Anomaly Detection for an Elderly Person Watching System using

Multiple Power Consumption Models

Maiya Hori

, Tatsu ro Harada

1,2

and Rin-ichiro Taniguchi

1,3

Center for Co-Evolutional Social System, Kyushu University, Fukuoka, Japan

Research and Education Center of Carbon Resources, Kyushu University, Fukuoka, Japan

Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

maiya-h@ieee.org, harada@cm.kyushu-u.ac.jp, rin@kyudai.jp

Keywords:

Safety, Anomaly Detection, P eople Activity Recognition.

Abstract:

We propose an anomaly detection method for watching elderly people using only the power data acquired by a

smart meter. In a conventional system that uses only power data, a warning is i ssued if the power consumption

does not increase after the wake-up time or when the amount of power does not change for a long time. These

methods need to set the wake-up time and power threshold for each user. Furthermore, wrong warnings are

issued while residents are out of the home. In our method, multiple common power consumption models are

created for each household for each short time zone, and a watching system is constructed by regarding the

gaps between these models and newly observed data as anomaly values. This can be automatically applied to

various situations such as “during sleep,” “during home activity” and “time zone for frequently going out in

the daytime.”

1 INTRODUCTION

According to a survey by the Cabinet Ofﬁce, Govern-

ment of Japan (Cabinet Ofﬁce, Governm ent of Japan,

2016), the to ta l population of Japan is 127.1 million

as of October 20 15. The population of elderly people

aged 65 a nd over is 33.92 million, which accounts for

26.7% of the total pop ulation. I t is estimated that the

aging rate will co ntinue to rise, reaching 39.9% in

2060, and at which time approximately 1 in 2. 5 pe-

ople will be ag e d 65 or over. Given this back groun d,

the development of a watching system that can recog-

nize an emergency situation involving the elderly is

desired from families living apart from the eld erly,

operators of nur sin g care services, and so on. Vari-

ous watching systems have been proposed and most

of them can notify the warning using a personal com-

puter, a smar tphone or by e-mail.

Various methods have b een proposed to detect an

anomaly in residents. Many systems monitor the lives

of elderly people by way of the installation o f various

sensors in houses and the performance of sensing in

real time (Doi et al., 2006) (Ota et al., 2011). Camera

and infrared sensors are representative examples, and

it is possible to monitor the behavior of residents with

high accuracy using these methods (Doi et al., 2006).

However, th e se methods have privacy problems, and

it cannot be said th a t the mental burden of residents

is small. Some systems monitor residents’ behavior

using sensors installed on electrical househo ld appli-

ances and doors frequently used in everyday life, such

as toilet doors and electric pots (kondo, 2 011) (Na-

kano and Ueno, 201 4). Residents can use watching

services while living their daily life. However, th e re

is a possibility th at detection may be delayed or mis-

recogn ition may occ ur when services are not used for

a long time. Nakano et al. (Nakano and Ueno, 2016)

created a watching system by estimating whether re si-

dents directly oper ated electrical equipment using the

full-load current o f househo lds measured every mi-

nute. This method took into account the living con-

ditions of reside nts by estimatin g the operating con-

dition of equipmen t deeply related to the daily living

behavior of residents. However, in formation on the

characteristics of electric appliances used in the home

is needed in advance. Because the afo rementioned

methods require the installation of new sensors, they

are difﬁcult to use immediately in various households

at present.

Therefore, in this research, we propose an ano-

maly detection method for watching the elderly that

uses only power c onsumption data obtained every 30

minutes from a smart meter installed in each house-

hold. A smart meter is a device that can measur e the

Hori, M., Harada, T. and Taniguchi, R-i.

Anomaly Detection for an Elderly Person Watching System using Multiple Power Consumption Models.

DOI: 10.5220/0006247006690675

In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), pages 669-675

ISBN: 978-989-758-222-6

669

amount of electricity used and it has the communi-

cation functionality to allow remote meter reading. In

Japan, along with liberalization of electr ic ity retail sa-

les, the installation of smart meters in each household

is progressing, and it is planned that the installation in

all households will be c ompleted by the end of ﬁscal

year 2024 (Ministry of Economy, Trade and Industry,

). Our proposed method does not require the installa-

tion of new sensors and it can be applied to all house-

holds where smart meters are installed. Furthermore,

because only electric power data is used, the mental

burden of residents is small.

In a conventiona l system that uses o nly power

data, a warnin g is issued if the power consumption

does not increase after the wake-up time or when the

amount of power doe s not change for a long time.

These methods need to set the wake-up time and po-

wer threshold fo r each user. Furthermore, wrong

warnings are issued while residents are out of the

home. Additiona lly, because of the characteristics of

the method, it takes time to detect an anomaly.

