Quantitative Assessment of Parkinsonian Tremor Based on an Inertial Measurement Unit

A Systematic Review of Oral Vertical Dyskinesia ("Rabbit" Syndrome)

Background and Objectives: Vertical rhythmic dyskinetic movements that are primarily drug-induced and affect solely the jaw, mouth, and lips without involving the tongue have been historically described as "rabbit" syndrome (RS). Evidence on the unique features and implications of this disorder remains limited. This literature review aims to evaluate the clinical-epidemiological profile, pathological mechanisms, and management of this movement disorder. Materials and Methods: Two reviewers identified and assessed relevant reports in six databases without language restriction published between 1972 and 2024. Results: A total of 85 articles containing 146 cases of RS were found. The mean frequency of RS among adults in psychiatric hospitals was 1.2% (range 0-4.4%). The mean age of affected patients was 49.2 (SD: 17.5), and 63.6% were females. Schizophrenia was the most frequent comorbidity found in 47.6%, followed by bipolar disorder (17.8%), major depressive disorder (10.3%), and obsessive-compulsive disorder (3.7%). Five cases were idiopathic. The most common medications associated with RS were haloperidol (17%), risperidone (14%), aripiprazole (7%), trifluoperazine (5%), and sulpiride (5%). The mean duration of pharmacotherapy before RS was 21.4 weeks (SD: 20.6). RS occurred in association with drug-induced parkinsonism (DIP) in 27.4% and with tardive dyskinesia (TD) in 8.2% of cases. Antipsychotic modification and/or anticholinergic drugs resulted in full or partial recovery in nearly all reported cases in which they were prescribed. Conclusions: RS occurs as a distinct drug-induced syndrome associated primarily but not exclusively with antipsychotics. Distinguishing RS from TD is important because the treatment options for the two disorders are quite different. By contrast, RS may be part of a spectrum of symptoms of DIP with similar course, treatment outcomes, and pathophysiology.

Multi-Shared-Task Self-Supervised CNN-LSTM for Monitoring Free-Body Movement UPDRS-III Using Wearable Sensors

The Unified Parkinson's Disease Rating Scale (UPDRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity. The rating is crucial for disease progression monitoring and treatment adjustment. This study aims to advance the capabilities of PD management by developing an innovative framework that integrates deep learning with wearable sensor technology to enhance the precision of UPDRS assessments. We introduce a series of deep learning models to estimate UPDRS Part III scores, utilizing motion data from wearable sensors. Our approach leverages a novel Multi-shared-task Self-supervised Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework that processes raw gyroscope signals and their spectrogram representations. This technique aims to refine the estimation accuracy of PD severity during naturalistic human activities. Utilizing 526 min of data from 24 PD patients engaged in everyday activities, our methodology demonstrates a strong correlation of 0.89 between estimated and clinically assessed UPDRS-III scores. This model outperforms the benchmark set by single and multichannel CNN, LSTM, and CNN-LSTM models and establishes a new standard in UPDRS-III score estimation for free-body movements compared to recent state-of-the-art methods. These results signify a substantial step forward in bioengineering applications for PD monitoring, providing a robust framework for reliable and continuous assessment of PD symptoms in daily living settings.

Neurological Examination via Telemedicine: An Updated Review Focusing on Movement Disorders

Patients with movement disorders such as Parkinson's disease (PD) living in remote and underserved areas often have limited access to specialized healthcare, while the feasibility and reliability of the video-based examination remains unclear. The aim of this narrative review is to examine which parts of remote neurological assessment are feasible and reliable in movement disorders. Clinical studies have demonstrated that most parts of the video-based neurological examination are feasible, even in the absence of a third party, including stance and gait-if an assistive device is not required-bradykinesia, tremor, dystonia, some ocular mobility parts, coordination, and gross muscle power and sensation assessment. Technical issues (video quality, internet connection, camera placement) might affect bradykinesia and tremor evaluation, especially in mild cases, possibly due to their rhythmic nature. Rigidity, postural instability and deep tendon reflexes cannot be remotely performed unless a trained healthcare professional is present. A modified version of incomplete Unified Parkinson's Disease Rating Scale (UPDRS)-III and a related equation lacking rigidity and pull testing items can reliably predict total UPDRS-III. UPDRS-II, -IV, Timed "Up and Go", and non-motor and quality of life scales can be administered remotely, while the remote Movement Disorder Society (MDS)-UPDRS-III requires further investigation. In conclusion, most parts of neurological examination can be performed virtually in PD, except for rigidity and postural instability, while technical issues might affect the assessment of mild bradykinesia and tremor. The combined use of wearable devices may at least partially compensate for these challenges in the future.

Smartwatch Versus Routine Tremor Documentation: Descriptive Comparison

We addressed the limitations of subjective clinical tremor assessment by comparing routine neurological evaluation with a Tremor Occurrence Score derived from smartwatch sensor data, among 142 participants with Parkinson disease and 77 healthy controls. Our findings highlight the potential of smartwatches for automated tremor detection as a valuable addition to conventional assessments, applicable in both clinical and home settings.

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.

Development of a New Wearable Device for the Characterization of Hand Tremor

Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.

Feasibility of Magnetic Resonance-Compatible Accelerometers to Monitor Tremor Fluctuations During Magnetic Resonance-Guided Focused Ultrasound Thalamotomy: Technical Note

BACKGROUND

Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy is used to treat essential tremor and tremor-dominant Parkinson disease. Feedback is collected throughout the procedure to verify the location of the target and completeness of response; however, variability in clinical judgments may underestimate or overestimate treatment response.

