BEYOND INDOOR PRESENCE MONITORING

WITH SIMPLE SENSORS

Tuan Anh Nguyen and Marco Aiello

Distributed Systems Group, University of Groningen, Groningen, The Netherlands

Keywords:

Context-awareness, Activity Recognition, Energy-awareness, Wireless Sensor Networks.

Abstract:

To have buildings that are able to adapt to the user needs and at the same time to operate efﬁciently, it is

essential to know the activity the people are performing. Presence sensors, which are widely deployed in

modern buildings, attempt to regulate lighting to the presence of people in indoor spaces. Though, much more

in terms of comfort and energy efﬁciency can be achieved if more detailed information on the activity of the

users is detected. In this paper, we provide an initial investigation on detecting indoor activities by using

simple sensors (infrared, pressure and acoustic) deliberately avoiding the use of rich sensors such as cameras.

The sensors are low-cost, wireless, and retroﬁttable in existing structures. Our prototype is able to recognize

ﬁve activities (working at a desk with or without a PC, having a meeting, and the presence/absence in the

ofﬁce) with accuracy of almost 95%, while unaffecting user’s behavior and comfort.

1 INTRODUCTION

Buildings account for more than 40% of energy con-

sumption in EU-27, with up to 50% in the UK

and Switzerland, and are the largest CO2 produc-

ers (EPBD, 2002). In addition, for typical industrial

and commercial buildings power usage amounts to

roughly 30% of the total operational costs. Hence, to-

wards a low carbon economy, making “smarter” use

of energy in buildings will fundamentally contribute

to energy and cost savings. Building automation sys-

tems (BAS) provide automatic control of the condi-

tions of indoor environments. Their primary goal is to

realize signiﬁcant savings in energy and reduce cost.

While BAS can currently operate lighting and heat-

ing, ventilating and air conditioning (L-HVAC) sys-

tems efﬁciently, they are based on very simple solu-

tions to detect the presence of people. In fact, cur-

rent technology is based on infrared sensors that de-

tect movement and assume presence for a ﬁxed tem-

poral interval after the last movement was detected.

Such a solution is reasonable in ﬁrst approximation,

but clearly having better understanding of user activ-

ities, would lead to better control and possibly higher

user comfort.

User activity is an essential ingredient for maxi-

mizing the energy savings and spurring rapid adop-

tion in buildings. The recognition solutions should

be very accurate to satisfy the user and provide effec-

tive energy savings, while consuming not too much

energy itself to function nor invading the privacy of

the user. Most current solutions for BAS still operate

based on pre-determined schedules. HVAC condition

rooms assuming maximum occupancy rather than ac-

tual usage. Most occupancy sensors currently in-

stalled within buildings are fairly coarse-grained and

inaccurate. Passive infrared (PIR) based sensors are

often used for occupancy given their relative low cost

and energy consumption, though these sensors only

sense movements and most of them use a timeout for

determining room occupancy. More advanced sys-

tems have been deployed using cameras and vision

algorithms, but these systems suffer from deployabil-

ity, cost and privacy issues.

We propose an accurate activity recognition sys-

tem working at the level of individual ofﬁces. Our

work contributes to one of the key components of an

energy-aware framework based on embedded service

middleware and a building-distributed architecture of

smart objects. The platform is currently under de-

velopment within the European Framework 7 project

GreenerBuildings, (GreenerBuildings, 2010). In the

present work, we design and illustrate a system based

on a battery-based wireless sensor network that can

identify ﬁve activities of one person in an ofﬁce, pos-

sibly with other people present in the room (e.g, meet-

ing at the table). The activities are: working at a desk

with or without a PC, having a meeting, and the pres-

Nguyen T. and Aiello M..

BEYOND INDOOR PRESENCE MONITORING WITH SIMPLE SENSORS.

