Anomaly Detection in Activities of Daily Living with Linear Drift

Abstract

Background: Anomaly detection in Activities of Daily Living (ADL) plays an important role in e-health applications. An abrupt change in ADL taken by a subject might indicate that she/he needs some help. Another important issue related with e-health application is the case where the change in ADL experiments a linear drift, this is the case in cognitive decline, Alzheimer disease or dementia. Methods: This work presents a novel method for detecting a linear drift in ADL modeled as circular normal distributions. The method is based on techniques commonly used in Statistical Process Control, and is able to detect and estimate the change point in time when a linear drift started.

Integrating Metamaterial Antenna Node and LiFi for Privacy Preserving Intelligent COVID-19 Hospital Patient Management

“…The 3D visual imaging using a microring embedded circuit and machine learning can be implemented in the robot brain [13,39,41,45], which can be applied in the surveillance data. The daily detection of e-health within the hospital area can be monitored and useful for decision making [13,50]. The big data network can support the required applications [46].…”

Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition

“…Moreover, some works focus on metrics to evaluate the deviation of behavior [ 29 , 30 , 31 ]. Therefore, these works mainly focus on temporal criteria, and only focus on these for the deviation.…”