OBJECTIVE

To objectively quantify joint motion after each sonication using accelerometers secured to the contralateral upper extremity in an effort to optimize MRgFUS treatment.

METHODS

Before the procedure, 3 accelerometers were secured to the patient's arm, forearm, and index finger. Throughout the procedure, tremor motion was regularly recorded during postural and kinetic tremor testing and individual joint angle measures were modeled. The joint angle from each accelerometer was compared with baseline measurements to assess changes in angles. Subsequent adjustments to the target location and sonication energy were made at the discretion of the neurosurgeon and neuroradiologist.

RESULTS

Intraoperative accelerometer measurements of hand tremor from 18 patients provided quantified data regarding joint angle reduction: 87.3%, 94.2%, and 86.7% for signature writing, spiral drawing, and line drawing tests, respectively. Target adjustment based on accelerometer monitoring of the angle at each joint added substantial value toward achieving optimal tremor reduction.

CONCLUSION

Real-time accelerometer recordings collected during MRgFUS thalamotomy offered objective quantification of changes in joint angle after each sonication, and these findings were consistent with clinical judgments of tremor response. These results suggest that this technique could be used for fine adjustment of the location of sonication energy and number of sonications to consistently achieve optimal tremor reduction.

Parkinson's Disease Action Tremor Detection with Supervised-Leaning Models

People with Parkinson's Disease (PD) have multiple symptoms, such as freezing of gait (FoG), hand tremors, speech difficulties, and balance issues, in different stages of the disease. Among these symptoms, hand tremors are present across all stages of the disease. PD hand tremors have critical consequences and negatively impact the quality of PD patients' everyday lives. Researchers have proposed a variety of wearable devices to mitigate PD tremors. However, these devices require accurate tremor detection technology to work effectively while the tremor occurs. This paper introduces a PD action tremor detection method to recognize PD tremors from regular activities. We used a dataset from 30 PD patients wearing accelerometers and gyroscope sensors on their wrists. We selected time-domain and frequency-domain hand-crafted features. Also, we compared our hand-crafted features with existing CNN data-driven features, and our features have more specific boundaries in 2-D feature visualization using the t-SNE tool. We fed our features into multiple supervised machine learning models, including Logistic Regression (LR), K-Nearest Neighbours (KNNs), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs), for detecting PD action tremors. These models were evaluated with 30 PD patients' data. The performance of all models using our features has more than 90% of F1 scores in five-fold cross-validations and 88% F1 scores in the leave-one-out evaluation. Specifically, Support Vector Machines (SVMs) perform the best in five-fold cross-validation with over 92% F1 scores. SVMs also show the best performance in the leave-one-out evaluation with over 90% F1 scores.

Comparison of resting tremor at the upper limb joints between patients with Parkinson's disease and scans without evidence of dopaminergic deficit

BACKGROUND

A representative symptom of Parkinson's disease (PD) is resting tremor. The clinical manifestation of scans without evidence of dopaminergic deficit (SWEDD) is similar to it of PD, though the phenomenology of SWEDD is not well known.

OBJECTIVE

In the present study, the resting tremor of 9 SWEDD patients was quantitatively compared with that of 11 PD patients.

METHODS

Four 3-axis gyro sensors were attached on the index finger, thumb, dorsum of the hand, and arm of the more tremulous side. Root mean square (RMS) angular speed and angular displacement as well as irregularity of angular speed and displacement were derived from the sensor data.

RESULTS

Although disease duration and Hoehn and Yahr stages were comparable, SWEDD patients exhibited different tremor features from PD patients. Significantly faster RMS angular speed and greater RMS angular displacement (p< 0.05) were observed in PD patients than in SWEDD patients. The irregularity of angular displacement of pitch direction at the dorsum of the hand was greater in SWEDD patients than in PD patients (p< 0.05).

CONCLUSION

These results indicate that quantitative indices obtained from resting tremor task could be important biomarkers for identifying potential patients with SWEDD among patients diagnosed with PD.

Development and Assessment of a Movement Disorder Simulator Based on Inertial Data

The detection analysis of neurodegenerative diseases by means of low-cost sensors and suitable classification algorithms is a key part of the widely spreading telemedicine techniques. The choice of suitable sensors and the tuning of analysis algorithms require a large amount of data, which could be derived from a large experimental measurement campaign involving voluntary patients. This process requires a prior approval phase for the processing and the use of sensitive data in order to respect patient privacy and ethical aspects. To obtain clearance from an ethics committee, it is necessary to submit a protocol describing tests and wait for approval, which can take place after a typical period of six months. An alternative consists of structuring, implementing, validating, and adopting a software simulator at most for the initial stage of the research. To this end, the paper proposes the development, validation, and usage of a software simulator able to generate movement disorders-related data, for both healthy and pathological conditions, based on raw inertial measurement data, and give tri-axial acceleration and angular velocity as output. To present a possible operating scenario of the developed software, this work focuses on a specific case study, i.e., the Parkinson's disease-related tremor, one of the main disorders of the homonym pathology. The full framework is reported, from raw data availability to pathological data generation, along with a common machine learning method implementation to evaluate data suitability to be distinguished and classified. Due to the development of a flexible and easy-to-use simulator, the paper also analyses and discusses the data quality, described with typical measurement features, as a metric to allow accurate classification under a low-performance sensing device. The simulator's validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinson's disease tremor was greater than 98% in the best test conditions.

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.