DOI: 10.5220/0003801300050014

In Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems (PECCS-2012), pages 5-14

ISBN: 978-989-8565-00-6

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

ence/absence in the ofﬁce, shown in Table 1. This is

done combining sensed data from a PIR, pressure and

acoustic sensors. We evaluate the initial prototype in

terms of accuracy and show that it can detect the activ-

ities in single room ofﬁces. We stress the requirement

of not resorting to any advanced sensors which are ex-

pensive or require a change in user behavior, such as

cameras, RFID tags, or wearable sensors. Instead of

that, simple, wireless, binary sensors are enough for

accurate activity recognition. Through a number of

experiments performed in our ofﬁces, we show that

with the proposed solution it is possible to recognize

ﬁve activities of interest with 94.81% accuracy.

The remainder of the paper is organized as fol-

lows. In Section 2, we describe the requirements in-

cluding the desired set of recognized activities. Sec-

tion 3 presents the implementation of the solution in

an actual ofﬁce and experimental results. We compare

our approach with related work in Section 4. Finally,

we conclude by summing up our ﬁndings and propose

future direction for investigation in Section 5.

2 INDOOR ACTIVITY

RECOGNITION

The main requirements for performing indoor activity

recognition with the goal of providing input to a con-

trol strategy for energy savings in ofﬁce buildings are

the following ones.

• (R1) Discriminative. The solution should distin-

guish as many activities as possible which are typ-

ical of ofﬁce presence.

• (R2) Simple sensors. The sensors used in the solu-

tion should be inexpensive and easy to retroﬁt in

existing buildings.

• (R3) Energy parsimonious. The sensing solution

should itself consume not too much energy.

• (R4) Privacy preserving. The solution should be

as less invasive as possible and affect the user pri-

vacy as little as possible.

• (R5) Accurate. The solution should be accurate in

its recognition of user activities.

2.1 Requirement Analysis

We address (R1) by performing a survey for a com-

plete working day (8 hours) in two ofﬁces, one at

a higher education institution’s ofﬁce building in

Groningen (Hanzehogeschool, The Netherlands) used

by two staff members and the another one at the

Figure 1: Activities of interest survey results.

premises of Mennes & Jager in Groningen, an elec-

tronics company with ﬁve staff members.

The ofﬁces were ﬁlmed and then the footage used

to classify by hand the activities of people over time.

The results of the manual classiﬁcation are summa-

rized in Figure 1 as pie-charts. One notices in both

cases a dominance of being at the desk working with

a PC or without one. At both Mennes & Jager and

Hanzehogeschool, occupants mainly spend their time

working with computers at their desks, nearly 5 hours

and 3 hours, respectively. They spend the main part of

their remaining time sitting on their chairs at the desk

doing other tasks. In total, staff at Hanzehogeschool

use 5 hours (63%) out of their 8-hour working day sit-

ting at their desk versus 6.7 hours (84%) at Mennes &

Jager. Absence is considered as an activity, represent-

ing the unoccupied status of the ofﬁce. Both ofﬁces

are not occupied more or less for a half hour dur-

ing the ﬁlmed working day. Presence indicates that

user is in the room but the speciﬁc activity does not

have any signiﬁcant impact on changing the state of

the appliances. Activities of this kind include tem-

porarily walking or standing looking through win-

dows, drinking tea and so on. In the data collection

at Hanzehogeschool, presence happens in two hours

and a half while the same occurs for just 0.7 hour at

Mennes & Jager in Groningen ofﬁce. With respect to

energy-awareness, “having a meeting” is an activity

that requires appliance’s adaptations such as turning

the lights on during the meeting and turning them off

otherwise, thus this activity is worth taking into ac-

count in order to save energy and satisfy user com-

fort. Therefore, in ﬁrst instance we take the activities

that have emerged from the video study as the start-

ing point of the indoor activity recognition system;

namely: 1) Working with PC, 2) Working without PC,

PECCS 2012 - International Conference on Pervasive and Embedded Computing and Communication Systems

3) Having a meeting, 4) Presence, and 5) Absence.

