Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches

Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches

Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts,...

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Bibliographic Details
Main Authors: Karin C. Knudson, Anoopum S. Gupta
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Sensors
Subjects:
ataxia
wearable sensors
IMUs
Bayesian nonparametrics
time-frequency analysis
hidden Markov models
Online Access:https://www.mdpi.com/1424-8220/22/23/9454

Internet

https://www.mdpi.com/1424-8220/22/23/9454

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