A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement

Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and identify age-related changes in wrist kinematics and response latency. Eighteen young (ages 18-20) and nine older (ages 49-57) adults performed two standard tasks with wearable inertial measurement units on their wrists. Frequency analysis revealed 5 kinematic variables distinguishing older from younger adults in a postural task, with best discrimination occurring in the 9-13 Hz range, agreeing with previously identified frequency range of age-related tremors, and achieving excellent classifier performance (0.86 AUROC score and 89% accuracy). In a second pronation-supination task, analysis of angular velocity in the roll axis identified a 71 ms delay in initiating arm movement in the older adults. This study demonstrates that an analysis of simple kinematic variables sampled at 100 Hz frequency with commercially available sensors is reliable, sensitive, and accurate at detecting age-related increases in physiological tremor and motor slowing. It remains to be seen if such sensitive methods may be accurate in distinguishing physiological tremors from tremors that occur in neurological diseases, such as Parkinson's Disease.

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor

BACKGROUND

Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems.

METHOD

We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method.

RESULTS

Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%.

CONCLUSION

Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

Wearable sensors during drawing tasks to measure the severity of essential tremor

Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant's dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn-Tolosa-Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4-12 Hz to 0.5-4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r² = 0.561) between FTM and a combination of power spectral density ratio and task time. However, the system could not accurately differentiate ET phenotypes according to the Consensus classification scheme. Potential applications of machine-based assessment of ET using wearable sensors include clinical trials and remote monitoring of patients.

Feasibility of a New Desktop Motion Analysis System with a Video Game Console for Assessing Various Three-Dimensional Wrist Motions

Background

The restriction of wrist motion results in limited hand function, and the evaluation of the range of wrist motion is related to the evaluation of wrist function. To analyze and compare the wrist motion during four selected tasks, we developed a new desktop motion analysis system using the motion controller for a home video game console.

Methods

Eighteen healthy, right-handed subjects performed 15 trials of selective tasks (dart throwing, hammering, circumduction, and winding thread on a reel) with both wrists. The signals of light-emitting diode markers attached to the hand and forearm were detected by the optic receptor in the motion controller. We compared the results between both wrists and between motions with similar motion paths.

Results

The parameters (range of motion, offset, coupling, and orientations of the oblique plane) for wrist motion were not significantly different between both wrists, except for radioulnar deviation for hammering and the orientation for thread winding. In each wrist, the ranges for hammering were larger than those for dart throwing. The offsets and the orientations of the oblique plane were not significantly different between circumduction and thread winding.

Conclusions

The results for the parameters of dart throwing, hammering, and circumduction of our motion analysis system using the motion controller were considerably similar to those of the previous studies with three-dimensional reconstruction with computed tomography, electrogoniometer, and motion capture system. Therefore, our system may be a cost-effective and simple method for wrist motion analysis.

Activity-aware essential tremor evaluation using deep learning method based on acceleration data

BACKGROUND

Essential tremor (ET), one of the most common neurological disorders is typically evaluated with validated rating scales which only provide a subjective assessment during a clinical visit, underestimating the fluctuations tremor during different daily activities. Motion sensors have shown favorable performances in both quantifying tremor and voluntary human activity recognition (HAR).

OBJECTIVE

To create an automated system of a reference scale using motion sensors supported by deep learning algorithms to accurately rate ET severity during voluntary activities, and to propose an IOTA based blockchain application to share anonymously tremor data.

METHOD

A smartwatch-based tremor monitoring system was used to collect motion data from 20 subjects while they were doing standard tasks. Two neurologists rated ET by Fahn-Tolosa Marin Tremor Rating Scale (FTMTRS). Supported by deep learning techniques, activity classification models (ACMs) and tremor evaluation models (TEMs) were created and algorithms were implemented, to distinguish voluntary human activities and evaluate tremor severity respectively.

RESULT

A practical application example showed that the proposed ACMs can classify six typical activities with high accuracy (89.73%-98.84%) and the results produced by the TEMs are significantly correlated with the FTMTRS ratings of two neurologists (r₁ = 0.92, p₁ = 0.008; r₂ = 0.93, p₂ = 0.007).

CONCLUSION

This study demonstrated that motion sensor data, supported by deep learning algorithms, can be used to classify human activities and evaluate essential tremor severity during different activities.