Activities and their deﬁnition are described in Table 1.

Table 1: Ofﬁce activities.

Activity Deﬁnition

Activity 1: Working with PC Manipulating keyboard or mouse

Activity 2: Working without PC Sitting at the desk but not manipulat-

ing keyboard and mouse

Activity 3: Having a meeting Discussing at the meeting table

Activity 4: Presence Being active in the room but the spe-

ciﬁc activity is not recognized

Activity 5: Absence Being absent from the room

The requirements (R2)-(R4) are strictly interdepen-

dent as they mostly involve the choice of the type

of sensor and the characteristics of the hardware cur-

rently available that implements them. We need to

avoid cameras for privacy reasons and sensors which

require users to wear them, such as RFID, active

badges and the like. We also have to look at sensors

which provide discriminative data without costing too

much and consuming excessive amounts of energy. In

summary, we resort to a combination of anonymous

and binary wireless sensors that are cheap, easy to

install, require minimal maintenance and supervision,

and do not have to be worn or carried, such as 1) chair

pressure sensor, 2) acoustic sensor, and 3) passive in-

frared (PIR) motion sensor. These sensors are placed

so that they are able to sense crucial information. In

addition, user privacy is unaffected as information is

sensed in binary manner (i.e. TRUE, FALSE), for ex-

ample whether the user is sitting on a chair or not,

whether the keyboard/mouse is being operated or not,

etc.

Finally, with respect to (R5), we propose the fol-

lowing recognition cases:

• Working with PC activity: we deﬁne this activity

when the user sits on the working chair AND uses

the PC by operating the keyboard/mouse.

• When user sits on a chair but does not operate the

keyboard/mouse then user is Working without PC.

• Having a meeting is indicated by user sits on a

chair at the meeting table AND speaking.

• Our system reports the current activity is Presence

in two cases: when user does not sit on working

chair, however user might be sitting on a chair at

the meeting table but no discussion is detected. In

the second case, no chair in the room is occupied,

but movement is still detected inside the room.

• The last activity Absence is indicated if following

condition is satisﬁed: no chair in the room is oc-

cupied AND no movement is detected.

Table 2: Activities associated with sensors’ values.

No.

Boolean value of sensors

Activity

Working

chair

Key/Mouse

acoustic

Meeting

chair

Meeting

acoustic

PIR

1 T T T T T

2 T T T T F

3 T T T F T

4 T T T F F

Working

5 T T F T T

with PC

6 T T F T F

7 T T F F T

8 T T F F F

9 T F T T T

10 T F T T F

11 T F T F T

12 T F T F F

Working

13 T F F T T

without PC

14 T F F T F

15 T F F F T

16 T F F F F

17 F T T T T

18 F T T T F

Having

19 F F T T T

a meeting

20 F F T T F

21 F T T F T

Presence

22 F T T F F

23 F T F T T

24 F T F F T

25 F F T F T

26 F F T F F

27 F F F T T

28 F F F F T

29 F F F F F

Absence

30 F T F T F

31 F T F F F

32 F F F T F

2.2 Activity Recognition

The task of recognizing an activity is reduced to read-

ing the sensor data and associating the data to a spe-

ciﬁc conﬁguration which, in turn, identiﬁes an activ-

ity. This is best represented as a truth table of basic

sensor values, shown in Table 2, in which T means

TRUE and F stands for FALSE. The table is exhaus-

tive of all possible readings of the sensors.

The recognition of the described cases are

performed on a base station which makes a clas-

siﬁcation every minute. Each minute, the Base

Station receives reports from the sensors, it fuses the

information to decide on what activity is currently

BEYOND INDOOR PRESENCE MONITORING WITH SIMPLE SENSORS

Table 3: Information Fusion at the Base Station.

