Neurological tremor: sensors, signal processing and emerging applications

Reliability and validity of estimated angles information assessed using inertial measurement unit-based motion sensors

BACKGROUND

Inertial measurement unit (IMU)-based motion sensors are affordable, and their use is appropriate for rehabilitation. However, regarding the accuracy of estimated angle information obtained from this sensor, it is reported that it is likely affected by velocity.

OBJECTIVE

The present study investigated the reliability and validity of the angle information obtained using IMU-based sensors compared with a three-dimensional (3D) motion analyzer.

METHODS

The Euler angle obtained using the 3D motion analyzer and the angle obtained using the IMU-based sensor (IMU angle) were compared. Reliability was assessed by comparing the Bland-Altman analysis, intra-class correlation coefficient (ICC) (1,1), and cross-correlation function. The root mean square (RMS) error, ICC (2,1), and cross-correlation function were used to compare data on the Euler and IMU angles to evaluate the validity.

RESULTS

Regarding reliability, the Bland-Atman analysis indicated no fixed or proportional bias in the angle measurements. The measurement errors ranged from 0.2° to 3.2°. In the validity, the RMS error ranged from 0.3° to 2.2°. The ICCs (2,1) were 0.9. The cross-correlation functions were >0.9, which indicated a high degree of agreement.

CONCLUSION

The IMU-based sensor had a high reliability and validity. The IMU angle may be used in rehabilitation.

Accelerometric Classification of Resting and Postural Tremor Amplitude

Clinical rating scales for tremors have significant limitations due to low resolution, high rater dependency, and lack of applicability in outpatient settings. Reliable, quantitative approaches for assessing tremor severity are warranted, especially evaluating treatment effects, e.g., of deep brain stimulation (DBS). We aimed to investigate how different accelerometry metrics can objectively classify tremor amplitude of Essential Tremor (ET) and tremor in Parkinson's Disease (PD). We assessed 860 resting and postural tremor trials in 16 patients with ET and 25 patients with PD under different DBS settings. Clinical ratings were compared to different metrics, based on either spectral components in the tremorband or pure acceleration, derived from simultaneous triaxial accelerometry captured at the index finger and wrist. Nonlinear regression was applied to a training dataset to determine the relationship between accelerometry and clinical ratings, which was then evaluated in a holdout dataset. All of the investigated accelerometry metrics could predict clinical tremor ratings with a high concordance (>70%) and substantial interrater reliability (Cohen's weighted Kappa > 0.7) in out-of-sample data. Finger-worn accelerometry performed slightly better than wrist-worn accelerometry. We conclude that triaxial accelerometry reliably quantifies resting and postural tremor amplitude in ET and PD patients. A full release of our dataset and software allows for implementation, development, training, and validation of novel methods.

Physiological Recordings of the Cerebellum in Movement Disorders

The cerebellum plays an important role in movement disorders, specifically in symptoms of ataxia, tremor, and dystonia. Understanding the physiological signals of the cerebellum contributes to insights into the pathophysiology of these movement disorders and holds promise in advancing therapeutic development. Non-invasive techniques such as electroencephalogram and magnetoencephalogram can record neural signals with high temporal resolution at the millisecond level, which is uniquely suitable to interrogate cerebellar physiology. These techniques have recently been implemented to study cerebellar physiology in healthy subjects as well as individuals with movement disorders. In the present review, we focus on the current understanding of cerebellar physiology using these techniques to study movement disorders.

Deep brain stimulation for movement disorders after stroke: a systematic review of the literature

OBJECTIVE

Stroke remains the leading cause of disability in the United States. Even as acute care for strokes advances, there are limited options for improving function once the patient reaches the subacute and chronic stages. Identification of new therapeutic approaches is critical. Deep brain stimulation (DBS) holds promise for these patients. A number of case reports and small case series have reported improvement in movement disorders after strokes in patients treated with DBS. In this systematic review, the authors have summarized the patient characteristics, anatomical targets, stimulation parameters, and outcomes of patients who have undergone DBS treatment for poststroke movement disorders.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The PubMed, Scopus, and SpringerLink databases were searched for the keywords "DBS," "stroke," "movement," and "recovery" to identify patients treated with DBS for movement disorders after a stroke. The Joanna Briggs Institute Critical Appraisal checklists for case reports and case series were used to systematically analyze the quality of the included studies. Data collected from each study included patient demographic characteristics, stroke diagnosis, movement disorder, DBS target, stimulation parameters, complications, and outcomes.

RESULTS

The authors included 29 studies that described 53 patients who underwent placement of 82 total electrodes. Movement disorders included tremor (n = 18), dystonia (n = 18), hemiballism (n = 6), spastic hemiparesis (n = 1), chorea (n = 1), and mixed disorders (n = 9). The most common DBS targets were the globus pallidus internus (n = 32), ventral intermediate nucleus of thalamus (n = 25), and subthalamic area/subthalamic nucleus (n = 7). Monopolar stimulation was reported in 43 leads and bipolar stimulation in 13. High-frequency stimulation was used in 57 leads and low-frequency stimulation in 6. All patients but 1 had improvement in their movement disorders. Two complications were reported: speech impairment in 1 patient and hardware infection in another. The median (interquartile range) duration between stroke and DBS treatment was 6.5 (2.1-15.8) years.

CONCLUSIONS

This is the first systematic review of DBS for poststroke movement disorders. Overall, most studies to date have been case reports and small series reporting heterogeneous patients and surgical strategies. This review suggests that DBS for movement disorders after a stroke has the potential to be effective and safe for diverse patients, and DBS may be a feasible option to improve function even years after a stroke.

Strain Gauge Measuring System for Subsensory Micromotions Analysis as an Element of a Hybrid Human-Machine Interface

The human central nervous system is the integrative basis for the functioning of the organism. The basis of such integration is provided by the fact that the same neurons are involved in various sets of sensory, cognitive, and motor functions. Therefore, the analysis of one set of integrative system components makes it possible to draw conclusions about the state and efficiency of the other components. Thus, to evaluate a person's cognitive properties, we can assess their involuntary motor acts, i.e., a person's subsensory reactions. To measure the parameters of involuntary motor acts, we have developed a strain gauge measuring system. This system provides measurement and estimation of the parameters of involuntary movements against the background of voluntary isometric efforts. The article presents the architecture of the system and shows the organization of the primary signal processing in analog form, in particular the separation of the signal taken from the strain-gauge sensor into frequency and smoothly varying components by averaging and subtracting the analog signals. This transfer to analog form simplifies the implementation of the digital part of the measuring system and allowed for minimizing the response time of the system while displaying the isometric forces in the visual feedback channel. The article describes the realization of the system elements and shows the results of its experimental research.

Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring

Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients' movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.