Continuous Monitoring of Essential Tremor Using a Portable System Based on Smartwatch

INTRODUCTION

Essential tremor (ET) shows amplitude fluctuations throughout the day, presenting challenges in both clinical and treatment monitoring. Tremor severity is currently evaluated by validated rating scales, which only provide a timely and subjective assessment during a clinical visit. Motor sensors have shown favorable performances in quantifying tremor objectively.

METHODS

A new highly portable system was used to monitor tremor continuously during daily lives. It consists of a smartwatch with a triaxial accelerometer, a smartphone, and a remote server. An experiment was conducted involving eight ET patients. The average effective data collection time per patient was 26 (±6.05) hours. Fahn-Tolosa-Marin Tremor Rating Scale (FTMTRS) was adopted as the gold standard to classify tremor and to validate the performance of the system. Quantitative analysis of tremor severity on different time scales is validated.

RESULTS

Significant correlations were observed between neurologist's FTMTRS and patient's FTMTRS auto-assessment scores (r = 0.84; p = 0.009), between the device quantitative measures and the scores from the standardized assessments of neurologists (r = 0.80; p = 0.005) and patient's auto-evaluation (r = 0.97; p = 0.032), and between patient's FTMTRS auto-assessment scores day-to-day (r = 0.87; p < 0.001). A graphical representation of four patients with different degrees of tremor was presented, and a representative system is proposed to summarize the tremor scoring at different time scales.

CONCLUSION

This study demonstrates the feasibility of prolonged and continuous monitoring of tremor severity during daily activities by a highly portable non-restrictive system, a useful tool to analyze efficacy and effectiveness of treatment.

Quantitative wearable sensors for objective assessment of Parkinson's disease

There is a rapidly growing interest in the quantitative assessment of Parkinson's disease (PD)-associated signs and disability using wearable technology. Both persons with PD and their clinicians see advantages in such developments. Specifically, quantitative assessments using wearable technology may allow for continuous, unobtrusive, objective, and ecologically valid data collection. Also, this approach may improve patient-doctor interaction, influence therapeutic decisions, and ultimately ameliorate patients' global health status. In addition, such measures have the potential to be used as outcome parameters in clinical trials, allowing for frequent assessments; eg, in the home setting. This review discusses promising wearable technology, addresses which parameters should be prioritized in such assessment strategies, and reports about studies that have already investigated daily life issues in PD using this new technology.

Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology

The objective was to capture levodopa-induced dyskinesia (LID) in patients with Parkinson's disease (PD) using body-worn motion sensors. Dopaminergic treatment in PD can induce abnormal involuntary movements, including choreatic dyskinesia (brief, rapid, irregular movements). Adjustments in medication to reduce LID often sacrifice control of motor symptoms, and balancing this tradeoff poses a significant challenge for management of advanced PD. Fifteen PD subjects with known LID were recruited and instructed to perform two stationary motor tasks while wearing a compact wireless motion sensor unit positioned on each hand over the course of a levodopa dose cycle. Videos of subjects performing the motor tasks were later scored by expert clinicians to assess global dyskinesia using the modified Abnormal Involuntary Rating Scale (m-AIMS). Kinematic features were extracted from motion data in different frequency bands (1-3Hz and 3-8Hz) to quantify LID severity and to distinguish between LID and PD tremor. Receiver operator characteristic analysis was used to determine thresholds for individual features to detect the presence of LID. A sensitivity of 0.73 and specificity of 1.00 were achieved. A neural network was also trained to output dyskinesia severity on a 0 to 4 scale, similar to the m-AIMS. The model generalized well to new data (coefficient of determination= 0.85 and mean squared error= 0.3). This study demonstrated that hand-worn motion sensors can be used to assess global dyskinesia severity independent of PD tremor over the levodopa dose cycle.

Distance estimation from acceleration for quantitative evaluation of Parkinson tremor

The purpose of this paper is to assess Parkinson tremor estimating actual distance amplitude. We propose a practical, useful and simple method for evaluating Parkinson tremor with distance value. We measured resting tremor of 7 Parkinson Disease (PD) patients with triaxial accelerometer. Resting tremor of participants was diagnosed by Unified Parkinson's Disease Rating Scale (UPDRS) by neurologist. First, we segmented acceleration signal during 7 seconds from recorded data. To estimate a displacement of tremor, we performed double integration from the acceleration. Prior to double integration, moving average method was used to reduce an error of integral constant. After estimation of displacement, we calculated tremor distance during 1s from segmented signal using Euclidean distance. We evaluated the distance values compared with UPDRS. Averaged moving distance during 1 second corresponding to UPDRS 1 was 11.52 mm, that of UPDRS 2 was 33.58 mm and tremor distance of UPDRS 3 was 382.22 mm. Estimated moving distance during 1s was proportional to clinical rating scale--UPDRS.

