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

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.

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.

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.

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.

Diagnostic Uncertainties in Tremor

The approach and diagnosis of patients with tremor may be challenging for clinicians. According to the most recent consensus statement by the Task Force on Tremor of the International Parkinson Movement Disorder Society, the differentiation between action (i.e., kinetic, postural, intention), resting, and other task- and position-specific tremors is crucial to this goal. In addition, patients with tremor must be carefully examined for other relevant features, including the topography of the tremor, since it can involve different body areas and possibly associate with neurological signs of uncertain significance. Following the characterization of major clinical features, it may be useful to define, whenever possible, a particular tremor syndrome and to narrow down the spectrum of possible etiologies. First, it is important to distinguish between physiological and pathological tremor, and, in the latter case, to differentiate between the underlying pathological conditions. A correct approach to tremor is particularly relevant for appropriate referral, counseling, prognosis definition, and therapeutic management of patients. The purpose of this review is to outline the possible diagnostic uncertainties that may be encountered in clinical practice in the approach to patients with tremor. In addition to an emphasis on a clinical approach, this review discusses the important ancillary role of neurophysiology and innovative technologies, neuroimaging, and genetics in the diagnostic process.

Improving puncture accuracy in percutaneous CT-guided needle insertion with wireless inertial measurement unit: a phantom study

OBJECTIVES

A novel method applying inertial measurement units (IMUs) was developed to assist CT-guided puncture, which enables real-time displays of planned and actual needle trajectories. The method was compared with freehand and laser protractor-assisted methods.

METHODS

The phantom study was performed by three operators with 8, 2, and 0 years of experience in CT-guided procedure conducted five consecutive needle placements for three target groups using three methods (freehand, laser protractor-assisted, or IMU-assisted method). The endpoints included mediolateral angle error and caudocranial angle error of the first pass, the procedure time, the total number of needle passes, and the radiation dose.

RESULTS

There was a significant difference in the number of needle passes (IMU 1.2 ± 0.42, laser protractor 2.9 ± 1.6, freehand 3.6 ± 2.0 time, p < 0.001), the procedure time (IMU 3.0 ± 1.2, laser protractor 6.4 ± 2.9, freehand 6.2 ± 3.1 min, p < 0.001), the mediolateral angle error of the first pass (IMU 1.4 ± 1.2, laser protractor 1.6 ± 1.3, freehand 3.7 ± 2.5 degree, p < 0.001), the caudocranial angle error of the first pass (IMU 1.2 ± 1.2, laser protractor 5.3 ± 4.7, freehand 3.9 ± 3.1 degree, p < 0.001), and the radiation dose (IMU 250.5 ± 74.1, laser protractor 484.6 ± 260.2, freehand 561.4 ± 339.8 mGy-cm, p < 0.001) among three CT-guided needle insertion methods.

CONCLUSION

The wireless IMU improves the angle accuracy and speed of CT-guided needle punctures as compared with laser protractor guidance and freehand techniques.

KEY POINTS

• The IMU-assisted method showed a significant decrease in the number of needle passes (IMU 1.2 ± 0.42, laser protractor 2.9 ± 1.6, freehand 3.6 ± 2.0 time, p < 0.001). • The IMU-assisted method showed a significant decrease in the procedure time (IMU 3.0 ± 1.2, laser protractor 6.4 ± 2.9, freehand 6.2 ± 3.1 min, p < 0.001). • The IMU-assisted method showed a significant decrease in the mediolateral angle error of the first pass and the caudocranial angle error of the first pass.

Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review

Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.

A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.