Activity 1:

Working with PC

condition-1= (WorkingChairOccupy == T RUE ∧

Key/MouseOccupy == T RUE)

Activity 2:

Working without PC

condition-2= (WorkingChairOccupy == T RUE ∧

Key/MouseOccupy == FALSE)

Activity 3:

Having a meeting

condition-3= (WorkingChairOccupy == FALSE ∧

MeetingChairOccupy == T RUE ∧ HumanVoice == T RUE)

Activity 4:

Presence

condition-4= ((WorkingChairOccupy == FALSE ∧

MeetingChairOccupy == T RUE ∧ HumanVoice == FALSE)

(WorkingChairOccupy == FALSE ∧ MeetingChairOccupy == FALSE ∧

PIR == T RUE))

Activity 5:

Absence

condition-5= (WorkingChairOccupy == FALSE ∧

MeetingChairOccupy == FALSE ∧ PIR == FALSE)

occurring. Table 3 shows the information fusion

process for recognizing the current activity. In

the ﬁrst case, Activity 1-Working with PC is con-

ﬁrmed by the condition: (WorkingChairOccupy ==

T RU E ∧ Key/MouseOccupy == T RU E). Activ-

ity 2-Working without PC is recognized through

the change in Keyboard/Mouse Occupy status,

with the condition: (WorkingChairOccupy ==

T RU E ∧ Key/MouseOccupy = FALSE). Acous-

tic sensor at the meeting table is used to check

the status of Human voice appearance and Ac-

tivity 3-Having a meeting is recognized un-

der the condition: (WorkingChairOccupy ==

FALSE ∧ MeetingChairOccupy == T RUE ∧

HumanVoice == T RU E). In the case of

Activity 4-Presence the following condi-

tion is checked: ((MeetingChairOccupy ==

T RU E ∧ HumanVoice ==

FALSE)

(WorkingChairOccupy == FALSE ∧

MeetingChairOccupy == FALSE ∧ PIR ==

T RU E). Finally, (WorkingChairOccupy ==

FALSE ∧ MeetingChairOccupy == FALSE ∧

PIR == FALSE) conﬁrms Activity 5-Absence. The

condition-(1 to 5) is then used in our recognition

algorithm represented in Algorithm 1. To give best

performance, synchronization of the sensed data is

important, the more precise the alignment of the

reports is, the more accurate the sensor fusion is.

We use the Packet-level Time Synchronization that

is limited to single-hop communication described

in (TEP133, 2008). More precisely, the proposed

recognition algorithm is given in Algorithm 1.

In the algorithm, the input is a vector of sensor

data, in our case, it is V = [WorkingChairOccupy,

Key/MouseOccupy, MeetingChairOccupy, Human-

Voice, PIR]. Data is pushed to the Base Station

periodically and the vector V is updated with the data

Algorithm 1: Activity Recognition.

Input: V: vector of sensor readings

Output: A: recognized user activity

1: for each time interval do

V = read(V)

3: A = activity

∗

(

V ) {* See Table 3}

4: return A

5: end for

and the time stamp (e.g. each minute, each ﬁve

minutes, etc.). The implemented time synchroniza-

tion mechanism (Packet-level Time Synchronization)

guarantees sensor data timestamps are accurate up to

±(T

/2 − min) (where T

is the round trip time and

min is an estimated minimum time for sending any

message). For a single hop network, the accuracy

is very high, given the small value of T

, especially

when compared with the size of the time interval.

By using reliable messaging, the base station is also

guaranteed to eventually get all sensor readings for

each time interval; moreover, message omissions due

to packet losses are handled by keeping the previous

state as valid until a new message arrives. The al-

gorithm works as an inﬁnite cycle that at the end of

each time interval reads all the pushes of the sensor

data and it uses them to determine which activity has

taken place in that time interval, that is, the sensor

data is read to

V from V by

V = read(V ) and then the

function activity(

V ) recognizes the occurring activity

based on the information fusion cases of Table 3.