Assessment of tremor activity in the Parkinson's disease using a set of wearable sensors

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.

A low cost fMRI-compatible tracking system using the Nintendo Wii remote

It is sometimes necessary during functional magnetic resonance imaging (fMRI) experiments to capture different movements made by the subjects, e.g. to enable them to control an item or to analyze its kinematics. The aim of this work is to present an inexpensive hand tracking system suitable for use in a high field MRI environment. It works by introducing only one light-emitting diode (LED) in the magnet room, and by receiving its signal with a Nintendo Wii remote (the primary controller for the Nintendo Wii console) placed outside in the control room. Thus, it is possible to take high spatial and temporal resolution registers of a moving point that, in this case, is held by the hand. We tested it using a ball and racket virtual game inside a 3 Tesla MRI scanner to demonstrate the usefulness of the system. The results show the involvement of a number of areas (mainly occipital and frontal, but also parietal and temporal) when subjects are trying to stop an object that is approaching from a first person perspective, matching previous studies performed with related visuomotor tasks. The system presented here is easy to implement, easy to operate and does not produce important head movements or artifacts in the acquired images. Given its low cost and ready availability, the method described here is ideal for use in basic and clinical fMRI research to track one or more moving points that can correspond to limbs, fingers or any other object whose position needs to be known.

Biomarker candidates of neurodegeneration in Parkinson's disease for the evaluation of disease-modifying therapeutics

Reliable biomarkers that can be used for early diagnosis and tracking disease progression are the cornerstone of the development of disease-modifying treatments for Parkinson’s disease (PD). The German Society of Experimental and Clinical Neurotherapeutics (GESENT) has convened a Working Group to review the current status of proposed biomarkers of neurodegeneration according to the following criteria and to develop a consensus statement on biomarker candidates for evaluation of disease-modifying therapeutics in PD. The criteria proposed are that the biomarker should be linked to fundamental features of PD neuropathology and mechanisms underlying neurodegeneration in PD, should be correlated to disease progression assessed by clinical rating scales, should monitor the actual disease status, should be pre-clinically validated, and confirmed by at least two independent studies conducted by qualified investigators with the results published in peer-reviewed journals. To date, available data have not yet revealed one reliable biomarker to detect early neurodegeneration in PD and to detect and monitor effects of drug candidates on the disease process, but some promising biomarker candidates, such as antibodies against neuromelanin, pathological forms of α-synuclein, DJ-1, and patterns of gene expression, metabolomic and protein profiling exist. Almost all of the biomarker candidates were not investigated in relation to effects of treatment, validated in experimental models of PD and confirmed in independent studies.

An EMG-based system for continuous monitoring of clinical efficacy of Parkinson's disease treatments

Current methods for assessing the efficacy of treatments for Parkinson's disease (PD) rely on physician rated scores. These methods pose three major shortcomings: 1) the subjectivity of the assessments, 2) the lack of precision on the rating scale (6 discrete levels), and 3) the inability to assess symptoms except under very specific conditions and/or for very specific tasks. To address these shortcomings, a portable system was developed to continuously monitor Parkinsonian symptoms with quantitative measures based on electrical signals from muscle activity (EMG). Here, we present the system design and the implementation of methods for system validation. This system was designed to provide continuous measures of tremor, rigidity, and bradykinesia which are related to the neurophysiological source without the need for multiple bulky experimental apparatuses, thus allowing more precise, quantitative indicators of the symptoms which can be measured during practical daily living tasks. This measurement system has the potential to improve the diagnosis of PD as well as the evaluation of PD treatments, which is an important step in the path to improving PD treatments.

WiiPD--an approach for the objective home assessment of Parkinson's disease

This paper introduces WiiPD, an approach to home-based objective assessment of Parkinson's disease. WiiPD aims to make use of the many capabilities of the Nintendo Wii Remote in combination with a number of bespoke data gathering methods to provide a rich and engaging user experience that can capture a wide range of motor and non-motor metrics. In this paper we discuss the architecture of the approach, and provide details of the implementation and testing of the motor-assessment component of the system. Initial results of testing on 6 users indicate that the system is able to differentiate between normal and abnormal motor performance, suggesting that the system has the potential to monitor the motor fluctuations associated with Parkinson's disease.