A Wearable In-home Tremor Assessment System via Virtual Reality Environment for the Activities in Daily Lives (ADLs)

Currently available diagnostic methods for tremor movements are mostly subjective measurements, and clinicians and researchers typically diagnose patients' symptoms with provocative maneuvers, and the inter-rater and intra-rater variabilities of those methods have been always reported. Even though various sensor-based quantitative approaches have been explored, most of the tools are limited to the tremor metrics (i.e., severity and frequency). A consistent environment that can provide a test setup to evaluate how their performance is affected by the tremor movement for activities of daily living would be needed for a smart tremor diagnosis. Therefore, we developed a virtual reality environment with a custom designed wearable sensor module to quantify tremor characteristics with performance-based assessment while they perform the activities of daily living, and correlated the performance to existing tremor scores (i.e., The Essential Tremor Rating Assessment Scale (TETRAS)). We evaluated this approach with five healthy participants (no tremor), and applied an artificial tremor using a vibration motor to mimic tremor movements as a pilot study. We analyzed three categorized tremor scenarios: resting, postural, and kinetic tremor tasks using six different tasks in virtual 3D space. All the artificial tremor was score as TETRAS=1, and we successfully analyzed the tremor metrics for different tasks by comparing them with TETRAS score, and verified the different tremor characteristics with the artificial tremor. Additionally, we analyzed the performance of 3D spiral drawing on the virtual reality track using "outside area" and "completion time" as the accuracy and speed of the performance. Clinical Relevance- This can be applied to quantify and track the tremor symptom at the patients' home, and ultimately this method can be synchronized with their current treatment parameters (i.e., dosage of medication, and parameters of the stimulation) to optimize/maximize the effect of treatment.

Use of Functional Data to Model the Trajectory of an IMU and Classify Levels of Motor Impairment for Stroke Patients

Motor impairment evaluations are key rehabilitation-related assessments for patients with stroke. Currently, such evaluations are subjective; they are based on physicians' judgements regarding the actions performed by patients. This leads to inconsistent clinical results. Many inertial sensing elements for motion detection have been designed. However, to more easily and rapidly evaluate motor impairment, we require a system that can collect data effectively to predict the degree of motor impairment. Lin et al. used data gloves equipped with an inertial measurement unit (IMU) to collect movement trajectories for motor impairment evaluations in patients with stroke. The present study used functional data analysis to model data trajectories to reduce the influence of noise from IMU data and proposed using coefficients of function as features for classifying motor impairment. To verify the appropriateness of feature construction, five classification methods were used to evaluate the extracted features in terms of the overall and sensor-specific ability to classify levels of motor impairment. The results indicated that the features derived from cubic smoothing splines could effectively reflect key data characteristics, and a support vector machine yielded relatively high overall and sensor-specific accuracy for distinguishing between levels of motion impairment in patients with stroke. Future data glove systems can contain cubic smoothing splines to extract hand function features and then classify motion impairment for appropriate rehabilitation programs to be prescribed.

The impact of wearable electronics in assessing the effectiveness of levodopa treatment in Parkinsons disease

OBJECTIVE

In order to evaluate Parkinson disease patients response to therapeutic interventions, sources of information are mainly patient reports and clinicians assessment of motor functions. However, these sources can suffer from patients subjectivity and from inter/intra raters score variability. Our work aimed at determining the impact of wearable electronics and data analysis in objectifying the effectiveness of levodopa treatment.

METHODS

Seven motor tasks performed by thirty-six patients were measured by wearable electronics and related data were analyzed. This was at the time of therapy initiation (T0), and repeated after six (T1) and 12 months (T2). Wearable electronics consisted of inertial measurement units each equipped with 3-axis accelerometer and 3-axis gyroscope, while data analysis of ANOVA and Pearson correlation algorithms, in addition to a support vector machine (SVM) classification.

RESULTS

According to our findings, levodopa-based therapy alters the patients conditions in general, ameliorating something (e.g. bradykinesia), leaving unchanged others (e.g. tremor), but with poor correlation to the levodopa dose.

CONCLUSION

A technology-based approach can objectively assess levodopa-based therapy effectiveness.

SIGNIFICANCE

Novel devices can improve the accuracy of the assessment of motor function, by integrating the clinical evaluation and patient reports.

Fitts Law-Based Performance Metrics to Quantify Tremor in Individuals with Essential Tremor

Current methods of evaluating essential tremor (ET) either rely on subjective ratings or use limited tremor metrics (i.e., severity/amplitude and frequency). In this study, we explored performance metrics from Fitts law tasks that replicate and expand existing tremor metrics, to enable low-cost, home-based tremor quantification and analyze the cursor movements of individuals using a 3D mouse while performing a collection of drawing tasks. We analyzed the 3D mouse cursor movements of 11 patients with ET and three controls, on three computer-based tasksa spiral navigation (SPN) task, a rectangular track navigation (RTN) task, and multi-directional tapping/clicking (MDT)with several performance metrics (i.e., outside area (OA), throughput (TP in Fitts law), path efficiency (PE), and completion time (CT)). Using an accelerometer and scores from the Essential Tremor Rating Assessment Scale (TETRAS), we correlated the proposed performance metrics with the baseline tremor metrics and found that the OA of the SPN and RTN tasks were strongly correlated with baseline tremor severity (R²=0.57 and R²=0.83). We also found that the TP in the MDT tasks were strongly correlated with tremor frequency (R²=0.70). In addition, as the OA of the SPN and RTN tasks was correlated with tremor severity and frequency, it may represent an independent metric that increases the dimensionality of the characterization of an individuals tremor. Thus, this pilot study of the analysis of those with ET-associated tremor performing Fitts law tasks demonstrates the feasibility of introducing a new tremor metric that can be expanded for repeatable multi-dimensional data analyses.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson's disease

BACKGROUND

Resting tremor is an essential characteristic in patients suffering from Parkinson's disease (PD).

OBJECTIVE

Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring.