3 IMPLEMENTATION

To test the quality of the activity recognition with real

sensors, we make a study in our own ofﬁce at the Uni-

versity of Groningen, using three types of sensors and

PECCS 2012 - International Conference on Pervasive and Embedded Computing and Communication Systems

classifying ﬁve activities, as presented in the previ-

ous sections. In one of the rooms, chairs occupancy

is detected by a pressure sensor. We also place one

acoustic sensor near the keyboard and mouse of desk-

top position. Another acoustic sensor is placed at the

meeting table for detecting human voice as the sign

of a meeting taking place. Finally, one central ceil-

ing mounted PIR sensor is placed in the center of the

room. Figure 6(a) overviews our instrumented room.

3.1 Simple Sensors

We use IEEE 802.15.4 compliant wireless sensors

based on the original open-source “TelosB” platform

design developed and published by the University of

California, Berkeley (“UC Berkeley”). The hardware

is produced by Advantic Systems (ADV, 2011), see

the photos in Figure 4. The sensors are equipped

with ultra low-power 16bit microcontroller MSP430

and run a low-power consumption management algo-

rithm. The motes also have an extension interface

that can be used to connect various sensor boards

containing photo-, temperature-, humidity-, pressure

sensors, and accelerometers, magnetometers and mi-

crophones. The on-board PIR and Microphone are

used together with FlexiForce pressure sensor. The

motes are programmed in nesC and run on the TinyOS

2.1.1 platform (Philip Levis, 2004), a lightweight,

low-power platform for embedded operating systems.

The component for Information Fusion and Activity

Recognition on the Base Station is written in Java.

Let us consider the sensors one by one.

3.1.1 Pressure Sensor

We use a pressure sensor to detect chair occupancy

status. The sensor designed by Advantic Systems uses

the Tekscan



A201-100 FlexiForce



sensor (see

Figure 4(b)), which provides force and load measure-

ments. The FlexiForce sensor can be used to measure

both static and dynamic forces (up to 100lb or 400N),

and is thin enough to enable non-intrusive measure-

ment. In our experiments, the sensor is placed on the

chair as shown in Figure 6(c). Figure 2 shows a 30-

minute (from minute 28 to minute 55) result from one

of our tests about chair pressure. When no one sits

on the chair, pressure sensor returns a value which is

less than 5 Newtons and has an average value of about

1 Newton. While the chair is occupied, the value of

pressure is greater than 20 Newtons and can go up

to almost 100 Newtons. Additionally, when work-

ing at the desk (with or without PC), the user might

stand up for a while or change the sitting posture thus

the pressure value hovers over time, however, we es-

tablish from experimental data that when the chair is

occupied the pressure average value in one minute is

greater than 20 Newtons while less than 10 Newtons

is the value when the chair is unoccupied. Therefore,

a one-minute average value of 20 Newtons is set as

the threshold to distinguish among the two states of

the chair’s occupancy. In order to get a precise aver-

age value, it is calculated from 10 polled values ev-

ery minute. The average value is then compared with

the threshold (20 Newtons) to identify the chair sta-

tus. The pressure sensor node wirelessly sends chair

status packets with a single value (TRUE if it detects

chair occupy; FALSE otherwise) to the Base Station.

Figure 2: Pressure Sensor Data.

3.1.2 PIR Motion Sensor

The Passive InfraRed sensor detects the motion of

users inside the ofﬁce. The sensor uses the Perkin

Elmer Optoelectronics



LHI878 sensor, Figure 4(c).

