Loading...
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 1.87 MB | Adobe PDF |
Advisor(s)
Abstract(s)
The world population is aging at a rapid pace. According to the WHO
(World Health Organization), from 2015 to 2050, the proportion of elderly people
AQ1 will practically double, from 12 to 22%, representing 2.1 billion people. From the
individual’s point of view, aging brings a series of challenges, mainly related to
AQ2 health conditions. Although, seniors can experience opposing health profiles. With
advancing age, cognitive functions tend to degrade, and conditions that affect the
physical and mental health of the elderly are disabilities or deficiencies that affect
Activities of Daily Living (ADL). The difficulty of carrying out these activities
within the domestic context prevents the individual from living independently
in their home. Abnormal behaviors in these activities may represent a decline
in health status and the need for intervention by family members or caregivers.
This work proposes the identification of anomalies in the ADL of the elderly in
the domestic context through Machine Learning algorithms using the Novelty
Detection method. The focus is on using available ADL data to create a baseline
of behavior and using new data to classify them as normal or abnormal daily. The
results obtained using the E-Health Monitoring database, using different Novelty
Detection algorithms, have an accuracy of 91% and an F1-Score of 90%.
Description
Keywords
Novelty Detection Anomaly Detection Activities of Daily Living Machine Learning One-Class Support Vector Machine (OC-SVM) Local Outlier Factor (LOF)
Citation
Publisher
Springer Nature Switzerland