Automatic Nighttime Agitation and Sleep Disruption Detection using Wearable Ankle Device and Machine Learning
Links to Files
Permanent Link
Author/Creator
Author/Creator ORCID
Date
Type of Work
Department
Computer Science and Electrical Engineering
Program
Computer Science
Citation of Original Publication
Rights
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Subjects
Abstract
Nighttime agitation behavior, such as wandering and restlessness during awake and sleep, in people with Alzheimer'sdisease (AD) is expensive to manage and adversely affects sleep. Nighttime agitation is mostly noted by subjective caregiver reports. An automated process for this assessment would improve clinical management. Here, we report on the RestEaZe system that uses an ankle band having a 3-axis accelerometer, a 3-axis gyroscope, and three textile capacitive sensors, along with the application of developed machine learning algorithm to automatically classify sleep status and nighttime agitation behaviours in older adults with AD. We created three binary classifiers- ?IsAwake?, ?IsWandering?, ?IsRestless?, and implemented our model in three phases pre-processing of data, creation of machine learning model and evaluation matrices. Finally, we evaluated our model over various train-test split with 5-fold CV.