A standardized accelerometry method for characterizing tremor: Application and validation in an ageing population with postural and action tremor

Background

Ordinal scales based on qualitative observation are the mainstay in the clinical assessment of tremor, but are limited by inter-rater reliability, measurement precision, range, and ceiling effects. Quantitative tremor evaluation is well-developed in research, but clinical application has lagged, in part due to cumbersome mathematical application and lack of established standards.

Objectives

To develop a novel method for evaluating tremor that integrates a standardized clinical exam, wrist-watch accelerometers, and a software framework for data analysis that does not require advanced mathematical or computing skills. The utility of the method was tested in a sequential cohort of patients with predominant postural and action tremor presenting to a specialized surgical clinic with the presumptive diagnosis of Essential Tremor (ET).

Methods

Wristwatch accelerometry was integrated with a standardized clinical exam. A MATLAB application was developed for automated data analysis and graphical representation of tremor. Measures from the power spectrum of acceleration of tremor in different upper limb postures were derived in 25 consecutive patients. The linear results from accelerometry were correlated with the commonly used non-linear Clinical Rating Scale for Tremor (CRST).

Results

The acceleration power spectrum was reliably produced in all consecutive patients. Tremor frequency was stable in different postures and across patients. Both total and peak power of acceleration during postural conditions correlated well with the CRST. The standardized clinical examination with integrated accelerometry measures was therefore effective at characterizing tremor in a population with predominant postural and action tremor. The protocol is also illustrated on repeated measures in an ET patient who underwent Magnetic Resonance-Guided Focused Ultrasound thalamotomy.

Conclusion

Quantitative assessment of tremor as a continuous variable using wristwatch accelerometry is readily applicable as a clinical tool when integrated with a standardized clinical exam and a user-friendly software framework for analysis. The method is validated for patients with predominant postural and action tremor, and can be adopted for characterizing tremor of different etiologies with dissemination in a wide variety of clinical and research contexts in ageing populations.

Development of a Robust, Simple, and Affordable Human Gait Analysis System Using Bottom-Up Pose Estimation With a Smartphone Camera

Gait analysis is used in many fields such as Medical Diagnostics, Osteopathic medicine, Comparative and Sports-related biomechanics, etc. The most commonly used system for capturing gait is the advanced video camera-based passive marker system such as VICON. However, such systems are expensive, and reflective markers on subjects can be intrusive and time-consuming. Moreover, the setup of markers for certain rehabilitation patients, such as people with stroke or spinal cord injuries, could be difficult. Recently, some markerless systems were introduced to overcome the challenges of marker-based systems. However, current markerless systems have low accuracy and pose other challenges in gait analysis with people in long clothing, hiding the gait kinematics. The present work attempts to make an affordable, easy-to-use, accurate gait analysis system while addressing all the mentioned issues. The system in this study uses images from a video taken with a smartphone camera (800 × 600 pixels at an average rate of 30 frames per second). The system uses OpenPose, a 2D real-time multi-person keypoint detection technique. The system learns to associate body parts with individuals in the image using Convolutional Neural Networks (CNNs). This bottom-up system achieves high accuracy and real-time performance, regardless of the number of people in the image. The proposed system is called the “OpenPose based Markerless Gait Analysis System” (OMGait). Ankle, knee, and hip flexion/extension angle values were measured using OMGait in 16 healthy volunteers under different lighting and clothing conditions. The measured kinematic values were compared with a standard video camera based normative dataset and data from a markerless MS Kinect system. The mean absolute error value of the joint angles from the proposed system was less than 9⁰ for different lighting conditions and less than 11⁰ for different clothing conditions compared to the normative dataset. The proposed system is adequate in measuring the kinematic values of the ankle, knee, and hip. It also performs better than the markerless systems like MS Kinect that fail to measure the kinematics of ankle, knee, and hip joints under dark and bright light conditions and in subjects with long robe clothing.

Adaptive deep brain stimulation: Retuning Parkinson's disease

A brain-machine interface represents a promising therapeutic avenue for the treatment of many neurologic conditions. Deep brain stimulation (DBS) is an invasive, neuro-modulatory tool that can improve different neurologic disorders by delivering electric stimulation to selected brain areas. DBS is particularly successful in advanced Parkinson's disease (PD), where it allows sustained improvement of motor symptoms. However, this approach is still poorly standardized, with variable clinical outcomes. To achieve an optimal therapeutic effect, novel adaptive DBS (aDBS) systems are being developed. These devices operate by adapting stimulation parameters in response to an input signal that can represent symptoms, motor activity, or other behavioral features. Emerging evidence suggests greater efficacy with fewer adverse effects during aDBS compared with conventional DBS. We address this topic by discussing the basics principles of aDBS, reviewing current evidence, and tackling the many challenges posed by aDBS for PD.

Neurodegenerative disorders management: state-of-art and prospects of nano-biotechnology

Neurodegenerative disorders (NDs) are highly prevalent among the aging population. It affects primarily the central nervous system (CNS) but the effects are also observed in the peripheral nervous system. Neural degeneration is a progressive loss of structure and function of neurons, which may ultimately involve cell death. Such patients suffer from debilitating memory loss and altered motor coordination which bring up non-affordable and unavoidable socio-economic burdens. Due to the unavailability of specific therapeutics and diagnostics, the necessity to control or manage NDs raised the demand to investigate and develop efficient alternative approaches. Keeping trends and advancements in view, this report describes both state-of-the-art and challenges in nano-biotechnology-based approaches to manage NDs, toward personalized healthcare management. Sincere efforts are being made to customize nano-theragnostics to control: therapeutic cargo packaging, delivery to the brain, nanomedicine of higher efficacy, deep brain stimulation, implanted stimulation, and managing brain cell functioning. These advancements are useful to design future therapy based on the severity of the patient's neurodegenerative disease. However, we observe a lack of knowledge shared among scientists of a variety of expertise to explore this multi-disciplinary research field for NDs management. Consequently, this review will provide a guideline platform that will be useful in developing novel smart nano-therapies by considering the aspects and advantages of nano-biotechnology to manage NDs in a personalized manner. Nano-biotechnology-based approaches have been proposed as effective and affordable alternatives at the clinical level due to recent advancements in nanotechnology-assisted theragnostics, targeted delivery, higher efficacy, and minimal side effects.

Does Cup-Grip Type Affect Tremor among People with Essential Tremor?

Essential tremor (ET) is a movement disorder that may cause functional disability in daily activities, such as drinking from a cup or drawing. This study aims to characterize effects of varied cup-grip types and measured axes on the actual performance of people with ET and find correlations between cup-grip type and measured axes, and spiral drawing measures. Participants (20 with ET and 18 controls) held a cup of water in a steady position in three grip types and drew a spiral. The cup acceleration was measured by the cup triaxial accelerometer, analyzed in X, Y and Z axes (directions); deviation of the measured acceleration from the desired steady position acceleration was computed. Significant group differences were found for outcome measures in all grip types. Among participants with ET, significantly higher measured values were found in the cup’s horizontal plane (X and Y axes) compared to the vertical direction (Z axis) and for on-the-handle versus around-the-cup grips in the X and Y axes. Significant correlations were found between this grip’s measures and spiral-drawing actual performance measures, indicating the measurement axis and grip type may affect actual performance. These findings may support the future development of assistive devices for tremor suppression and personalized supportive therapy.

Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming

Programming in deep brain stimulation (DBS) is a labour-intensive process for treating advanced motor symptoms. Specifically for patients with medication-refractory tremor in multiple sclerosis (MS). Wearable sensors are able to detect some manifestations of pathological signs, such as intention tremor in MS. However, methods are needed to visualise the response of tremor to DBS parameter changes in a clinical setting while patients perform the motor task finger-to-nose. To this end, we attended DBS programming sessions of a MS patient and intention tremor was effectively quantified by acceleration amplitude and frequency. A new method is introduced which results in the generation of therapeutic maps for a systematic review of the programming procedure in DBS. The maps visualise the combination of tremor acceleration power, clinical rating scores, total electrical energy delivered to the brain and possible side effects. Therapeutic maps have not yet been employed and could lead to a certain degree of standardisation for more objective decisions about DBS settings. The maps provide a base for future research on visualisation tools to assist physicians who frequently encounter patients for DBS therapy.

QUANTITATIVE ANALYSIS OF ESSENTIAL TREMOR DURING CLINICAL SPIRAL DRAWING TASK USING GYRO SENSORS

The spiral drawing task is a representative clinical test used to assess essential tremor. Many clinicians have used spirography to assess through visual rating of the drawings. We quantitatively investigated the clinical characteristics of action tremors during spiral drawing tasks using 3-axis gyro sensors. Eighteen essential tremor patients with sensors attached to their forefinger, dorsum of the hand, and forearm participated in a spiral drawing test. As quantitative variables, the average speed, maximum frequency and maximum power were derived from the gyro sensor signals. The average speed and maximum power were also logarithmically transformed. Two-way repeated measures analysis of variance (ANOVA) with tremor direction and joint (attachment location) as independent factors was performed to evaluate the main and interactive effects of direction and joint. Also, Spearman’s correlation analysis was performed to evaluate the relationship between the quantitative variables and visual rating scores. The average speed showed a significant main effect of direction ([Formula: see text]). Logarithmic variables showed interactive effects ([Formula: see text]) as well as main effects ([Formula: see text]). The interactive effects were attributed to no significant difference among the joints particularly in the roll direction ([Formula: see text]). The logarithmic variables of roll direction were significantly greater than that of the other directions ([Formula: see text]). Also, the quantitative variables were moderately correlated with visual rating scores ([Formula: see text], [Formula: see text]). These results indicate that kinetic tremor during spiral drawing mainly appears in the roll direction and may contribute to the determination of sensor location and direction for continuous monitoring of patients with essential tremor.

Detecting subtle yet fast skeletal muscle contractions with ultrasoft and durable graphene-based cellular materials

Human bodily movements are primarily controlled by the contractions of skeletal muscles. Unlike joint or skeletal movements that are generally performed in the large displacement range, the contractions of the skeletal muscles that underpin these movements are subtle in intensity yet high in frequency. This subtlety of movement makes it a formidable challenge to develop wearable and durable soft materials to electrically monitor such motions with high fidelity for the purpose of, for example, muscle/neuromuscular disease diagnosis. Here we report that an intrinsically fragile ultralow-density graphene-based cellular monolith sandwiched between silicone rubbers can exhibit a highly effective stress and strain transfer mechanism at its interface with the rubber, with a remarkable improvement in stretchability (>100%). In particular, this hybrid also exhibits a highly sensitive, broadband-frequency electrical response (up to 180 Hz) for a wide range of strains. By correlating the mechanical signal of muscle movements obtained from this hybrid material with electromyography, we demonstrate that the strain sensor based on this hybrid material may provide a new, soft and wearable mechanomyography approach for real-time monitoring of complex neuromuscular–skeletal interactions in a broad range of healthcare and human–machine interface applications. This work also provides a new architecture-enabled functional soft material platform for wearable electronics.

Drawing Direction Effect on a Task's Performance Characteristics among People with Essential Tremor

Essential tremor (ET) is a common movement disorder affecting the performance of various daily tasks, including drawing. While spiral-drawing task characteristics have been described among patients with ET, research about the significance of the drawing direction of both spiral and lines tasks on the performance process is scarce. This study mapped inter-group differences between people with ET and controls related to drawing directions and the intra-effect of the drawing directions on the tremor level among people with ET. Twenty participants with ET and eighteen without ET drew spirals and vertical and horizontal lines on a digitizer with an inking pen. Time-based outcome measures were gathered to address the effect of the drawing directions on tremor by analyzing various spiral sections and comparing vertical and horizontal lines. Significant group differences were found in deviation of the spiral radius from a filtered radius curve and in deviation of the distance curve from a filtered curve for both line types. Significant differences were found between defined horizontal and vertical spiral sections within each group and between both line types within the ET group. A significant correlation was found between spiral and vertical line deviations from filtered curve outcome measures. Achieving objective measures about the significance of drawing directions on actual performance may support the clinical evaluation of people with ET toward developing future intervention methods for improving their functional abilities.

Wearable Devices for Assessment of Tremor

Tremor is an impairing symptom associated with several neurological diseases. Some of such diseases are neurodegenerative, and tremor characterization may be of help in differential diagnosis. To date, electromyography (EMG) is the gold standard for the analysis and diagnosis of tremors. In the last decade, however, several studies have been conducted for the validation of different techniques and new, non-invasive, portable, or even wearable devices have been recently proposed as complementary tools to EMG for a better characterization of tremors. Such devices have proven to be useful for monitoring the efficacy of therapies or even aiding in differential diagnosis. The aim of this review is to present systematically such new solutions, trying to highlight their potentialities and limitations, with a hint to future developments.

Wearable Peripheral Electrical Stimulation Devices for the Reduction of Essential Tremor: A Review

Essential tremor is the most common pathological tremor, with a prevalence of 6.3% in people over 65 years of age. This disorder interferes with a patient's ability to carry out activities of daily living independently, and treatment with medical and surgical interventions is often insufficient or contraindicated. Mechanical orthoses have not been widely adopted by patients due to discomfort and lack of discretion. Over the past 30 years, peripheral electrical stimulation has been investigated as a possible treatment for patients who have not found other treatment options to be satisfactory, with wearable devices revolutionizing this emerging approach in recent years. In this paper, an overview of essential tremor and its current medical and surgical treatment options are presented. Following this, tremor detection, measurement and characterization methods are explored with a focus on the measurement options that can be incorporated into wearable devices. Then, novel interventions for essential tremor are described, with a detailed review of open and closed-loop peripheral electrical stimulation methods. Finally, discussion of the need for wearable closed-loop peripheral electrical stimulation devices for essential tremor, approaches in their implementation, and gaps in the literature for further research are presented.