There are many conventiona l methods for de-

tecting an anomaly with re spect to measurement data.

A k-nearest neighbor (k-NN) algorithm (Dasarath y,

1990) is one o f the anomaly detection methods that

detects an outlier va lue. Its advantage is that supervi-

sed data is not necessary for anomaly detection. By

applying these to power data, it is possible to detect

the tim e of power usage that is different from d a ily

life as an anomaly; however, this will not work as a

watching system.

In our method, the problems are overcome by deﬁ-

ning features specialized for watching. The routine of

residents’ activities is learned automatically without

setting parameters o n the lifestyles for each re sident

by constructing different power consumption models

for each time zone. Furthermore, when newly obser-

ved da ta deviates from the generate d mode l, a war-

ning of an a nomaly is issued so that it is p ossible to

quickly detect an emergency situation.

2 ANOMALY DETECTION FOR

AN ELDERLY PERSON USING

MULTIPLE POWER

CONSUMPTION MODELS

In this study, we detect an anomaly for the watching

system when the ﬂuctuation of the power consump-

tion is small and different from usual. As a re sult,

unlike a conventional method, even when the power

consumption is large, when the ﬂuctuation of power

is sm a ll a nd it differs from usual consumption, it is

Figure 1: Flow of the proposed method.

detected as an anomaly. Furthermore, during sleep or

a time zone that the re sid ent often goes out, even when

the ﬂuctuation of power is small, it is not detected as

an a nomaly.

Figure 1 shows the ﬂow of the pro posed met-

hod. Fir st, power consumption d ata obtained from the

smart meter every 30 minutes is used for input. Next,

feature vectors are ge nerated using an index called a

non-ac tivity level. By accumulating this featur e vec-

tor, a c ommon distribution model is created, and the

gaps in the model are calculated for use as an anomaly

score. Finally, the transition of the anomaly score is

displayed for the eld erly per son watching system. De-

tails of e ach feature are described b elow.

2.1 Features for Anomaly Detection

To d etect an anomaly in the elderly, an index that in-

creases as the ﬂuctuation of power decreases is neces-

sary. As an index that inc reases as the ﬂuctuation of

power decreases, the reciprocal of the absolute value

of the power change can be considered:

− c

t−1

(1)

where c

represents the power consumption observed

at time t. When there is no power change, the value of

diverges to inﬁnity. Therefore, when v

is used as

an index for watching, it can be detected only when

the power change is close to zero. Even if it is an ano-

maly, however, a minute change occurs in the electri-

cal power. A dditionally, in this case, it is necessary to

perform anomaly detection appropriately.

Therefore, in this study, we deﬁne the non-activity

level N

1 + exp(− av

(2)

ICPRAM 2017 - 6th International Conference on Pattern Recognition Applications and Methods

670

(a) Time series data wi th respect to the power consumption

of a household for a week.

(b) v

calculated by Equation (1) with respect to the time

series data of (a).

spect to v

of (b).

Figure 2: Relationship between power consumption data

and the non-activity level.

Even if v

diverges to inﬁnity, that is, when there is

no power change, N

converges to one. Furthermore,

by adjusting the coefﬁcient a, it is possible to approx-

imate the value when there is no power change , even

when the electric power slightly changes. As a re-

sult, it is possible to detect an anomaly even when a

minute change occurs in electrical power when an el-

derly person is in an eme rgency situation. Note that

it is possible to detect an anomaly in a short time by

calculating N

at the sam e frequency as data acquisi-

tion.

Figure 2 shows an example of the relationship be-

tween actually observed power consumption data and

the non-a ctivity level. Figure 2 (a ) shows time se-

ries data with respect to the power consumption of

a household for a week. It can be observed that the

power consumption is large durin g the da y. Figure 2

(b) shows v

calculated by Equation (1) with respect

to the time series data of (a). As shown in this ex-

ample, v

has a large value when the change in the

power consumption in ( a) is close to zero. However,

even if this value is used for anomaly detection, it can

be de te cted only when the change in the power con-

sumption is close to zero. As a result, an anomaly

cannot be detected whe n a minute change occurs in

the power consumption . By contrast, (c) in Figure 2

is an example of plotting the non-activity level shown

in the expression (2). Particularly for the example of

a = 10, w e observe th at N

has a relatively large value,

even if there is a small change in the power consump-

tion. Common power consumption mode ls for each

time zone are constructed using the non-ac tivity level

Figure 3: Creation of multiple power consumption models.

calculated in this manner, and anomaly detection is

performed by the k-NN algorithm.