METHODS

Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson's Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis.

RESULTS

The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities.

CONCLUSION

The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

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.

Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease

BACKGROUND

Deep brain stimulation (DBS) is a treatment option for Parkinson's disease patients when medication does not sufficiently manage their symptoms. DBS can be a highly effect therapy, but only after a time-consuming trial-and-error stimulation parameter adjustment process that is susceptible to clinician bias. This trial-and-error process will be further prolonged with the introduction of segmented electrodes that are now commercially available. New approaches to optimizing a patient's stimulation parameters, that can also handle the increasing complexity of new electrode and stimulator designs, is needed.

METHODS

To improve DBS parameter programming, we explored two semi-automated optimization approaches: a Bayesian optimization (BayesOpt) algorithm to efficiently determine a patient's optimal stimulation parameter for minimizing rigidity, and a probit Gaussian process (pGP) to assess patient's preference. Quantified rigidity measurements were obtained using a robotic manipulandum in two participants over two visits. Rigidity was measured, in 5Hz increments, between 10-185Hz (total 30-36 frequencies) on the first visit and at eight BayesOpt algorithm-selected frequencies on the second visit. The participant was also asked their preference between the current and previous stimulation frequency. First, we compared the optimal frequency between visits with the participant's preferred frequency. Next, we evaluated the efficiency of the BayesOpt algorithm, comparing it to random and equal interval selection of frequency.

RESULTS

The BayesOpt algorithm estimated the optimal frequency to be the highest tolerable frequency, matching the optimal frequency found during the first visit. However, the participants' pGP models indicate a preference at frequencies between 70-110 Hz. Here the stimulation frequency is lowest that achieves nearly maximal suppression of rigidity. BayesOpt was efficient, estimating the rigidity response curve to stimulation that was almost indistinguishable when compared to the longer brute force method.

CONCLUSIONS

These results provide preliminary evidence of the feasibility to use BayesOpt for determining the optimal frequency, while pGP patient's preferences include more difficult to measure outcomes. Both novel approaches can shorten DBS programming and can be expanded to include multiple symptoms and parameters.

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.

Three Days of Measurement Provide Reliable Estimates of Daily Tremor Characteristics: A Pilot Study in Organic and Functional Tremor Patients

Background

Long-term tremor recording is particularly useful for the assessment of overall severity and therapeutic interventions in tremor patients. The purpose of this paper is to investigate the optimal number of days needed to obtain reliable estimates of tremor percentage, tremor frequency variability and tremor intensity in tremor patients using long-term tremor recordings.

Methods

Participants were 18 years or older and were diagnosed with tremor by a movement disorders specialist. Participants wore an accelerometer on the wrist of the most affected arm during 30 consecutive days. Tremor presence, frequency variability and intensity were calculated per day. We used reliability analysis to determine the minimum number of days needed to obtain reliable estimates of these tremor characteristics.

Results

Data from 36 adult organic (OrgT) and functional tremor (FT) patients (24 males; mean age 63.9 ± 11.9 years; 15 FT) were analyzed. Using five hours per day, one day of measurement is enough, except for tremor frequency variability in the OrgT group, where three days are needed and for tremor intensity where two days are always needed.

Discussion

Visual analysis suggested that reliability can be increased considerably by using data from three days instead of one day even when using six hours of data per day. Three days with at least three hours of tremor data provide estimates of tremor percentage, frequency variability and intensity with good to excellent reliability, both for organic and functional tremor.

Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III

BACKGROUND

Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson's disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson's disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications.

METHODS

We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time-frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time-frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data.

RESULTS

The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of [Formula: see text] and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks.

CONCLUSION

Our analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease.

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%.

Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease

Longitudinal, remote monitoring of motor symptoms in Parkinson's disease (PD) could enable more precise treatment decisions. We developed the Motor fluctuations Monitor for Parkinson's Disease (MM4PD), an ambulatory monitoring system that used smartwatch inertial sensors to continuously track fluctuations in resting tremor and dyskinesia. We designed and validated MM4PD in 343 participants with PD, including a longitudinal study of up to 6 months in a 225-subject cohort. MM4PD measurements correlated to clinical evaluations of tremor severity (ρ = 0.80) and mapped to expert ratings of dyskinesia presence (P < 0.001) during in-clinic tasks. MM4PD captured symptom changes in response to treatment that matched the clinician's expectations in 94% of evaluated subjects. In the remaining 6% of cases, symptom data from MM4PD identified opportunities to make improvements in pharmacologic strategy. These results demonstrate the promise of MM4PD as a tool to support patient-clinician communication, medication titration, and clinical trial design.

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.

Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks

Resting tremor in Parkinson's disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients' quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients' daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients' daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.

Unobtrusive detection of Parkinson's disease from multi-modal and in-the-wild sensor data using deep learning techniques

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.

Parkinson's Disease Tremor Detection in the Wild Using Wearable Accelerometers

Continuous in-home monitoring of Parkinson's Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios). We evaluate the performance of several feature sets and classification algorithms for robust PD tremor detection in laboratory and wild settings. We report results for both laboratory data with accurate labels and wild data with weak labels. The best performance was obtained using a combination of a pre-processing module to extract information from the tremor spectrum (based on non-negative factorization) and a deep neural network for learning relevant features and detecting tremor segments. We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be easier to estimate the start and end time points of each tremor event while still providing clinically useful information.