Tremor assessment using smartphone sensor data and fuzzy reasoning

Background

Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized analyses. The goal of this work is to assess severity of tremor objectively, to be better able to asses improvement in ET patients due to deep brain stimulation or other treatments.

Methods

We collect tremor data by strapping smartphones to the wrists of ET patients. The resulting raw sensor data is then pre-processed to remove any artifact due to patient’s intentional movement. Finally, this data is exploited to automatically build a transparent, interpretable, and succinct fuzzy model for the severity assessment of ET. For this purpose, we exploit pyFUME, a tool for the data-driven estimation of fuzzy models. It leverages the FST-PSO swarm intelligence meta-heuristic to identify optimal clusters in data, reducing the possibility of a premature convergence in local minima which would result in a sub-optimal model. pyFUME was also combined with GRABS, a novel methodology for the automatic simplification of fuzzy rules.

Results

Our model is able to assess tremor severity of patients suffering from Essential Tremor, notably without the need for subjective questionnaires nor interviews. The fuzzy model improves the mean absolute error (MAE) metric by 78–81% compared to linear models and by 71–74% compared to a model based on decision trees.

Conclusion

This study confirms that tremor data gathered using the smartphones is useful for the constructing of machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor. The model produced by our methodology is easy to inspect and, notably, characterized by a lower error with respect to approaches based on linear models or decision trees.

A Low-g MEMS Accelerometer with High Sensitivity, Low Nonlinearity and Large Dynamic Range Based on Mode-Localization of 3-DoF Weakly Coupled Resonators

This paper presents a new design of microelectromechanical systems (MEMS) based low-g accelerometer utilizing mode-localization effect in the three degree-of-freedom (3-DoF) weakly coupled MEMS resonators. Two sets of the 3-DoF mechanically coupled resonators are used on either side of the single proof mass and difference in the amplitude ratio of two resonator sets is considered as an output metric for the input acceleration measurement. The proof mass is electrostatically coupled to the perturbation resonators and for the sensitivity and input dynamic range tuning of MEMS accelerometer, electrostatic electrodes are used with each resonator in two sets of 3-DoF coupled resonators. The MEMS accelerometer is designed considering the foundry process constraints of silicon-on-insulator multi-user MEMS processes (SOIMUMPs). The performance of the MEMS accelerometer is analyzed through finite-element-method (FEM) based simulations. The sensitivity of the MEMS accelerometer in terms of amplitude ratio difference is obtained as 10.61/g for an input acceleration range of ±2 g with thermomechanical noise based resolution of 0.22 μg/Hz and nonlinearity less than 0.5%.

A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson's Patients

Parkinson's disease patients face numerous motor symptoms that eventually make their life different from those of normal healthy controls. Out of these motor symptoms, tremor and bradykinesia, are relatively prevalent in all stages of this disease. The assessment of these symptoms is usually performed by traditional methods where the accuracy of results is still an open question. This research proposed a solution for an objective assessment of tremor and bradykinesia in subjects with PD (10 older adults aged greater than 60 years with tremor and 10 older adults aged greater than 60 years with bradykinesia) and 20 healthy older adults aged greater than 60 years. Physical movements were recorded by means of an AWEAR bracelet developed using inertial sensors, i.e., 3D accelerometer and gyroscope. Participants performed upper extremities motor activities as adopted by neurologists during the clinical assessment based on Unified Parkinson's Disease Rating Scale (UPDRS). For discriminating the patients from healthy controls, temporal and spectral features were extracted, out of which non-linear temporal and spectral features show greater difference. Both supervised and unsupervised machine learning classifiers provide good results. Out of 40 individuals, neural net clustering discriminated 34 individuals in correct classes, while the KNN approach discriminated 91.7% accurately. In a clinical environment, the doctor can use the device to comprehend the tremor and bradykinesia of patients quickly and with higher accuracy.

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility of using accelerometric data to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positions with a sampling frequency of 60 Hz. The deep learning system uses all of the frequency components in a range of 〈0,30 〉 Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.

Effects of Deep Dry Needling on Tremor Severity and Functionality in Stroke: A Case Report

This study aimed to determine the effect of one session of dry needling on the severity of tremor, motor function and skills, and quality of life of a 39-year-old woman with post-stroke tremor. Myofascial trigger points (MTrP) of the following muscles were treated: extensor digitorum, flexor digitorum superficialis and profundus, brachioradialis, short head of biceps brachii, long head of triceps brachii, mid deltoid, infraspinatus, teres minor, upper trapezius, and supraspinatus. Outcomes were assessed via (i) clinical scales (activity of daily living (ADL-T24), a visual analog scale (VAS), and the Archimedes spiral), (ii) a functional test (9-Hole Peg test), and (iii) biomechanical and neurophysiological measurements (inertial sensors, electromyography (EMG), and dynamometry). The subject showed a decrease in the severity of tremor during postural (72.7%) and functional (54%) tasks after treatment. EMG activity decreased after the session and returned to basal levels 4 days after. There was an improvement post-intervention (27.84 s) and 4 days after (32.43 s) in functionality and manual dexterity of the affected limb, measured with the 9-Hole Peg test, as well as in the patient's hand and lateral pinch strength after the treatment (26.9% and 5%, respectively), that was maintained 4 days later (15.4% and 16.7%, respectively).

A Fabric-Based Textile Stretch Sensor for Optimized Measurement of Strain in Clothing

Fabric stretch sensors are available as planar fabrics, but their reliability and reproducibility are low. To find a good working setup for use in an elastic sports garment, the design of such sensors must be optimized. The main purpose of this study was to develop resistive strain sensors from stretchable conductive fabric and investigating the influence of stretchability on conductivity/resistivity. The influence of using the sensor in a sweat rich environment was also determined, in order to evaluate the potential use of the sensor in sporting garments. The sensor resistivity performance was analyzed for its sensitivity, working range, and repeatability and it was determined what makes the sensitivity when elongated or stretched. The resistivity was found to decrease with elongation if no sweat is present, this can be due to molecular rearrangement and a higher degree of orientation that improves the conductivity of a material. The result from this finding also shows that for wearable applications the commercial EeonTex^TM conductive stretchable fabric did not show a considerable resistivity increase, nor a good sensitivity. The sensitivity of the sensor was between 0.97 and 1.28 and varies with different elongation %. This may be due to the mechanical deformation characteristics of knitted samples that lead to changes in conductivity. We advise that the testing performed in this paper is done by default on new stretch sensitive textile materials, so practical use of the material can be correctly estimated.