2.2 Creation of Multiple Common

Power Consumption Models

In our method, common power consumption models

are created and anomaly detec tion is pe rformed si-

multaneously with power measurement by regarding

the gaps between these models and newly observed

data as anomaly scores. The power consumption mo-

dels are created for each househ old to respond to the

diversity of residents’ behavior. If only one activity

model is generated by treating the non-activity le-

vels in all time zones, an anomaly cannot be detected

because many phenomena with a small power ﬂuc-

tuation are observed during sleep. It is conceivable

to construct a model that assumes speciﬁc behaviors,

such as “during sleep,” “during home activity” and

“time zone for frequently going out in the daytime.”

However, it is difﬁcult to assume all types of behavi-

ors because behaviors have diversity, and it is difﬁcult

to address temporal deviations in customary behavior.

Therefore, in th is research , as shown in Figure

3, multiple mo dels are built for the same tim e zone

on different days. Because power consumption data

is acquired every 30 minutes, 48 models are genera-

ted for each household. The models described here

are not deﬁned by mathematical expressions; they are

merely distributions of the non-activity levels. Mo-

dels that depend on the activity of a resident are crea-

ted for each time zone.

Figure 4 shows an example of the difference of

the distributions of the non-activity levels among dif-

ferent time zones. This is a visualization of the data

accumulated for three months for the non-activity le-

vels acq uired by a particular household. In this ﬁ-

gure, the higher the density, the higher the observa-

tion freque ncy of the value, and conversely, a sparser

density indicates more unusual phenomena. Figure 4

(a) shows the distribution of th e non-activity levels at

Anomaly Detection for an Elderly Person Watching System using Multiple Power Consumption Models

671

(a) Distribution of non-activity levels at 2:30 am.

(b) Distribution of non-activity level s at 7:30 am.

Figure 4: Difference of the distributions of the non-activity

level s among different time zones.

2:30 am. We know that the resident of this house-

hold goes to bed at this time on a daily basis. It is

found that the frequency at which the non-activity le-

vel shows a high value is high. Figure 4 (b) shows the

distribution of the non-activity levels at 7:30 am. We

know that the resident of this household has already

woken up at this time. It is understood that the fre-

quency at wh ich the non-activity level shows a high

value is not as high compared with case (a). Figure 4

12:30 pm. It is known that the resident of this hou-

sehold does not go ou t on a daily basis during this

time. It is found that th ere is almost no frequency for

which the non-activity level shows a high value. In

this method, for examp le , when a h igh value o f the

non-ac tivity level is newly observed during this time

zone, it is detected as an anomaly. As a result, it is

possible to respond ﬂexibly to various activities wit-

hout explicitly deﬁning the behavior of the reside nt,

such as “during sleep,” “during home activity” and

“time zone for frequently going out in the day time.”

In the above examples, N

(scalar) was used as

an index for the distribution models; however, in

our method, instead of N

, multidimensional vectors

, N

t−1

, N

t−2

···} can be used. This makes it possi-

ble to detect anomalies on a long-term basis.

By conﬁguring only the data at the time of th e nor-

mal pattern a s the accumulated past data, the advan-

tage is tha t there is no need to manually pre pare the

supervised data as observed in general m achine lear-

ning. Addition a lly, because of the characteristics of

the proposed method regarding setting the gaps f rom

Figure 5: An example of anomaly detection based on k-NN.

the non-activity level N

is used as an index of distribution

and this example shows an example when k = 3.

accumulated past data as anomalies, it is theoretically

possible to perform a calculation even if there are few

accumulated data. Furthermore, by automatically de-

leting past old data when constructing models, even

if the resident’s life routine changes, it is possible to

adapt ﬂexibly.

2.3 Anomaly Detection Based on the

k-NN algorithm

In our method, multiple common power consumption

models are created for each hou sehold for each short

time zone, and a watching system is constructed b y

regarding the gaps between these models and n ewly

observed data as anomaly values. In the k -NN based

method, for the newly observed data, k nea rest neig-

hbor data is selected from the accumulated past data.

Next, we calculate the average distance from tho se k

data and use this as the anomaly score.

Figure 5 shows an example when k = 3. If the

newly observed data is anomaly data, the average dis-

tance increases, and if it is no rmal data, it decreases.

Parameter k is determined experimentally, but if you

set k to be small in gen eral, it becomes sensitive to

anomaly data , and conversely, it becomes insensitive

if it is set large.

In the above examples, the non-activity level N

(scalar) is used as an index for distribution mo-

dels, but if, instead of N

, multidimension a l vectors,

, N

t−1

, N

t−2

···}, are used, th e distributions be-

come multidimention.