A Textile Embedded Wearable Device for Movement Disorders Quantification

Wearable devices have been showing promising results in a large range of applications: since industry, to entertainment and, in particular, healthcare. In the scope of movement disorders, wearable devices are being widely implemented for motor symptoms objective assessment. Currently, clinicians evaluate patients' motor symptoms resorting to subjective scales and visual perception, such as in Parkinson's Disease. The possibility to make use of wearable devices to quantify this disorder motor symptoms would bring an accurate follow-up on the disease progression, leading to more efficient treatments.Here we present a novel textile embedded low-power wearable device capable to be used in any scenario of movement disorders assessment due to its seamless, comfort and versatility. Regarding our research, it has already improved the setup of a wrist rigidity quantification system for Parkinson's Disease patients: the iHandU system. The wearable comprises a hardware sensing unit integrated in a textile band with an innovative design assuring higher comfort and easiness-to-use in movement disorders assessment. It enables to collect inertial data (9-axis) and has the possibility to integrate two analog sensors. A web platform was developed for data reading, visualization and recording. To ensure inertial data reliability, validation tests for the accelerometer and gyroscope sensors were conducted by comparison with its theoretical behavior, obtaining very good results.

Motor and Sensory Features of Cervical Dystonia Subtypes: Data From the Italian Dystonia Registry

Introduction: Cervical dystonia (CD) is one of the most common forms of adult-onset isolated dystonia. Recently, CD has been classified according to the site of onset and spread, in different clinical subgroups, that may represent different clinical entities or pathophysiologic subtypes. In order to support this hypothesis, in this study we have evaluated whether different subgroups of CD, that clinically differ for site of onset and spread, also imply different sensorimotor features.

Methods: Clinical and demographic data from 842 patients with CD from the Italian Dystonia Registry were examined. Motor features (head tremor and tremor elsewhere) and sensory features (sensory trick and neck pain) were investigated. We analyzed possible associations between motor and sensory features in CD subgroups [focal neck onset, no spread (FNO-NS); focal neck onset, segmental spread (FNO-SS); focal onset elsewhere with segmental spread to neck (FOE-SS); segmental neck involvement without spread (SNI)].

Results: In FNO-NS, FOE-SS, and SNI subgroups, head tremor was associated with the presence of tremor elsewhere. Sensory trick was associated with pain in patients with FNO-NS and with head tremor in patients with FNO-SS.

Conclusion: The frequent association between head tremor and tremor elsewhere may suggest a common pathophysiological mechanism. Two mechanisms may be hypothesized for sensory trick: a gating mechanism attempting to reduce pain and a sensorimotor mechanism attempting to control tremor.

Parkinsonian Symptoms, Not Dyskinesia, Negatively Affect Active Life Participation of Dyskinetic Patients with Parkinson's Disease

Background:

The impact of slight-to-moderate levodopa-induced dyskinesia (LID) on the level of participation in active life in patients with Parkinson’s disease (PD) has never been objectively determined.

Methods:

Levels of LID, tremor and bradykinesia were measured during best-ON state in 121 patients diagnosed with PD and having peak-dose LID using inertial sensors positioned on each body limb. Rigidity and postural instability were assessed using clinical evaluations. Cognition and depression were assessed using the MMSE and the GDS-15. Participation in active life was assessed in patients and in 69 healthy controls using the Activity Card Sort (ACS), which measures levels of activity engagement and activities affected by the symptomatology. Outcome measures were compared between patients and controls using ANCOVA, controlling for age or Wilcoxon-Mann-Whitney tests. Spearman correlations and multivariate analyses were then performed between symptomatology and ACS scores.

Results:

Patients had significantly lower activity engagement than controls and had significantly affected activities. LID was neither associated with activity engagement nor affected activities. Higher levels of tremor, postural instability, cognitive decline and depression were associated with lower activity engagement and higher affected activities. Multivariate analyses revealed that only tremor, postural instability and depression accounted significantly in the variances of these variables.

Discussion:

Slight-to-moderate LID had little impact compared to other symptoms on the level of participation in active life, suggesting that other symptoms should remain the treatment priority to maintain the level of participation of patients in an active lifestyle.

Quantitative measures of postural tremor at the upper limb joints in patients with essential tremor

BACKGROUND

It is important to quantitatively assess tremor for accurate diagnosis and evaluation of the response to interventions in patients with essential tremor (ET).

OBJECTIVE

The purpose of this study was to investigate the relationship between quantitative measures of postural tremor and clinical rating scale in patients with ET.

METHODS

18 ET patients performed a postural tremor task that required them to hold their arms outstretched parallel to the floor while wearing a gyro sensor based measurement system. The time domain variables were derived from the sensor signals. Additionally, the frequency domain variables were derived from the power spectrum of the angular velocity signal. Spearman correlation analysis was employed in the relationship between the variables and clinical score.

RESULTS

The RMS angular velocity of roll and yaw directions at the hand joint were strongly correlated with the clinical rating scale (r= 0.7, p< 0.01). Similarly, the peak power of roll and yaw directions at the hand joint were moderately correlated with the clinical rating scale (r= 0.61 and r= 0.67, p< 0.01). In contrast, no significant correlation coefficients were observed in the peak frequency (p> 0.05).

CONCLUSION

These results indicate that hand tremor of roll and yaw directions are more associated with assessment of severity of ET compared to other joints. This study suggests that quantitative measurements of postural tremor should be considered as tremor directionality as well as attachment location.

Tremor Assessment during Virtual Reality Brain Tumor Resection

OBJECTIVE

Assessment of physiological tremor during neurosurgical procedures may provide further insights into the composites of surgical expertise. Virtual reality platforms may provide a mechanism for the quantitative assessment of physiological tremor. In this study, a virtual reality simulator providing haptic feedback was used to study physiological tremor in a simulated tumor resection task with participants from a "skilled" group and a "novice" group.