Quantifying Tremor in Essential Tremor Using Inertial Sensors-Validation of an Algorithm

Background Assessment of essential tremor is often done by a trained clinician who observes the limbs during different postures and actions and subsequently rates the tremor. While this method has been shown to be reliable, the inter- and intra-rater reliability and need for training can make the use of this method for symptom progression difficult. Many limitations of clinical rating scales can potentially be overcome by using inertial sensors, but to date many algorithms designed to quantify tremor have key limitations. Methods We propose a novel algorithm to characterize tremor using inertial sensors. It uses a two-stage approach that 1) estimates the tremor frequency of a subject and only quantifies tremor near that range; 2) estimates the tremor amplitude as the portion of signal power above baseline activity during recording, allowing tremor estimation even in the presence of other activity; and 3) estimates tremor amplitude in physical units of translation (cm) and rotation (°), consistent with current tremor rating scales. We validated the algorithm technically using a robotic arm and clinically by comparing algorithm output with data reported by a trained clinician administering a tremor rating scale to a cohort of essential tremor patients. Results Technical validation demonstrated rotational amplitude accuracy better than ±0.2 degrees and position amplitude accuracy better than ±0.1 cm. Clinical validation revealed that both rotation and position components were significantly correlated with tremor rating scale scores. Conclusion We demonstrate that our algorithm can quantify tremor accurately even in the presence of other activities, perhaps providing a step forward for at-home monitoring.

Local Vibrational Therapy for Essential Tremor Reduction: A Clinical Study

Background and objectives: tremor is an unintentional and rhythmic movement of any part of the body that is a typical symptom of Essential Tremor (ET). ET impairs the quality of life of patients and is treated with pharmacotherapy. We investigated the tremor reduction efficacy of an innovative vibrational medical device (IMD) in ET patients. Materials and Methods: we conducted a prospective, single-center, single-arm, pragmatic study in ET patients with an extended safety study to evaluate the efficacy and safety of the Vilim Ball-a local hand-arm vibration device that produces vibrations in the frequency range of 8-18 Hz and amplitude from 0 to 2 mm. The primary endpoint was the decrease in the power spectrum after device use. The secondary endpoints were safety outcomes. Results: In total, 17 patients with ET were included in the main study, and no patients withdrew from the main study. The tremor power spectrum (m²/s³ Hz) was lower after the device use, represented as the mean (standard deviation): 0.106 (0.221); median (Md) 0.009 with the interquartile range; IQR, 0.087 vs. 0.042 (0.078); Md = 0.009 with the IQR 0.012; Wilcoxon signed-rank test V = 123; and p = 0.027. Seven patients reported that vibrational therapy was not effective. Two patients reported an increase in tremor after using the device. In the extended safety study, we included 51 patients: 31 patients with ET and 20 with Parkinsonian tremor, where 48 patients reported an improvement in tremor symptoms and 49 in function. No serious adverse events were reported, while two patients in the Parkinsonian tremor group reported a lack of efficacy of the proposed medical device. Conclusions: the device reduces essential tremor in some patients and is safe to use in ET.

Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects

Parkinson's disease (PD) decreases the quality of life of the affected individuals. The incidence of PD is expected to increase given the growing aging population. Motor symptoms associated with PD render the patients unable to self-care and function properly. Given that several drugs have been developed to control motor symptoms, highly sensitive scales for clinical evaluation of drug efficacy are needed. Among such scales, the objective and continuous evaluation of wearable devices is increasingly utilized by clinicians and patients. Several electronic technologies have revolutionized the clinical monitoring of PD development, especially its motor symptoms. Here, we review and discuss the recent advances in the development of wearable devices for bradykinesia, tremor, gait, and myotonia. Our aim is to capture the experiences of patients and clinicians, as well as expand our understanding on the application of wearable technology. In so-doing, we lay the foundation for further research into the use of wearable technology in the management of PD.

Quantitative Assessment of Head Tremor in Patients with Essential Tremor and Cervical Dystonia by Using Inertial Sensors

Tremor is most common among the movement disabilities that affect older people, having a prevalence rate of 4.6% in the population older than 65 years. Despite this, distinguishing different types of tremors is clinically challenging, often leading to misdiagnosis. However, due to advances in microelectronics and wireless communication, it is now possible to easily monitor tremor in hospitals and even in home environments. In this paper, we propose an architecture of a system for remote health-care and one possible implementation of such system focused on head tremor monitoring. In particular, the aim of the study presented here was to test new tools for differentiating essential tremor from dystonic tremor. To that aim, we propose a number of temporal and spectral features that are calculated from measured gyroscope signals, and identify those that provide optimal differentiation between two groups. The mean signal amplitude feature results in sensitivity = 0.8537 and specificity = 0.8039 in distinguishing patients having cervical dystonia with or without tremor. In addition, mean signal amplitude was shown to be significantly higher in patients with essential tremor than in patients with cervical dystonia, whereas the mean peak frequency is not different between two groups.

Lamotrigine Induces Tremor among Epilepsy Patients Probably via Cerebellar Pathways

Lamotrigine, a frequently used antiepileptic drug, inhibits voltage-gated sodium-channels. By suppressing the release of glutamate and aspartate, lamotrigine acts as a membrane stabilizer, and it is also effective in bipolar disorder and migraine. However, lamotrigine is known to induce tremor among 4-10% of patients. We examined the lamotrigine-induced tremor in 28 epilepsy patients (age: 38.06 ± 13.56 years; 24 females and 4 males) receiving lamotrigine monotherapy and compared the data to 30 age- and sex-matched controls (age: 33.06 ± 10.71 years; 25 females and 5 males). Tremor was visually assessed by clinical tremor rating scales. Quantitative characteristics (intensity, center frequency and frequency dispersion) which are regularly used to differentiate various tremor syndromes were measured by validated, sensitive biaxial accelerometry in resting, postural and intentional positions. Regularity of repetitive finger and hand movements and reaction time were also determined. Data were statistically analyzed. Clinical tremor rating scales detected pathological tremor in three patients (10%), while accelerometry revealed tremor in seven patients (25%). Center frequency of patients with pathological tremor was similar to controls, but the frequency dispersion was significantly lower and tremor intensity was significantly higher in both postural and intentional positions. Rhythmic movements and reaction time were normal. Our results show that objective measurements detect pathological intention tremor in 25% of epilepsy patients receiving lamotrigine monotherapy. Quantitative characteristics suggest the involvement of the cerebellum in the pathomechanism of lamotrigine-induced tremor. Determining the parameters of drug-induced tremor syndromes might help to understand the complex action of tremor generator networks.

Personalised profiling to identify clinically relevant changes in tremor due to multiple sclerosis

Background

There is growing interest in sensor-based assessment of upper limb tremor in multiple sclerosis and other movement disorders. However, previously such assessments have not been found to offer any improvement over conventional clinical observation in identifying clinically relevant changes in an individual’s tremor symptoms, due to poor test-retest repeatability.

