# Real-Time Energy Measurement for Non-Intrusive Well-Being Monitoring of
Elderly People – a Case Study ††thanks: This work was supported by the
National Centre for Research and Development within the Smart Growth
Operational Programme (agreement No. POIR.01.02.00-00-0352/16-00).

Mateusz Brzozowski
OneMeter Sp. z o.o.
ul. Dobrzańskiego 3
20-262 Lublin, Poland
Email<EMAIL_ADDRESS>
Artur Janicki
Warsaw University of Technology
ul. Nowowiejska 15/19
00-665 Warsaw, Poland
Email<EMAIL_ADDRESS>
###### Abstract

This article presents a case study demonstrating a non-intrusive method for
the well-being monitoring of elderly people. It is based on our real-time
energy measurement system, which uses tiny beacons attached to electricity
meters. Four participants aged 67–82 years took part in our study. We observed
their electric power consumption for approx. a month, and then we analyzed
them, taking into account the participants’ notes on their activities. We
created typical daily usage profiles for each participant and used anomaly
detection to find unusual energy consumption. We found out that real-time
energy measurement can give significant insight into someone’s daily
activities and, consequently, bring invaluable information to caregivers about
the well-being of an elderly person, while being discreet and entirely non-
intrusive.

###### Index Terms:

energy measurement, smart metering, assistive technology, assisted ambient
living, anomaly detection, non-intrusive monitoring

##  I Introduction

Our societies are getting older. The growing number of older people requires,
among other things, effective yet discreet methods of monitoring the well-
being of the elderly by their caregivers, such as children or other relatives.
In this article, we present a method for non-invasive remote monitoring of a
person’s well-being at their residence based on real-time electricity
measurement. Our method is based on the electric energy measurement system
presented in [1]. It was originally proposed to speed up the development of
smart metering. However, here, we will present its application to assistive
technologies.

The proposed monitoring method does not require any interference with
installations in the user’s residence, nor does it involve using any wearable
devices, such as smartwatches or wristbands. It does not require the
installation of a camera or microphone at the user’s residence, which could be
considered invasive to privacy. The proposed method is suitable for use with
different types of electricity meters, which may or may not be smart. It is
sufficient that they are equipped with an optical port, a flashing diode, or
another port for communication (e.g., galvanic).

In this paper, in Section II, we will briefly present existing solutions for
monitoring elderly people in their homes. Next, in Section III, we will
describe the proposed method for non-invasive monitoring of elderly people
using energy measurement based on our approach. In Section IV), we will
present the setup of our study, followed by initial results, presented in
Section V. Finally, we will conclude in Section VI with a plan for the future
of our work.

Figure 1: OneMeter beacon with optical sensor attached to IEC 62056-21 port of
electricity meter.

##  II Monitoring Elderly People

Various methods of remote monitoring of human well-being are known. For
example, video and voice monitoring systems are used, which involve installing
devices such as motion sensors, cameras, and microphones at the residence of
the person being monitored, forming the so-called assisted living system (ALS)
[2, 3]. These devices are designed to check how the person moves around the
apartment, such as whether the person gets out of bed, opens the medicine
cabinet, walks from the room to the kitchen, etc. The microphone or microphone
arrays, on the other hand, are designed to record disturbing noises that, for
example, may indicate a fall [4]. Another way is to equip the monitored person
with various types of sensors, such as a wristband, smartwatch, or chest-
mounted sensors [5, 6]. These monitor the monitored person’s vitals, such as
pulse or oxygen saturation.

Some systems analyze electricity consumption by inserting an appropriate probe
into the user’s electrical system, which measures energy independently of the
measurement that the electricity supplier makes. This is called non-intrusive
load monitoring (NILM) and has been described, e.g., in [7, 8] for helping
patients with Alzheimer’s disease.

However, the methods of monitoring human well-being discussed above have
several disadvantages. For example, they require the installation of devices
that can invade the privacy of the monitored person, such as sensors, cameras,
and microphones, making them often unacceptable to the monitored person. Other
methods require the monitored person to wear devices such as wristbands or
other wearables, which is also inconvenient and, therefore, often rejected. In
addition, the monitored person may remove their wearables (e.g., before
bathing) and forget to put them back on. These devices also require regular
charging. Once they are discharged, they stop working, which can cause
interruptions in monitoring and generating false alarms. Finally, other
existing methods require interference with the electrical system at the
residence of the monitored person, which is costly and requires employing
qualified staff, and, as a consequence, it may also not be accepted by the
monitored person. To sum up, contrary to our solution, none of the above-
described approaches is truly non-invasive.