The LHI878 pyroelectric infrared-detector series are

the standard dual element design recommended for

all variants of motion control. PIR sensor data pro-

vides indication of whether the user is likely to be in

the ofﬁce, especially when all other sensors do not de-

tect any activity. Different from pressure information,

with which average value reﬂects the pressure during

the polled period, user movement only triggers PIR

sensor for short time intervals. Figure 3 illustrates a

10-minute reading (from minute 45 to minute 55) of

PIR data. PIR value is measured in analog-to-digital

converter (ADC) unit. When there is no movement

inside the ﬁeld of view, the used PIR sensor returns

a small value that stably is either one, two or three

ADC. Movement is detected when PIR is triggered

and it returns a value that is also stably at around

2840 ADC. Based on these experimental results we

use 2800 ADC as the threshold for detecting motion

of user inside the ofﬁce. After the polled period, max-

imal PIR value is taken, instead of calculating an av-

erage value, as the key value to compare with the

threshold of 2800 ADC. The PIR sensor also sends

BEYOND INDOOR PRESENCE MONITORING WITH SIMPLE SENSORS

a single-value status (TRUE or FALSE) packet to the

base station each minute.

Figure 3: PIR Sensor Data.

(a) Advantic TelosB mote

(b) Tekscan



A201-100 (c) PIR and Acoustic

Figure 4: Advantic Sensor. Retrieved November 11 2011

from http://www.advanticsys.com/.

3.1.3 Acoustic Sensor

The SE1000 acoustic sensor provided by Advantic

Systems goes on the attachable sensor board is a mini-

microphone (20 16000 Hz, SNR 58 dB), Figure 4(c),

capturing ambient audio information. It is designed

to detect the presence/absence of sound.

Different from PIR or pressure data, with which

we are able to set an absolute value as a thresh-

old to distinguish occupancy states, the changes in

sound level (decibel) depend on ofﬁces and settings.

For example, background noise in different ofﬁces or

at different time of the day is different. Therefore,

the threshold value can not be given as an absolute

value. However, monitored sounds (keyboard typing,

mouse clicking, person speaking) make a signiﬁcant

change in sound level in comparison with ambient

background noise. For this reason, before ﬁnal de-

ployment, one runs a training phase to establish the

difference in decibel level of acoustic data between

two cases. Acoustic data is sampled at 1 MHz provid-

ing microsecond time resolution needed, in turn, for

capturing the appearance of expected sounds. Then,

average value of 1-minute polling data is used as deci-

bel value of the background noise, then again, the

maximal value of polled data is compared with this

ambient noise’s level to check the appearance of mon-

itored sounds.

For sensing keyboard/mouse sounds, we place the

acoustic sensor within 30 centimeters from the key-

board and mouse, as shown in Figure 6(b), another

acoustic sensor is placed on the meeting table to sense

human voice. In case of keyboard typing and/or

mouse clicking, data from early experiments shows

that when keyboard is being typed and/or mouse is

being clicked the change in decibel level is greater

than 20 dB. In “person speaking” case, the differ-

ence between background noise and human voice is

even bigger, the subtraction of average value from

maximal one leaves a value greater than 30 dB. Fig-

ure 5 shows results from some of our experiments dur-

ing training phase, illustrating 1-minute polling. Fig-

ure 5(a) exempliﬁes the result from Key/Mouse us-

age while Figure 5(b) represents result from Meet-

ing table when person’s speaking sound is present.

From the results during the training phase, we set the

value of 20 dB, getting from the subtraction of aver-

age value from maximal one as the threshold for both

keyboard/mouse acoustic sensor at the working table

and human voice acoustic sensor at the meeting table.

3.2 Experimental Setup and Results

Using the layout of Figure 6, the sensors just de-

scribed and the classiﬁcation algorithm presented in

Section 2, we perform an experiment lasting 5 days,

that is, daily from 9:00 AM to 18:00 PM, from August

22 to 26, 2011 to verify the accuracy of the proposed

approach in terms of activity classiﬁcation. During

the experiment, the user takes accurate notes of ac-

tual activities happening in the ofﬁce every ﬁve min-

utes, which is used as golden truth for the evaluation.

Table 4 shows the real occurrences of activities for

each minute of each activity. As can be seen, the user

spends most of his/her time Working with PC, 1350

out of 2700 minutes, followed by Working without PC

with 595 minutes over the experiment. Another 180

PECCS 2012 - International Conference on Pervasive and Embedded Computing and Communication Systems

(a) Keyboard/Mouse Acoustic Data

(b) Human Voice Acoustic Data

Figure 5: Acoustic Data.