Method

We hypothesised that this barrier could be overcome by constructing a tremor change metric that is customised to each individual’s tremor characteristics, such that random variability can be distinguished from clinically relevant changes in symptoms. In a cohort of 24 people with tremor due to multiple sclerosis, the newly proposed metrics were compared against conventional clinical and sensor-based metrics. Each metric was evaluated based on Spearman rank correlation with two reference metrics extracted from the Fahn-Tolosa-Marin Tremor Rating Scale: a task-based measure of functional disability (FTMTRS B) and the subject’s self-assessment of the impact of tremor on their activities of daily living (FTMTRS C).

Results

Unlike the conventional sensor-based and clinical metrics, the newly proposed ’change in scale’ metrics presented statistically significant correlations with changes in self-assessed impact of tremor (maxR²>0.5,p<0.05 after correction for false discovery rate control). They also outperformed all other metrics in terms of correlations with changes in task-based functional performance (R²=0.25 vs. R²=0.15 for conventional clinical observation, both p<0.05).

Conclusions

The proposed metrics achieve an elusive goal of sensor-based tremor assessment: improving on conventional visual observation in terms of sensitivity to change. Further refinement and evaluation of the proposed techniques is required, but our core findings imply that the main barrier to translational impact for this application can be overcome. Sensor-based tremor assessments may improve personalised treatment selection and the efficiency of clinical trials for new treatments by enabling greater standardisation and sensitivity to clinically relevant changes in symptoms.

Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review

Motion capture systems are recognized as the gold standard for joint angle calculation. However, studies using these systems are restricted to laboratory settings for technical reasons, which may lead to findings that are not representative of real-life context. Recently developed commercial and home-made inertial measurement sensors (M/IMU) are potentially good alternatives to the laboratory-based systems, and recent technology improvements required a synthesis of the current evidence. The aim of this systematic review was to determine the criterion validity and reliability of M/IMU for each body joint and for tasks of different levels of complexity. Five different databases were screened (Pubmed, Cinhal, Embase, Ergonomic abstract, and Compendex). Two evaluators performed independent selection, quality assessment (consensus-based standards for the selection of health measurement instruments [COSMIN] and quality appraisal tools), and data extraction. Forty-two studies were included. Reported validity varied according to task complexity (higher validity for simple tasks) and the joint evaluated (better validity for lower limb joints). More studies on reliability are needed to make stronger conclusions, as the number of studies addressing this psychometric property was limited. M/IMU should be considered as a valid tool to assess whole body range of motion, but further studies are needed to standardize technical procedures to obtain more accurate data.

Deep Brain Stimulation of the Caudal Zona Incerta and Motor Thalamus for Postischemic Dystonic Tremor of the Left Upper Limb: Case Report and Review of the Literature

BACKGROUND

Dystonic tremor is defined as a tremor occurring in a body region affected by dystonia. The pathophysiologic mechanisms behind dystonic tremor supposedly involve anomalies affecting the pallidothalamic-receiving area (for the dystonic component) and the ventralis intermedius-cortical loop (for the tremor component). Interest in posterior subthalamic area stimulation for various types of involuntary abnormal movements has arisen owing to positive results in patients affected by tremor refractory to ventralis intermedius deep brain stimulation.

CASE DESCRIPTION

A 23-year-old man, with a 15-year history of left upper limb dystonic tremor due to a stroke in the right thalamus, underwent deep brain stimulation with a single electrode passing through the right ventralis oralis anterior/ventralis oralis posterior nuclei and caudal zona incerta. Objective movement outcomes were assessed through the Unified Dystonia Rating Scale and Fahn-Tolosa-Marin Clinical Rating Scale for Tremor. The impact of tremor on activities of daily living was assessed with the ADL-T24 questionnaire, and quality of life was assessed with the Quality of Life Scale. All questionnaires were administered before deep brain stimulation and at 5-year follow-up. Unified Dystonia Rating Scale and Fahn-Tolosa-Marin Clinical Rating Scale for Tremor scores decreased from 14.5 to 4.5 and from 46 to 7, respectively. ADL-T24 score decreased from 19 to 3, whereas Quality of Life Scale score increased from 49 to 82.

CONCLUSIONS

Stimulation of motor thalamus and caudal zona incerta could be a viable treatment for patients affected by tremor of various origins, including dystonic tremor, refractory to medical therapy.

Frequency response of the leap motion controller and its suitability for measuring tremor

Although tremor is one of the most common movement disorders, it is evaluated using relatively coarse clinical scales. We propose to measure tremor in clinical settings using the Leap Motion Controller (LMC), which is a markerless motion capture sensor that has a low cost, zero set-up time, and dynamic accuracy of 1.2 mm. However, the frequency response of the LMC has not been characterized, so its ability to track oscillations such as tremor is unknown. To characterize the frequency response of the LMC, we measured the position of a mannequin hand simultaneously with the LMC and a high-resolution encoder while the mannequin hand oscillated at various combinations of frequency (1-15 Hz) and amplitudes (0.01-30 mm). We calculated the magnitude ratio and phase shift of the LMC and found the bandwidth of the LMC to range from 0-3 Hz to 0-5 Hz for tremor amplitudes greater than the dynamic accuracy. This bandwidth is too small to accurately measure most tremors. However, we developed an inverse filter to estimate the actual tremor amplitude and phase despite the limited bandwidth. Over the combinations of frequency and amplitude mentioned above, the inverse filter estimated the actual tremor amplitude and phase with errors of 3% and 2%, respectively.

Characterization of Parkinsonian Hand Tremor and Validation of a High-Order Tremor Estimator

Recent progress in wearable technology has made wearable tremor suppression devices (WTSDs) for Parkinson's patients a potentially viable alternative solution for tremor management. So far, in contrast to wrist and elbow tremor, finger tremors have not been studied in depth despite the huge impact that they have on a patient's daily life. In addition, more evidence has been found showing that the performance of current tremor estimators may be limited by their model order due to the multiple harmonics present in tremor. The aim of this paper is to characterize finger and wrist tremor in both the time and frequency domains, and to propose a high-order tremor estimation algorithm. Tremor magnitudes are reported in the forms of linear acceleration, angular velocity, and angular displacement. The activation of forearm flexor and extensor muscles is also investigated. The frequency analysis shows that Parkinsonian tremors produce oscillations of the hand with pronounced harmonics. At last, a high-order weighted-frequency Fourier linear combiner (WFLC)-based Kalman filter is proposed. The percentage estimation accuracy achieved from the proposed estimator is 96.3 ± 1.7%, showing average improvements of 28.5% and 48.9% over its lower-order counterpart and the WFLC. The proposed estimator shows promise for use in a WTSD.

Quantification characteristics of digital spiral analysis for understanding the relationship among tremor and clinical measures in persons with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a degenerative neurological condition causing demyelination and neuronal loss. Tremor, a symptom of MS, is prevalent in 45.0-46.8% NARCOMS registrants. Although several tools to measure tremor exist, few outcomes are quantitative or regularly utilized clinically.