TABLE I: Study participants and electrical equipment they used User | Age | Sex | Lighting | Electrical devices used
---|---|---|---|---
Regular | LED | Fridge | Kettle | Oven | Dish- | Hair- | Washing | Iron | TV | Other
washer | dryer | machine
S1 | 82 | F | - | x | x | x | x | x | x | x | x | x | -
S2 | 76 | M | x | x | x | x | x | x | x | x | x | x | Alarm
S3 | 77 | F | - | x | x | - | - | - | - | x | - | x | -
S4 | 67 | F | - | x | x | x | x | - | x | x | x | x | A/C
Figure 2: 15-min mean power profiles for clustered into three clusters for
monitored user S4. The thick black line shows the mean profiles for each
cluster.

##  III Proposed method

Figure 3: Mean 15-min mean power profiles for three clusters identified per
each monitored user.

The idea of our solution is based on using a small, energy-efficient wireless
beacon with optical sensors attached to an electricity meter (see Figure 1). A
small bottle cap-shaped beacon of 32 mm diameter (compatible with the IEC
62056-21 standard) was designed, equipped with an optical sensor, LED diode,
Nordic Semiconductor’s processor nRF51, flash memory, Bluetooth Low Energy
(BLE) radio components, and a 3.0V battery (CR2032). The beacon is attached
magnetically to an electronic meter equipped with an optical port. The optical
sensor is designed with a miniature silicon photodiode of high radiant
sensitivity and a low-power comparator. The optical sensor, together with the
IR LED diode, are able to set up communication with a meter using the IEC
62056-21 (old: IEC 1107) protocol. The amount of measurement data acquired
from the meter depends on the meter’s model – some of the meters present only
the absolute active energy, while the others allow the readout of more
detailed information, such as positive and negative active energy, or reactive
energy.

The processor is programmed so that the beacon performs a readout of the meter
every 15 min and stores the metering data in the flash memory. The BLE
component allows other BLE devices to connect to the beacon to download
metering data or to transmit the readout in real-time. In the original version
[1], we proposed employing either smartphones or dedicated gateways to
transfer measurement data to the cloud. For real-time transmission, the
preferable option would be using gateways, e.g., based on the LoRaWAN
protocol, as described in our another study [9].

Energy measurement data are then securely transmitted to the data platform. It
provides gathering, analysis, and visualizations of the collected metering
data. It can also allow performing analyses that can be used for non-intrusive
monitoring of people’s well-being. In this case study, we will show that the
measurement data can be used to verify if a user follows a regular daily
routine or, using anomaly detection, if the energy consumption is somehow
anomalous, thus implying potential problems with the user’s well-being.

##  IV Experiments

Figure 4: Three anomalous days detected for user S1. Black lines denote the
closest mean usage profile; red lines denote the usage from the anomalous
days.

###  IV-A Participants

Four participants (named S1–S4), aged 67–82 yrs, took part in our study. They
had the energy monitoring system installed for approx. one month, between June
and September, in Warsaw, Poland. Their households were screened for
electrical equipment being used. The participants were also asked to make
notes on their home activities and their own well-being. Each of the
participants signed a consent to take part in this study.

Each of the participants lived alone in their homes. Each household was lit
with LED lighting; in addition, participant S2 used traditional electric
bulbs. All households were equipped with a fridge, a TV set, and a washing
machine. Half of the participants used a dishwasher. Even though all
participants had hair dryers, S3 never used them, as she let her hair dry
naturally. Similarly, she never ironed her clothes, but she let them dry
naturally. She also used gas to cook and make tea or coffee. Participant S3
had an alarm system at home. Participant S4 had a portable air-conditioning
system, which she used on hot days. Noteworthy, in none of the households in
this study, the electricity was used for house heating or domestic water
heating.

The participants also differed in terms of lifestyles. Participants S1 and S2
occasionally received guests, and the others did not. Participant S2 traveled
and was absent for a couple of days. Participant S2 thoroughly cleaned the
house once a week, while the others did it irregularly. Participant S4 often
used her washing machine. All the above-presented information will be needed
when analyzing the mean power profiles.