(a) Overview of the instrumented room

(b) Acoustic Sensors’ Placement (c) Pressure Sensor Placement

Figure 6: The instrumented room.

minutes during one working week is spent for Having

a meeting activity while Presence and Absence activ-

Table 4: Golden Truth of activities’ happening time.

Activity Working

with

Working

without

Having

meeting

Presence Absence

Happening

time (min.)

1350 595 180 265 310

Total (min.) 2700

ities take 265 and 310 minutes, respectively.

To be more precise, we adapt to our case the follow-

ing performance measures. Given an activity A, we

measure accuracy in the following way:

• FP (False Positive): the number of happening

minutes of other activities recognized incorrectly

as A.

• FN (False Negative): the number of happening

minutes of A recognized falsely as not A.

• TP (True Positive): the number of happening min-

utes of A recognized truly as A.

• Recall: the proportion of the time correctly recog-

nized as A against the real happening time of A.

Also called “True Positive Rate” - the percentage

of positives the system recognizes correctly.

BEYOND INDOOR PRESENCE MONITORING WITH SIMPLE SENSORS

Recall =

T P

T P + FN

(1)

• Precision: the rate of the real happening time of A

over all the time recognized as A.

Precision =

T P

T P + FP

(2)

The overall success rate of the system is computed

based on the following formula:

success rate =



1 −

∑

∀activities

(FP + FN)

2700



(3)

in which,

∑

∀activities

(FP + FN) is the total minutes of

wrong recognition. 2700 is the number of minutes of

total experimental period.

Our system makes a classiﬁcation every minute.

The results of our experiment are presented in Ta-

ble 5. The results for TP of Working with PC is 1350

out of 270 minutes. The FN is kept to 0 minutes,

which leads to the perfect Recall of 100% while the

Precision is 96.84% caused by 44 minutes of the FP.

Our system recognize 565 minutes for Working with-

out PC activity with 30 minutes of FN and the FP is

0, resulting in 94.96% of Recall and 100% of Preci-

sion. The next row in the table depicts the atributes

of Having a meeting activity. 93.57% is the Preci-

sion of the system as recognized happening time is

192 minutes while the real one is 180 minutes. As

a consequence, the system recognizes mistakenly 12

minutes more than the total happening time of this ac-

tivity, reﬂected on FP is 12, FN is 0, and Recall for the

one is 100%. The Recall of the system in recogniz-

ing Presence and Absence activities are 94.34% and

96.45%, respectively. The FN values express that 15

minutes for Presence and 11 minutes for Absence are

recognized incorrectly as the activities. However, the

Precision for both of them is 100%.

Table 5: Experimental Results.

Activity FN

(min.)

Recall

(%)

Precision

(%)

Working with PC 0 44 1350 100 96.84

Working without PC 30 0 565 94.96 100

Having a meeting 0 12 180 100 93.75

Presence 15 0 250 94.34 100

Absence 11 0 299 96.45 100

3.3 Discussion

By analyzing raw result data, we ﬁnd out that 44-

minute FP of “Working with PC” is taken from FNs

of “Working without PC” and “Presence” activities.

While 12-minute FP of “Having a meeting” is taken

from FNs of “Presence” and “Absence” activities.

This is caused by inaccurate acoustic data decisions.

At some moments some louder sound appears causing

the subtraction from the average value and thus brin-

ing the maximum value above the 20dB threshold,

thus the acoustic sensors returns TRUE. As a conse-

quence, the system indicates the activity as “Working

with PC” or “Having a meeting”.