DESIGN

The task involved using a virtual ultrasonic aspirator to remove a series of virtual brain tumors with different visual and tactile characteristics without causing injury to surrounding tissue. Power spectral density analysis was employed to quantitate hand tremor during tumor resection. Statistical t test was used to determine tremor differences between the skilled and novice groups obtained from the instrument tip x, y, z coordinates, the instrument roll, pitch, yaw angles, and the instrument haptic force applied during tumor resection.

SETTING

The study was conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

PARTICIPANTS

The skilled group comprised 23 neurosurgeons and senior residents and the novice group comprised 92 junior residents and medical students.

RESULTS

The spectral analysis allowed quantitation of physiological tremor during virtual reality tumor resection. The skilled group displayed smaller physiological tremor than the novice group in all cases. In 3 out of 7 cases the difference was statistically significant.

CONCLUSIONS

The first investigation of the application of a virtual reality platform is presented for the quantitation of physiological tremor during a virtual reality tumor resection task. The goal of introducing such methodology to assess tremor is to highlight its potential educational application in neurosurgical resident training and in helping to further define the psychomotor skill set of surgeons.

COMPARISON OF THEORIES OF REST TREMOR MECHANISM IN PARKINSON’S DISEASE: CENTRAL OSCILLATOR (SOURCE-TRIGGERED OSCILLATIONS) AND FEEDBACK-INDUCED INSTABILITY IN THE SENSORIMOTOR LOOP (SELF-SUSTAINED OSCILLATIONS)

Rest tremor is one of the most common and disabling symptoms of Parkinson’s disease (PD). The exact neural origin of rest tremor is still not clearly understood. Understanding the origin of rest tremor is important as it may aid in optimizing existing treatment strategies such as Deep Brain Stimulation or in developing new treatment strategies for rest tremor reduction. There are broadly two theories that are gaining prominence for rest tremor generation in PD. The first theory is the central oscillator theory that states that the rest tremor is triggered by an oscillatory source in the brain. The second theory is the feedback-induced instability theory that states that the rest tremor arises out of a feedback-induced instability in the sensorimotor loop. This paper analyzes validity of the two theories based on established clinical observations of Parkinsonian rest tremor by using representative simulation examples. Finally, based on our analysis, we propose two test-worthy experiments for further validation.

A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms - bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter- and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity out-of-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and machine learning algorithms. The sensor system is composed of a force-sensor, three inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson's Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and 10 healthy subjects were recruited as a comparison control group. Results prove the system accurately predicts UPDRS scores for all symptoms (85.4% match on average with physician assessment) and discriminates between healthy subjects and PD patients (96.6% on average). MMG features can also be used for remote monitoring of severity and fluctuations in PD symptoms out-of-clinic. This closed-loop feedback system enables individually tailored and regularly updated treatment, facilitating better outcomes for a very large patient population.

A Wearable System for Attenuating Essential Tremor Based on Peripheral Nerve Stimulation

OBJECTIVE

Currently available treatments for kinetic tremor can cause intolerable side effects or be highly invasive and expensive. Even though several studies have shown the positive effects of external feedback (i.e., electrical stimulation) for suppressing tremor, such approaches have not been fully integrated into wearable real-time feedback systems.

METHOD

We have developed a wireless wearable stimulation system that analyzes upper limb tremor using a three-axis accelerometer and that modulates/attenuates tremor using peripheral-nerve electrical stimulation with adjustable stimulation parameters and a real-time tremor detection algorithm. We outfitted nine subjects with tremor with a wearable system and a set of surface electrodes placed on the skin overlying the radial nerve and tested the effects of stimulation with nine combinations of parameters for open- and closed-loop stimulation on tremor. To quantify the effects of the stimulation, we measured tremor movements, and analyzed the dominant tremor frequency and tremor power.

RESULTS

Baseline tremor power gradually decreased over the course of 18 stimulation trials. During the last trial, compared with the control trial, the reduction rate of tremor power was 42.17 ± 3.09%. The dominant tremor frequency could be modulated more efficiently by phase-locked closed-loop stimulation. The tremor power was equally reduced by open- and closed-loop stimulation.

CONCLUSION

Peripheral nerve stimulation significantly affects tremor, and stimulation parameters need to be optimized to modulate tremor metrics. Clinical Impact: This preliminary study lays the foundation for future studies that will evaluate the efficacy of the proposed closed-loop peripheral nerve stimulation method in a larger group of patients with kinetic tremor.

Effects of acute cannabidiol administration on anxiety and tremors induced by a Simulated Public Speaking Test in patients with Parkinson's disease

BACKGROUND

Cannabidiol (CBD) is one of the main components of Cannabis sativa and has anxiolytic properties, but no study has been conducted to evaluate the effects of CBD on anxiety signs and symptoms in patients with Parkinson's disease (PD). This study aimed to evaluate the impacts of acute CBD administration at a dose of 300 mg on anxiety measures and tremors induced by a Simulated Public Speaking Test (SPST) in individuals with PD.

METHODS

A randomised, double-blinded, placebo-controlled, crossover clinical trial was conducted. A total of 24 individuals with PD were included and underwent two experimental sessions within a 15-day interval. After taking CBD or a placebo, participants underwent the SPST. During the test, the following data were collected: heart rate, systemic blood pressure and tremor frequency and amplitude. In addition, the Visual Analog Mood Scales (VAMS) and Self-Statements during Public Speaking Scale were applied. Statistical analysis was performed by repeated-measures analysis of variance (ANOVA) while considering the drug, SPST phase and interactions between these variables.

