Sponsor
This work was supported by the NINDS Intramural Program at the National Institutes of Health.
Published In
IEEE Journal of Translational Engineering in Health and Medicine
Document Type
Article
Publication Date
10-2020
Subjects
Tremor -- Assessment, Movement disorders, Wearable technology, Biomedical engineering, Detectors -- Technological innovations
Abstract
Background Assessment of essential tremor is often done by a trained clinician who observes the limbs during different postures and actions and subsequently rates the tremor. While this method has been shown to be reliable, the inter- and intra-rater reliability and need for training can make the use of this method for symptom progression difficult. Many limitations of clinical rating scales can potentially be overcome by using inertial sensors, but to date many algorithms designed to quantify tremor have key limitations.
Methods We propose a novel algorithm to characterize tremor using inertial sensors. It uses a two-stage approach that 1) estimates the tremor frequency of a subject and only quantifies tremor near that range; 2) estimates the tremor amplitude as the portion of signal power above baseline activity during recording, allowing tremor estimation even in the presence of other activity; and 3) estimates tremor amplitude in physical units of translation (cm) and rotation (°), consistent with current tremor rating scales. We validated the algorithm technically using a robotic arm and clinically by comparing algorithm output with data reported by a trained clinician administering a tremor rating scale to a cohort of essential tremor patients.
Results Technical validation demonstrated rotational amplitude accuracy better than ±0.2 degrees and position amplitude accuracy better than ±0.1 cm. Clinical validation revealed that both rotation and position components were significantly correlated with tremor rating scale scores.
Conclusion We demonstrate that our algorithm can quantify tremor accurately even in the presence of other activities, perhaps providing a step forward for at-home monitoring.
Rights
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
DOI
10.1109/JTEHM.2020.3032924
Persistent Identifier
https://archives.pdx.edu/ds/psu/34342
Citation Details
McGurrin, P., McNames, J., Wu, T., Hallett, M., & Haubenberger, D. (2020). Quantifying Tremor in Essential Tremor using Inertial Sensors—Validation of an Algorithm. IEEE Journal of Translational Engineering in Health and Medicine
Description
Originally appeared in IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, 2021. May be accessed at https://doi.org/10.1109/JTEHM.2020.3032924.