NEW METHOD

Introduction of a novel adaptation of the digital spiral drawing to find a quick, sensitive, and clinically useful technique, to predict tremor in persons with MS (pwMS). Digital spiral measures included: Segment Rate (SEGRT), Standard Deviation (SD) of Radial Velocity (VSD-R), SD of Tangential Velocity (VSD-T), SD of Overall Velocity (VSD-O), Mean Drawing Velocity (MNV-O) and Mean Pen Pressure Acceleration (MNA-P). Digital spiral measures were compared with the manual Archimedes Spiral (AS) drawing and the following clinical measures: Finger-Nose Test (FNT), presence of visually observed intention tremor (VOT), Nine-Hole Peg Test (NHPT), and Box and Block Test (BBT).

RESULTS

All clinical measures utilized demonstrated significant relationships with all digital variables, except VSD-R. The forward-stepwise regression revealed BBT accounted for the most variance, followed by SEGRT. Comparison with Existing Methods: SEGRT is more sensitive in detecting VOT and better for quantifying tremor than AS. BBT and SEGRT are optimal predictive measures for tremor.

CONCLUSIONS

SEGRT has stronger sensitivity and negative predictive value than AS in detecting VOT. All clinical measures (NHPT, FNT, BBT, and AS) were significantly associated with the digital variables (SEGRT, VSD-T, VSD-O, MNV-O, and MNA-P) except for VSD-R. After controlling for Patient Determined Disease Steps (PDDS), BBT and SEGRT are the best predictive measures for tremor.

Quantitative Analysis of Parkinsonian Tremor in a Clinical Setting Using Inertial Measurement Units

Background

Parkinson's disease (PD) is a neurodegenerative disorder that affects human voluntary movements. Tremor is one of the most common symptoms of PD and is expressed as involuntary oscillation of the body. Tremors can be analysed in the frequency domain.

Objective

The aim of the current study was to examine selected tremor parameters (frequency, root mean square, and approximated entropy) in order to quantify the characteristics of patients diagnosed with PD, compared to a healthy control group, and to compare the parameters by dividing the subjects according to UPDRS assessment.

Methods

The subjects were divided into two groups: a group of people diagnosed with PD (n = 19) and a control group consisting of healthy volunteers (CO = 12). Each subject performed motor tasks specific to certain tremors: the finger-to-nose test. Each subject performed a motor task three times. A nine degree of freedom (DOF) wireless inertial measurement unit was used for the measurement of upper limb motor tasks. For the quantitative estimation of kinetic and postural tremors, dominant frequency, root means square, and approximation entropy were selected and calculated from the measured angular velocity and linear acceleration signals. A one-way ANOVA with a significance level of α = 0.05 was used to test the null hypothesis that the means of the tremor metrics were the same between the PD and CO groups.

Results

Statistically significant differences between PD patients and control groups were observed in ApEn acceleration signal of kinetic tremor, ApEn angular velocity signal of kinetic tremor, ApEn angular velocity of postural tremor, frequency acceleration signal of postural tremor, and RMS angular speed kinetic tremor.

Conclusion

Application of inertial measurement units for clinical research of patients and PD tremor evaluation allows providing quantitative information for diagnostic purposes, during screening in a clinical setting that differentiates between PD patients and controls.

A digital assessment system for evaluating kinetic tremor in essential tremor and Parkinson's disease

Background

Spiral drawing on papers is a common tremor evaluation tool for diagnosing patients with essential tremor (ET) or Parkinson’s disease (PD). No standard drawing methods and parameters that use graphic tablets are yet available for objective evaluation.

Methods

This study established a tremor assessment system for tremor severity by using graphic tablets. Twelve patients with ET and twelve patients with PD were tested to establish system algorithms, and six additional patients were tested with the developed system to evaluate its performance. The patients also performed spiral drawing with three guiding paradigms on a graphic tablet: traced along a given spiral (S1), performed freehand drawing (S2), and traced along a guiding point (S3). Three parameters were calculated to quantify tremor severity: the means of radial difference per radian (|dr/dθ|), the means of radial difference per second (|dr/dt|), and the area under curve (AUC) of the frequency spectrum of the velocity. Each patient’s drawing was also evaluated using a visual rating scale (VRS) by experienced physicians. The interrater reliability was examined to identify the most consistent test paradigm.

Results

The parameter |dr/dθ| and AUC correlated well with the VRS (R > 0.8) in S1, S2 and S3 tests. The S1 test presented the best interrater reliability (Weighted Kappa coefficient, k = 0.80) among three tests. The Weighted Kappa coefficients are 0.67 and 0.71 in S2 and S3 tests, respectively.

Conclusions

We developed three different guiding paradigms for spiral drawing on a digital graphic tablet for clinical tests. Three parameters were calculated to represent the tremor severity in spiral drawing and used to quantify temporal and spatial characteristics of tremor, and provided good correlation with current clinical assessments. The test “traced along a given spiral” is recommended due to its good interrater reliability.

Electronic supplementary material

The online version of this article (10.1186/s12883-018-1027-2) contains supplementary material, which is available to authorized users.

Towards remote monitoring of Parkinson's disease tremor using wearable motion capture systems

The management of movement disorders is shifting from a centralized-clinical assessment towards remote monitoring and individualized therapy. While a variety of treatment options are available, ranging from pharmaceutical drugs to invasive neuromodulation, the clinical effects are inconsistent and often poorly measured. For instance, the lack of remote monitoring has been a major limitation to optimize therapeutic interventions for patients with Parkinson's Disease (PD). In this work, we focus on the assessment of full-body tremor as the most recognized PD symptom. Forty PD and twenty two healthy participants were recruited. The main assessment tool was an inertial measurement unit (IMU)-based motion capture system to quantify full-body tremor and to separate tremor-dominant from non-tremor-dominant PD patients as well as from healthy controls. We developed a new measure and evaluated its clinical utility by correlating the results with the Unified Parkinson's Disease Rating Scale (UPDRS) scores as the gold standard. Significant correlation was observed between the UPDRS and the tremor severity scores for the selected tasks. The results suggest that it is feasible and clinically meaningful to utilize the suggested objective tremor score for the assessment of PD patients. Furthermore, this portable assessment tool could potentially be used in the home environment to monitor PD tremor and facilitate optimizing therapeutic interventions.

