses (p = 0.02). It is technically feasible to develop an easy-to-use, portable, and accurate device that can assist physicians in the assessment of nocturnal movements of patients with PD.

An Ambulatory Tremor Score for Parkinson's Disease

BACKGROUND

While tremor in Parkinson's Disease (PD) can be characterised in the consulting room, its relationship to treatment and fluctuations can be clinically helpful.

OBJECTIVE

To develop an ambulatory assessment of tremor of PD.

METHODS

Accelerometry data was collected using the Parkinson's KinetiGraph System (PKG, Global Kinetics). An algorithm was developed, which could successfully distinguish been subjects with a resting or postural tremor that involved the wrist whose frequency was greater than 3 Hz. Percent of time that tremor was present (PTT) between 09 : 00 and 18 : 00 was calculated.

RESULTS

This algorithm was applied to 85 people with PD who had been assessed clinically for the presence and nature of tremor. The Sensitivity and Selectivity of a PTT ≥0.8% was 92.5% and 92.9% in identifying tremor, providing that the tremor was not a fine kinetic and postural tremor or was not in the upper limb. A PTT >1% provide high likely hood of the presence of clinical meaningful tremor. These cut-offs were retested on a second cohort (n = 87) with a similar outcome. The Sensitivity and Selectivity of the combined group was 88.7% and 89.5% respectively. Using the PTT, 50% of 22 newly diagnosed patients had a PTT >1.0%.The PKG's simultaneous bradykinesia scores was used to find a threshold for the emergence of tremor. Tremor produced artefactual increase in the PKG's dyskinesia score in 1% of this sample.

CONCLUSIONS

We propose this as a means of assessing the presence of tremor and its relationship to bradykinesia.

Support vector machine classification of Parkinson's disease and essential tremor subjects based on temporal fluctuation

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the realization of treatment depends on specific medication. A novel feature is developed based on a hypothesis that tremor of PD subject has a larger fluctuation during resting than action task. Tremor signal is collected using a triaxial gyroscope sensor attached to subject's finger during kinetic and resting task. The angular velocity signal is analyzed by transforming a one-dimensional to two-dimensional signal using a relation of signal and its delay versions. Tremor fluctuation is defined as the area of 95% confidence ellipse covering the two-dimensional signal. The tremor fluctuation during kinetic and resting task is used as classification features. The support vector machine is used as a classifier and tested with 10-fold cross-validation. This novel feature provides a perfect PD/ET classification with 100% accuracy, sensitivity and specificity.

Clinical Assessments in Parkinson's Disease: Scales and Monitoring

Measurement of disease state is essential in both clinical practice and research in order to assess the severity and progression of a patient's disease status, effect of treatment, and alterations in other relevant factors. Parkinson's disease (PD) is a complex disorder expressed through many motor and nonmotor manifestations, which cause disabilities that can vary both gradually over time or come on suddenly. In addition, there is a wide interpatient variability making the appraisal of the many facets of this disease difficult. Two kinds of measure are used for the evaluation of PD. The first is subjective, inferential, based on rater-based interview and examination or patient self-assessment, and consist of rating scales and questionnaires. These evaluations provide estimations of conceptual, nonobservable factors (e.g., symptoms), usually scored on an ordinal scale. The second type of measure is objective, factual, based on technology-based devices capturing physical characteristics of the pathological phenomena (e.g., sensors to measure the frequency and amplitude of tremor). These instrumental evaluations furnish appraisals with real numbers on an interval scale for which a unit exists. In both categories of measures, a broad variety of tools exist. This chapter aims to present an up-to-date summary of the most relevant characteristics of the most widely used scales, questionnaires, and technological resources currently applied to the assessment of PD. The review concludes that, in our opinion: (1) no assessment methods can substitute the clinical judgment and (2) subjective and objective measures in PD complement each other, each method having strengths and weaknesses.

Temporal fluctuations of tremor signals from inertial sensor: a preliminary study in differentiating Parkinson's disease from essential tremor

BACKGROUND

Parkinson's disease (PD) and essential tremor (ET) are the two most common movement disorders but the rate of misdiagnosis rate in these disorders is high due to similar characteristics of tremor. The purpose of the study is to present: (a) a solution to identify PD and ET patients by using the novel measurement of tremor signal variations while performing the resting task, (b) the improvement of the differentiation of PD from ET patients can be obtained by using the ratio of the novel measurement while performing two specific tasks.

METHODS

35 PD and 22 ET patients were asked to participate in the study. They were asked to wear a 6-axis inertial sensor on his/her index finger of the tremor dominant hand and perform three tasks including kinetic, postural and resting tasks. Each task required 10 s to complete. The angular rate signal measured during the performance of these tasks was band-pass filtered and transformed into a two-dimensional representation. The ratio of the ellipse area covering 95 % of this two-dimensional representation of different tasks was investigated and the two best tasks were selected for the purpose of differentiation.

RESULTS

The ellipse area of two-dimensional representation of the resting task of PD and ET subjects are statistically significantly different (p < 0.05). Furthermore, the fluctuation ratio, defined as a ratio of the ellipse area of two-dimensional representation of resting to kinetic tremor, of PD subjects were statistically significantly higher than ET subjects in all axes (p = 0.0014, 0.0011 and 0.0001 for x, y and z-axis, respectively). The validation shows that the proposed method provides 100 % sensitivity, specificity and accuracy of the discrimination in the 5 subjects in the validation group. While the method would have to be validated with a larger number of subjects, these preliminary results show the feasibility of the approach.

CONCLUSIONS

This study provides the novel measurement of tremor variation in time domain termed 'temporal fluctuation'. The temporal fluctuation of the resting task can be used to discriminate PD from ET subjects. The ratio of the temporal fluctuation of the resting task to the kinetic task improves the reliability of the discrimination. While the method is powerful, it is also simple so it could be applied on low resource platforms such as smart phones and watches which are commonly equipped with inertial sensors.

Temporal fluctuation analysis of tremor signal in Parkinson's disease and Essential tremor subjects

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the treatment depends on specific medication. A novel feature was developed based on a hypothesis stating that the tremor of PD subject has a larger fluctuation while performing resting task than action task. Tremor signal was collected using a gyroscope sensor attached to subject's finger. The angular velocity signal was analyzed by transforming a one-dimensional to two-dimensional signal based on relation of different units of time-delay. The tremor fluctuation was defined as the area of 95% confidence ellipse covering the two-dimensional signal. Experimenting with 32 PD and 20 ET subjects, a ratio of fluctuation of resting to kinetic task can be a sensitive feature to discriminate PD from ET with 100% accuracy.