RESULTS

There were statistically significant differences in the VAMS anxiety factor for the drug; CBD attenuated the anxiety experimentally induced by the SPST. Repeated-measures ANOVA showed significant differences in the drug for the variable related to tremor amplitude as recorded by the accelerometer.

CONCLUSION

Acute CBD administration at a dose of 300 mg decreased anxiety in patients with PD, and there was also decreased tremor amplitude in an anxiogenic situation.

The Rehapiano-Detecting, Measuring, and Analyzing Action Tremor Using Strain Gauges

We have developed a device, the Rehapiano, for the fast and quantitative assessment of action tremor. It uses strain gauges to measure force exerted by individual fingers. This article verifies the device's capability to measure and monitor the development of upper limb tremor. The Rehapiano uses a precision, 24-bit, analog-to-digital converter and an Arduino microcomputer to transfer raw data via a USB interface to a computer for processing, database storage, and evaluation. First, our experiments validated the device by measuring simulated tremors with known frequencies. Second, we created a measurement protocol, which we used to measure and compare healthy patients and patients with Parkinson's disease. Finally, we evaluated the repeatability of a quantitative assessment. We verified our hypothesis that the Rehapiano is able to detect force changes, and our experimental results confirmed that our system is capable of measuring action tremor. The Rehapiano is also sensitive enough to enable the quantification of Parkinsonian tremors.

Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson's Disease

BACKGROUND

Technology-based objective measures (TOMs) recently gained relevance to support clinicians in the assessment of motor function in Parkinson's disease (PD), although limited data are available in the early phases.

OBJECTIVE

To assess motor performances of a population of newly diagnosed, drug free PD patients using wearable inertial sensors and to compare them to healthy controls (HC) and differentiate different PD subtypes [tremor dominant (TD), postural instability gait disability (PIGD), and mixed phenotype (MP)].

METHODS

We enrolled 65 subjects, 36 newly diagnosed, drug-free PD patients and 29 HCs. PD patients were clinically defined as tremor dominant, postural instability-gait difficulties or mixed phenotype. All 65 subjects performed seven MDS-UPDRS III motor tasks wearing inertial sensors: rest tremor, postural tremor, rapid alternating hand movement, foot tapping, heel-to-toe tapping, Timed-Up-and-Go test (TUG) and pull test. The most relevant motor tasks were found combining ReliefF ranking and Kruskal- Wallis feature-selection methods. We used these features, linked to the relevant motor tasks, to highlight differences between PD from HC, by means of Support Vector Machine (SVM) classifier. Furthermore, we adopted SVM to support the relevance of each motor task on the classification accuracy, excluding one task at time.

RESULTS

Motion analysis distinguished PD from HC with an accuracy as high as 97%, based on SVM performed with measured features from tremor and bradykinesia items, pull test and TUG. Heel-to-toe test was the most relevant, followed by TUG and Pull Test.

CONCLUSIONS

In this pilot study, we demonstrate that the SVM algorithm successfully distinguishes de novo drug-free PD patients from HC. Surprisingly, pull test and TUG tests provided relevant features for obtaining high SVM classification accuracy, differing from the report of the experienced examiner. The use of TOMs may improve diagnostic accuracy for these patients.

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning

Parkinson's Disease (PD) is a slowly evolving neurological disease that affects about [Formula: see text] of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings.

Wearable Electronics Assess the Effectiveness of Transcranial Direct Current Stimulation on Balance and Gait in Parkinson's Disease Patients

Currently, clinical evaluation represents the primary outcome measure in Parkinson's disease (PD). However, clinical evaluation may underscore some subtle motor impairments, hidden from the visual inspection of examiners. Technology-based objective measures are more frequently utilized to assess motor performance and objectively measure motor dysfunction. Gait and balance impairments, frequent complications in later disease stages, are poorly responsive to classic dopamine-replacement therapy. Although recent findings suggest that transcranial direct current stimulation (tDCS) can have a role in improving motor skills, there is scarce evidence for this, especially considering the difficulty to objectively assess motor function. Therefore, we used wearable electronics to measure motor abilities, and further evaluated the gait and balance features of 10 PD patients, before and (three days and one month) after the tDCS. To assess patients' abilities, we adopted six motor tasks, obtaining 72 meaningful motor features. According to the obtained results, wearable electronics demonstrated to be a valuable tool to measure the treatment response. Meanwhile the improvements from tDCS on gait and balance abilities of PD patients demonstrated to be generally partial and selective.

Quantitative assessment of Parkinsonian tremor by using biosensor device

Parkinson disease (PD) is the second most common neurodegenerative disease which affects population older than 65 years. Tremor represents one of the main symptomatic triads in PD, particularly in rest state.We enrolled 41 idiopathic PD patients, to validate the assessment of tremor symptoms.To be enrolled in the study, patients had to fulfill the movement disorder society clinical diagnostic criteria for PD.We used an innovative home-made, low-cost device, able to quantify the frequency and amplitude of rest tremor and stress conditionOur results confirmed the presence of tremor during muscular effort in a significant number of patients and the influence of emotional stress.We suppose that this new device should be validated in clinical practice as a support of differential diagnosis and therapeutic management of PD patients.