A Solution-Processable, Omnidirectionally Stretchable, and High-Pressure-Sensitive Piezoresistive Device

The development of omnidirectionally stretchable pressure sensors with high performance without stretching-induced interference has been hampered by many challenges. Herein, an omnidirectionally stretchable piezoresistive pressure-sensing device is demonstrated by combining an omniaxially stretchable substrate with a 3D micropattern array and solution-printing of electrode and piezoresistive materials. A unique substrate structural design and materials mean that devices that are highly sensitive are rendered, with a stable out-of-plane pressure response to both static (sensitivity of 0.5 kPa^-1 and limit of detection of 28 Pa) and dynamic pressures and the minimized in-plane stretching responsiveness (a small strain gauge factor of 0.17), achieved through efficient strain absorption of the electrode and sensing materials. The device can detect human-body tremors, as well as measure the relative elastic properties of human skin. The omnidirectionally stretchable pressure sensor with a high pressure sensitivity and minimal stretch-responsiveness yields great potential to skin-attachable wearable electronics, human-machine interfaces, and soft robotics applications.

Evaluation of a laser-based sensor for the diagnosis of neurological disorders

Involuntary muscle activities like fasciculations or tremor are an indication for several neurological disorders. However, currently used techniques for measuring those activities are limited due to their invasiveness, the unsuitability for measuring a whole body simultaneously and the lack of an objective measurement of amplitude and duration of muscle activity. Hence, we developed a new laser-based sensor for the remote quantification of muscle activity. In the present paper we show a basic evaluation of our system by reference to ultrasound measurements. Our results show the detection limits of our remote sensor technology in terms of fasciculation size and depth within the muscle. Those results will help us for a better interpretation of our measurement results and hold promise for the future development of our system.

A Postural Tremor Highly Responsive to Transcranial Cerebello-Cerebral DCS in ARCA3

Background and objectives

Cerebellar ataxias are disabling disorders that impact the quality of life of patients. In many cases, an effective treatment is missing. Despite the increasing knowledge on the pathogenesis of cerebellar disorders including genetic aspects, there is currently a gap in the therapeutical management of cerebellar deficits. Cerebellar ataxia associated with ANO10 mutation (ARCA3) presents a disabling cerebellar syndrome. The aim of this study is to report a patient with a marked postural tremor responding to transcranial cerebello-cerebral direct current stimulation (tCCDCS).

Methods

We applied tCCDCS using anodal stimulation over the cerebellum with a return electrode on the contralateral motor cortex. We performed a clinical rating, accelerometry studies, and recordings of voluntary movements at baseline, after sham, and after active tCCDCS.

Results

A dramatic response of postural tremor was observed after tCCDCS, with a major drop of the power spectral density to 26.12% of basal values.

Discussion

The postural tremor of cerebellar ataxia associated with ANO10 mutation was highly responsive to tCCDCS in our patient. This case illustrates that tCCDCS is a novel therapeutic option in the treatment of cerebellar deficits and might represent a promising tool to reduce tremor in ARCA3.

Development of method for quantifying essential tremor using a small optical device

BACKGROUND

Clinical assessment scales are the most common means used by physicians to assess tremor severity. Some scientific tools that may be able to replace these scales to objectively assess the severity, such as accelerometers, digital tablets, electromyography (EMG) measurement devices, and motion capture cameras, are currently available. However, most of the operational modes of these tools are relatively complex or are only able to capture part of the clinical information; furthermore, using these tools is sometimes time consuming. Currently, there is no tool available for automatically quantifying tremor severity in clinical environments.

NEW METHOD

We aimed to develop a rapid, objective, and quantitative system for measuring the severity of finger tremor using a small portable optical device (Leap Motion).

RESULTS

A single test took 15s to conduct, and three algorithms were proposed to quantify the severity of finger tremor. The system was tested with four patients diagnosed with essential tremor.

COMPARISON WITH EXISTING METHOD

The proposed algorithms were able to quantify different characteristics of tremor in clinical environments, and could be used as references for future clinical assessments.

CONCLUSIONS

A portable, easy-to-use, small-sized, and noncontact device (Leap Motion) was used to clinically detect and record finger movement, and three algorithms were proposed to describe tremor amplitudes.

Approach to a tremor patient

Tremors are commonly encountered in clinical practice and are the most common movement disorders seen. It is defined as a rhythmic, involuntary oscillatory movement of a body part around one or more joints. In the majority of the population, tremor tends to be mild. They have varying etiology; hence, classifying them appropriately helps in identifying the underlying cause. Clinically, tremor is classified as occurring at rest or action. They can also be classified based on their frequency, amplitude, and body part involved. Parkinsonian tremor is the most common cause of rest tremor. Essential tremor (ET) and enhanced physiological tremor are the most common causes of action tremor. Isolated head tremor is more likely to be dystonic rather than ET. Isolated voice tremor could be considered to be a spectrum of ET. Psychogenic tremor is not a diagnosis of exclusion; rather, demonstration of various clinical signs is needed to establish the diagnosis. Severity of tremor and response to treatment can be assessed using clinical rating scales as well as using electrophysiological measurements. The treatment of tremor is symptomatic. Medications are effective in half the cases of essential hand tremor and in refractory patients; deep brain stimulation is an alternative therapy. Midline tremors benefit from botulinum toxin injections. It is also the treatment of choice in dystonic tremor and primary writing tremor.

Hilbert-Huang transform based instrumental assessment of intention tremor in multiple sclerosis

OBJECTIVE

This paper describes a method to extract upper limb intention tremor from gyroscope data, through the Hilbert-Huang transform (HHT), a technique suitable for the study of nonlinear and non-stationary processes. The aims of the study were to: (i) evaluate the method's ability to discriminate between healthy controls and MS subjects; (ii) validate the proposed procedure against clinical tremor scores assigned using Fahn's tremor rating scale (FTRS); and (iii) compare the performance of the HHT-based method with that of linear band-pass filters.

APPROACH

HHT was applied on gyroscope data collected on 20 MS subjects and 13 healthy controls (CO) during finger-to-nose tests (FNTs) instrumented with an inertial sensor placed on the hand. The results were compared to those obtained after traditional linear filtering. The tremor amplitude was quantified with instrumental indexes (TIs) and clinical FTRS ratings.

MAIN RESULTS

The TIs computed after HHT-based filtering discriminated between CO and MS subjects with clinically-detected intention tremor (MS_T). In particular, TIs were significantly higher in the final part of the movement (TI2) with respect to the first part (TI1), and, for all components (X, Y, Z), MS_T showed a TI2 significantly higher than in CO subjects. Moreover, the HHT detected subtle alterations not visible from clinical ratings, as TI2 (Z-component) was significantly increased in MS subjects without clinically-detected tremor (MS_NT). The method's validity was demonstrated by significant correlations between clinical FTRS scores and TI2 related to X (rs = 0.587, p = 0.006) and Y (rs = 0.682, p < 0.001) components. Contrarily, fewer differences among the groups and no correlation between instrumental and clinical indexes emerged after traditional filtering.

SIGNIFICANCE

The present results supported the use of the HHT-based procedure for a fully-automated quantitative and objective measure of intention tremor in MS, which can overcome the limitations of clinical scales and provide supplementary information about this sign.