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Before discussing these measurement requirements and challenges, I present a brief overview of a monitoring system for
the elderly.

Overview of Elderly Monitoring
Systems
Fig. 1 provides an overall architecture of a monitoring system for older people. The system consists of four functional
layers as follows: Environment Layer, Interconnection Layer,
Activity Recognition Layer, and Decision-Making Layer. The
environment layer comprises smart devices (e.g., cameras,
smart appliances, RFIDs, etc.), and wearable physiological
sensors. Data from smart devices allow caregivers to track
daily activities such as cooking, sleeping time, bathing,
entertaining, etc. Wearable devices, on the other hand, are responsible for collecting health measurement information such
as heart rate, blood pressure, glucose level, oxygen saturation
level, etc. The interconnection layer implements the concept of
IoT, which consists of standard protocols (e.g., Bluetooth Low
Energy (BLE), ZigBee), wireless body sensor network (WBSN),
and ad-hoc networks to connect the devices for data collection. The IoT platform provides a model to monitor the safety
and health of the elderly in the environment by continuously
collecting measurement data and sending it to a computing
center for further analysis [2]. The activity layer provides sensor data fusion and measurement analysis. This layer may
employ data analytics and artificial intelligence mechanisms
for the discovery of elderly behavioral insights and detecting
abnormal events. The recent advancement in big data analytics
and machine learning algorithms coupled with high-performance computing systems allows for instantaneous analysis
of the massive volume of data in near real-time. Existing
studies (e.g., [3] and [4]) show the potential of analyzing measurement data from smart devices installed in the environment
to monitor, assess, and predict an elder's activity and general

health based on context-awareness. This layer is also responsible for storing measurement data and disseminating results for
decision-making. Finally, the decision layer makes informed
actions based on data measurement analysis. This layer encompasses health services that can be customized according
to the situation of the elderly. It acts as a hub for information
sharing and action initiation by healthcare personnel, administration, and family members. The importance of this layer
lies in the assessment of emergency during pandemics or unexpected events. With the continuous analysis of information,
healthcare providers can monitor situations and take immediate actions, including urgent medical service and timely
interaction with caregivers and family members.

Measurement Requirements for an
Elderly Monitoring System
The COVID-19 crisis shows that older people need continuous monitoring to protect them from consequences of the
virus. Furthermore, if an elderly person becomes infected, the
monitoring system must be able to combine data from devices
and sensors to track the progress of the disease and report any
abrupt changes in the patient's health status. The design of
such a system must take into consideration several measurement requirements, as shown in Fig. 2 and described below.

Integration of Infection Control Measurements
Current monitoring systems lack mechanisms for infection
control to prevent the spread of deadly viruses. Traditional infection control systems are human-dependent and laborious,
which proved to be ineffective in senior homes during COVID-19, as many of these facilities are suffering from a severe
shortage of personnel. The integration of an infection control
mechanism into the elderly monitoring system must be able to
identify critical situations by analyzing inhibitory signs of virus infection parameters (e.g., excess temp, diarrhea, severe

Fig. 1. An overview of an elderly monitoring system and its functional layers.
April 2021	

IEEE Instrumentation & Measurement Magazine	7



Instrumentation & Measurement Magazine 24-2

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