3 EXPERIMENTS

In this experiment, to show the effectiven ess of th e

proposed method, we veriﬁed whether anomaly de-

tection was properly performed by using actual po-

wer consumption data. The target was the power c on-

sumption data of an elderly single-person household

for three months from July 1 to September 30. The

ICPRAM 2017 - 6th International Conference on Pattern Recognition Applications and Methods

672

(a) Example of power consumption data for one week for

the target household.

(b) Comparison result of the non-activity levels. The upper

row of the ﬁgure shows the result of a = 1, the middle

row shows the result of a = 5 and the lower row shows the

result of a = 10.

Figure 6: Comparison result when a in Equation (2) is chan-

ged.

power consumption data was acquired by a smart me-

ter every 30 minutes.

First, as a preliminary expe riment, the comp arison

result when a in Equation (2) is changed is shown. Fi-

gure 6 shows data for one week for the target house-

hold.

Figure 6 (a) shows an example of power consump-

tion data for one week for the target household. Fi-

gure 6 (b) shows the comparison result of the non-

activity levels when a in Equation (2) is changed. The

upper row of the ﬁgure shows the result of a = 1,

the middle row shows the result of a = 5 an d the lo-

wer row shows the result of a = 10. It can be ob-

served that the non-activity level is large as a incre-

ases, even if there is a minute power chang e. When

a = 1, the non-activity level is small when there is a

minute power change. For anomaly detection, a was

found to require a somewhat larger value because a

minute change occurs in electrical power when an el-

derly person is in an emergency situation. Ther efore,

for all subsequent experiments, we used a = 10.

Next, a s a preliminary experiment, the com pari-

son result when k of th e k-NN algorithm changed is

shown. Figure 7 sh ows o ne wee k’s data for the hou-

sehold of the same subjec t as the previous prelimi-

nary experiment. Figur e 7 (a) shows an example of

power consum ption data for one week fo r the target

household. Figure 7 ( b) shows the comparison result

of the anomaly scores when k of the k-N N algorithm

is c hanged . The upper row of the ﬁgure shows th e re-

sult of k = 2, the middle row sh ows the result of k = 4

and the lower row shows the result of k = 8. In this ex-

periment, the total number of data of the accumulated

non-ac tivity levels is 91. If k = 1, the data is sensitive

(a) Example of power consumption data for one week for

the target household.

(b) Comparison result of the anomaly scores. The upper

row of the ﬁgure shows the result of k = 2, the middle row

shows the result of k = 4 and the lower row shows the result

of k = 8.

Figure 7: Comparison result when k of the k-NN algorithm

is changed.

to an anomaly, and conversely if k = 8, the data be-

comes insensitive to an anomaly. For all subseq uent

experiments, we used k = 4.

Figure 8 shows the results of the anomaly scores

on a particular day.

Figure 8 (a) shows an example of a small anomaly

score. This is a pattern that is comm on in everyday

life. Figure 8 (b) shows an example of a small ano-

maly score. In this case, although the ﬂuctuation of

power is small, similar patterns were obser ved for the

same time zon e . This sh ould not be detected as an

anomaly because this is a typical pa ttern tha t occur-

red during sleep. Figure 8 (c) shows an example of a

small anomaly score. In this case, although the po-

wer consumption is less than usual, the ﬂuctuation

of power is large. This case may be incurred when

not using equipm ent that consumes large amounts o f

electrical power, such as coolin g and heating equip-

ment. Figure 8 (d) and (e) show examples of large

anomaly scores. In th ese cases, the ﬂuctuation of po-

wer is small even tho ugh it is different from usua l.

This case may be incurred when the elderly person

does not get up in the morning. Figure 8 (f ) shows an

example of a large anomaly score. In this case, the

power consumption is large; however, the ﬂuctuation

of power is small. For the elderly watching system, it

is important to jedge as an anomaly in this case.

The above results ar e visu alized in real time and

shown to the families living apart from the elderly pe-

ople, operators of nursing care services, an d so on. Fi-

gure 9 shows the transition of the anomaly score over

three months. It is understood that a high anomaly

score is observed approximately 30 times in th ree

months. Vigilance is necessary, especially when the

Anomaly Detection for an Elderly Person Watching System using Multiple Power Consumption Models

673

(a) Example of a small anomaly score. This is a pat-

tern that is common in everyday life.

(b) Example of a small anomaly score. In this case,

although the ﬂuctuation of power is small, similar

patterns have been observed at the same time zone.

although the power consumption is less than usual,

the ﬂuctuation of power is large.

(d) Example of a l arge anomaly score. In this case,

the ﬂuctuation of power is small even though it is dif-

ferent from usual.

