Quantitative assessment of arm tremor in people with neurological disorders

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.

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.

Reliability of a Novel Video-Based Method for Assessing Age-Related Changes in Upper Limb Kinematics

Monitoring age-related changes in motor function can be used to identify deviations that represent underlying diseases for which early diagnosis is often paramount for efficacious, interventional therapies. Currently, the availability of cost-effective and reliable diagnostic tools capable of routine monitoring is limited. Adequate diagnostic systems are needed to identify, monitor and distinguish early subclinical symptoms of neurological diseases from normal aging-associated changes. Herein, we describe the development, initial validation and reliability of the Hand-Arm Movement Monitoring System (HAMMS), a video-based data acquisition system built using a programmable, versatile platform for acquiring temporal and spatial metrics of hand and arm movements. A healthy aging population of 111 adults were used to evaluate the HAMMS via a repetitive motion test of changing target size. The test required participants to move a fiducial on their hand between two targets presented on a video monitor. The test-retest reliability based on Intraclass Correlation Coefficient (ICCs) for the system ranged from 0.56 to 0.87 and the Linear Correlation Coefficients (LCCs) ranged from 0.58 to 0.87. Average speed, average acceleration, speed error and center offset all demonstrated a positive correlation with age. Using an intertarget path of hand motion, we observed an age-dependent increase in the average number of points outside the most direct motion path, indicating a reduction in hand-arm movement control with age. The reliability, flexibility and programmability of the HAMMS makes this low cost, video-based platform an effective tool for evaluating longitudinal changes in hand-arm related movements and a potential diagnostic device for neurological diseases where hand-arm movements are affected.