Quantitative assessment of lower limbs gross motor function in children with cerebral palsy based on surface EMG and inertial sensors

Taking advantage of motion sensing technology, a quantitative assessment method for lower limbs motor function of cerebral palsy (CP) based on the gross motor function measurement (GMFM)-24 scale was explored in this study. According to the motion analysis on GMFM-24 scale, we translated the assessment problem of GMFM-24 scale into a detection problem of different motion modes including static state, fall, step, turning, alternating gait, walking, running, lifting legs, kicking balls, and jumping. The surface electromyography (sEMG) electrodes and inertial sensors were adopted to capture motion data, and a framework integrating a series of detection algorithms was presented for the assessment of lower limbs gross motor function. Two groups of participants including 8 healthy adults and 14 CP children were recruited. A self-developed data acquisition equipment integrating 24 sEMG electrodes and 9 inertial units was adopted for data acquisition. A platform based on two laser beam sensors was used to perform cross-border detection. The parameters/thresholds of motion detection algorithms were determined by the data from healthy adults, and the lower limbs gross motor function evaluation was conducted on 14 CP children. The experimental results verified the feasibility and effectiveness of the proposed quantitative assessment method. Compared to the clinical assessment score based on GMFM-24 scale, 90.1% accuracy was obtained for evaluation of 303 tasks in 14 CP children. The objective motor function assessment method proposed has potential application value for the quantitative assessment of lower limbs motor function of CP children in clinical practice. Graphical abstract The algorithm framework for the assessment of lower limbs gross motor function. Using the GMFM-24 scale as the evaluation standard, a quantitative evaluation program for the lower limbs gross motor function of CP children based on motion sensing technology was proposed.

Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson's Disease Screening

Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders. In this study, spirals and straight lines in polar coordinates are used to extract polar expression features such as the key parameters deviation (cm) and accumulation angle (rad). These parameters are quantitative manner to scale the variations of functional tremors in normal control subjects and patients with Parkinson's disease (PD) and essential tremor (ET). However, difficulty arises in using nonlinear polar expression features in the two-dimensional feature space to separate normal control subjects from those with PD and ET. To solve the nonlinear separable classification problem, hash transformation is used to map polar expression features to a high-dimensional space using hash weighing function and modulo operation. Then, a machine learning method, such as the generalized regression neural network (GRNN), is implemented to train a decision-making classifier using the particle swarm optimization (PSO) algorithm for possible class assessment. With the enrolled data from 50 subjects, the fivefold cross validation, mean true positive, mean true negative, and mean hit rates of 98.93%, 98.96%, and 98.93%, respectively, are obtained to quantify the performance of the proposed decision-making classifier to identify normal controls and subjects with PD or ET. The experimental results indicate that the proposed screening model can improve the accuracy rate compared with the conventional machine learning classifier.

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.

Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements

Tremor is one of the main symptoms of Parkinson's Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients' tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients' tremor from continuous monitoring of the subjects' movement in their natural environment.

Age Matters: Objective Gait Assessment in Early Parkinson's Disease Using an RGB-D Camera

Background

Gait alterations are hallmarks for the diagnosis and follow-up of patients with Parkinson's disease (PD). In normal conditions, age could affect gait dynamics. Although it is known that objective assessment of gait is a valuable tool for diagnosis and follow-up of patients with PD, only few studies evaluate the effect of aging on the gait pattern of patients with PD.

Objective

The purpose of this study was to assess differences in gait dynamics between PD patients and healthy subjects and to investigate the effects of aging on these differences using a low-cost RGB-D depth-sensing camera.

Methods

30 PD patients and 30 age-matched controls were recruited. Descriptive analysis was used for clinical variables, and Spearman's rank correlation was used to correlate age and gait variables. The sample was distributed in age groups; then, Mann–Whitney U test was used for comparison of gait variables between groups.

Results

PD patients exhibited prolonged swing (p=0.002) and stance times (p < 0.001) and lower speed values (p < 0.001) compared to controls. This was consistent in all age groups, except for the one between 76 and 88 years old, in which the controls were slower and had longer swing and stance times. These results were statically significant for the group from 60 to 66 years.

Conclusion

Gait speed, swing, and stance times are useful for differentiating PD patients from controls. Quantitative gait parameters measured by an RGB-D camera can complement clinical assessment of PD patients. The analysis of these spatiotemporal variables should consider the age of the subject.

Kinematic Metrics from a Wireless Stylus Quantify Tremor and Bradykinesia in Parkinson's Disease

A fundamental challenge in the clinical care of Parkinson disease (PD) is the current dependence on subjective evaluations of tremor and bradykinesia. New technologies offer the ability to evaluate motor deficits using purely objective measures. The aim of this study was to develop and evaluate the efficacy of a wireless stylus (Cleveland Clinic Stylus) with an embedded motion sensor to quantitatively assess tremor and bradykinesia in patients with PD with subthalamic nucleus (STN) deep brain stimulation (DBS). Twenty-one subjects were tested in various on and off DBS conditions while holding the Cleveland Clinic Stylus while at rest, maintaining a postural hold, and during a movement task. Kinematic metrics were calculated from the motion sensor data, including 3D angular velocity and 3D acceleration data, and were compared between the on and off conditions. Generalized estimating equations (GEEs) were used to determine the relationship between kinematic metrics and MDS-Unified Parkinson's Disease Rating Scale Motor III (UPDRS-III) subscores. Kinematic metrics from the rest and postural tasks were significantly related to the UPDRS-III subscores of tremor (p < 0.001 for all metrics), and kinematic metrics from the movement task were significantly related to the UPDRS-III subscore of bradykinesia (p < 0.001 for 3/7 metrics). Kinematic metrics (7/9) showed a significant effect of stimulation setting (range: p < 0.03– < 0.0001) across the three tasks, indicating significant improvements from DBS in these measures. The Cleveland Clinic Stylus provided quantitative kinematic measures of tremor and bradykinesia severity and detected significant improvements in these measures from medication and DBS therapy. This low-cost, easy-to-use tool can provide objective measures that will improve clinical care of PD patients by providing a more reliable and objective evaluation of movement symptoms, disease progression, and effects of therapy in and outside the clinical setting.