(e) Example of a large anomaly score. In this case,

the ﬂuctuation of power is small even though it is dif-

ferent from usual.

(f) Example of a large anomaly score. In this case,

the power consumption is l arge, but the ﬂuctuation

of power is small.

Figure 8: Examples of anomaly scores estimated by the proposed method. Each upper row shows the power consumption

data of one day and each lower row shows the result of anomaly score.

anomaly score is high continuously.

4 DISCUSSION

As shown in Figure 8, it is possible to automati-

cally detect anomalies for various patterns by con -

structing multiple com mon power consumption mo-

dels for each time zone and using the k-NN method

to r egar d the g a ps between these models and newly

observed d a ta as anom aly scores. Detecting a speci-

ﬁc pattern is possible with the conventional method;

however, not only it is necessary to set parameters for

each household, but also only some typical patterns

can be detected. Although our method has two va-

riable parameter s, a in Equation (2) and k of the k-

NN a lgorithm, it is no t necessary to change these in

particular, and the parameters determined during the

preliminar y expe riment c an also be applied to other

households.

A limitatio n of the proposed method is th a t an

ICPRAM 2017 - 6th International Conference on Pattern Recognition Applications and Methods

674

Figure 9: Transition of the anomaly score.

anomaly is barely detected w hen various power con-

sumption patterns are observed. This is one of th e

limitations of the k-NN algorithm, which ma kes th e

gaps from the normal pattern anomalous. To over-

come this limitation, it is necessary to arrange the

data when constructing a normal pattern model. Be-

cause there are four seasons in one year in Japan, and

a person ’s life pattern gradually changes in each sea-

son. We believe that it is possible to respond to these

changes using data from approximately three months

in the near future, that is, not using all past data, when

generating the distribution model. Hence, in the ex-

periment, we used data for three months fro m July 1

to September 30. Thus, it is possible to respond au-

tomatically when a life pattern changes for reasons

other than the inﬂuence of the season.

5 CONCLUSIONS

We p roposed an anomaly detection method for wat-

ching elderly people using only the power data acqui-

red by a smart meter. Multiple common power con-

sumption models were constructed for each time zone

and an omaly detection for watching the elderly was

condu c te d using th e k-NN algorithm to regard the

gaps between these m odels and newly observed data

as anomaly scores. As a result, it was possible to re-

spond ﬂexibly to various activities without explicitly

deﬁning the behavior of the resident, such as “during

sleep,” “during home activity” and “time zone for fre-

quently going out in the daytime.”

A future task is to conduct demonstration expe-

riments on elder ly people and implement social ex-

periments in cooperation with local governments. To

operate in the real world , we will need to properly set

the timing to notify u s of an anomaly by considering

the opinions of families living apart from the elderly

and operators of nursing care services.

ACKNOWLEDGEMENTS

This research was supported by the Japan Science

and Technology Agency (JST) through its Center of

Innovation: Science and Tech nology Based Radi-

cal Innovation and Entrepreneurship Program (COI

STREAM).

REFERENCES

Cabinet Ofﬁce, Government of Japan (2016).

Annual report on the aging society.

http://www8.cao.go.jp/kourei/whitepaper/w-

2016/zenbun/28pdf

index.html.

Dasarathy, B. V. (1990). Nearest Neighbor: Pattern Classi-

ﬁcation Techniques. IE EE Computer Society.

Doi, M., Inoue, H., Aoki, Y., and Oshiro, O. (2006). Video

surveillance system for elderly person living alone by

person tracking and fall detection. IEEJ Transactions

on Sensors and Micromachines, 126(8):457–463.

kondo, S. (2011). Safety conﬁrmation system for elderly

single-person household with sensor systems. IEEJ

Transactions on Electronics, Information and Sys-

tems, 131(7):1293–1297.

Ministry of Economy, Trade and Industry.

http://www.meti.go.jp/english/index.html.

Nakano, Y. and Ueno, T. (2014). Sensor data classiﬁcation

for indoor situations using the multifunctional outlet.

IEEJ Transactions on Electronics, Information and

Systems, 134(7):949–955.

Nakano, Y. and Ueno, T. (2016). Watching system for

aged single person based on electricity use. IEEJ

Transactions on Electronics, Information and Sys-

tems, 136(6):767–774.

Ota, K., Otsu, M., Ota, Y., and Kajiwara, A. (2011).

Elderly-care motion monitoring sensor using ultra-

wideband radio. IEEJ Transactions on Electronics,

Information and Systems, 131(9):1547–1552.

Anomaly Detection for an Elderly Person Watching System using Multiple Power Consumption Models

675