###  IV-B Data analysis

We analyzed the collected energy measurement data by calculating the 15-min
mean power value throughout the study’s timespan. For each participant, we
analyzed daily profiles of these values. Next, using a clustering algorithm
(in our case, the $k$-Means algorithm, originating from the signal processing
domain [10]), we grouped the daily profiles into $k$ clusters, changing $k$ in
the range from $1$ to $6$.

Next, we employed an anomaly detection algorithm by identifying time series
(here: daily profiles), which were the furthest from the cluster’s centroids.
We compared this information with the participants’ notes. The results are
presented and described in detail in the next section.

##  V Results

15-minute mean power profiles for each user have been collected and analyzed.
Fig. 2 shows the result of grouping them into three clusters for one of the
users, S1. The figure displays daily profiles and the average profiles in each
of the clusters. As one can see, the k-Means algorithm was able to identify
three typical daily routines of the S4 user:

  * •

Cluster 1: with increased power consumption around noon. The S4 user reported
the following activities for these hours on the days attributed to these
clusters: cooking lunch on an electric oven, cleaning the house with a vacuum
cleaner, and doing laundry.

  * •

Cluster 2: with increased power consumption in the late afternoon. Activities
recorded: baking a cake for a name day party, running an air-conditioning;

  * •

Cluster 3: the most typical routine: slightly increased power consumption in
the morning, around 8 a.m. (using a hair-dryer, boiling water for tea/coffee,
etc.), and similarly slightly increased power consumption in the evening,
around 8–9 p.m. (watching TV, using lighting).

At night, the power consumption was minimal (probably only the fridge consumed
power, plus devices on stand-by, like a TV or phone charger).

As one can see in Fig. 3, similarly, such clusters were created for all four
participants of this study. Usually, one of these clusters was the most
populated, as it contained “the most typical” daily routine (as Cluster 3 in
the above example for user S4), with preparing meals, watching TV, running a
dishwasher, etc. The other clusters, according to the participants’ reports,
could be attributed to the days when the participants received guests, did
more cooking (e.g., baking), did laundry, or cleaned the house with vacuum
cleaners.

We researched various numbers of clusters (from 1 to 6), but it turned out
that three clusters were sufficient to cover the variability of daily routines
for the study participants and the number of days being analyzed. One
exception here was the S3 user, for whom the mean power profiles in each of
the three clusters were very similar, indicating that a lower number of
clusters (two, or even one) might be sufficient. In this case, the most
populated was Cluster 1, showing very low variability within a day. It turned
out that this user rarely used any electric devices, apart from LED lighting
and energy-saving TV. The S3 user used gas for cooking, rarely ironed clothes,
and always let her hair dry naturally. Therefore, the daily fluctuations in
power consumption were minimal, and the possibility of monitoring the user’s
well-being in such a case would be very limited.

Having grouped the users’ mean power profiles into clusters, we tried to use
them to identify anomalous days to see if changes in user behavior could be
captured by monitoring electric energy consumption. To do so, we found the
days with the mean power profiles most distant (in the Euclidean sense) from
the closest average profile. Fig. 4 shows such profiles for user S1. According
to the S1 participant, the identified days corresponded to the days with
extensive evening baking accompanied with washing dishes in a dishwasher (24
Jun), baking a cake in the morning (9 Jul), or being absent in the morning (20
Jul) and shifting morning activities (preparing a meal, washing hair) a couple
of hours later. As one can see, observing energy consumption can give
significant insight into someone’s daily activities and, as a consequence,
bring invaluable information to caregivers about the well-being of an elderly
person, or another person who lives on their own but needs discreet, non-
intrusive monitoring.

##  VI Conclusions and Future Work

The proposed method offers an entirely non-intrusive method of monitoring
elderly people or any other people who need to be discreetly supervised, such
as by their relatives or other caregivers. The method works best if the
supervised person uses more electrical devices, such as an electric kettle or
an oven. Low-power devices, such as radio receivers, may not be readily
visible in our measurements. If a person uses fewer electrical devices,
information about, e.g., someone’s reduced activity may be observed with some
delay. This delay can depend on the time of year, which affects the usage of
electric lighting at home.

It should be noted that information about someone’s energy consumption can
pose a threat to their privacy. This means that the energy measurement data
should be securely transmitted, stored, and analyzed. It is important to
ensure that a person who is monitored consents to the monitoring.

In the future, observing a larger group of participants over a much longer
time span would be very advantageous. It would allow for the taking into
account of changes in daylight cycles, which influence energy consumption. It
would also allow us to observe long-term changes in the users’ behavior.

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