The error may cause unnecessary energy usage

by increasing the PC-related or meeting-related load

(e.g. LCD monitor or lights are not turned off). Mean-

while, this results in the Recall of “Working with

PC” and “Having a meeting” activities is 100%, mak-

ing sure that PC/meeting-related devices are working

probably while the user is really working with the PC

or having a meeting. These satisfy one of the impor-

tant criterion of a smart building that is the user com-

fort has higher priority than energy saving.

4 RELATED WORK

In recent years, many research efforts have been car-

ried out to design smart building. The advent of

low-cost wireless sensor networks has enabled wider

deployment opportunities of a large number of con-

nected sensors thus allowing for improved sensing in

buildings. We shall review here some of the most rel-

evant previous works on using activity recognition as

a driver for control of smart building with respect to

energy-aware perspective.

Prior researches in HVAC control systems show

that occupancy information can be used to drive a

more optimized HVAC schedule. In (Erickson and

Cerpa, 2010) or (Agarwal et al., 2010), the authors

propose a demand response HVAC control strategy

that uses real-time occupancy monitoring with oc-

cupancy prediction to achieve efﬁcient conditioning.

Another example is (Newsham and Birt, 2010), where

the ARIMAX model is developed to forecast the

power demand of the building in which a measure of

building occupancy is a signiﬁcant independent vari-

able and increased the model accuracy. However, due

to the difﬁculty in obtaining real-time accurate occu-

pancy data, many of these techniques focus on us-

ing pre-determined schedules. Furthermore, these re-

searches only try to use occupancy data, far less than

the details of user activity.

In the context of Artemis SOFIA Project (SOFIA,

2009), the authors in (Niezen et al., 2010) provide a

way for users to physically interact with devices, ex-

pressing themselves. Through developing meaning-

ful interaction devices their system can predict to un-

PECCS 2012 - International Conference on Pervasive and Embedded Computing and Communication Systems

derstand what the user is trying to accomplish, never-

theless user needs to perform necessary actions. Our

approach differs in trying to recognize user’s activity

automatically.

Simple sensors are used in many modern build-

ings. For instance, Passive InfraRed (PIR) based

sensors are often used (especially with lighting sys-

tem) for occupancy detection. The sensors are con-

nected directly to local lighting ﬁxtures. These PIR

sensors are also simple movement sensors and of-

ten cannot actually determine if the room is occupied

or not. Thus, most of them use a timeout for shut-

ting off the lights (30 minutes is common) which can

be sub-optimal. (Padmanabh et al., 2009) investi-

gate the use of microphones and PIR sensors for the

efﬁcient scheduling of conference rooms. (Delaney

et al., 2009) use PIR based wireless occupancy sen-

sors to measure wasted energy in lighting even when

there are no occupants. In the AIM Project (Barbato

et al., 2009), authors suggest to measure some phys-

ical parameters like temperature and light as well as

user presence based on PIR sensors in each room of

a house. (Gao and Whitehouse, 2009) seek to use

coarse occupancy data (leave home, return home) to

drive a self-programming home thermostat; however

the focus is on the thermostat self-programming al-

gorithms, and not on obtaining accurate occupancy.

(Wang S, 1998) examine CO2-based occupancy de-

tection yet it is very slow to detect events such as

incoming people. These efforts, however, neither

use occupancy information to drive actual systems

nor evaluate accuracy of their detection sensor, and

are rarely used for intelligent energy management in

buildings. Our system uses the same simple sensors

but provides far more detailed information on the ac-

tivity of the users.

More advanced systems, such as using sonar-

based sensing (Tarzia et al., 2009) or cameras and vi-

sion algorithms (Teixeira and Savvides, 2008) have

been presented, though they suffer from deployabil-

ity, cost and privacy issues. SCOPES (Kamthe et al.,

2009) is an occupancy monitoring system that detects

near real-time occupant movement between rooms

with an accuracy of 80%. (Erickson et al., 2009) pro-

pose a wireless network of cameras (which have the

aforementioned privacy and cost issues) to determine

real-time occupancy across a larger area in a building,

focusing more towards coarse-grained ﬂoor-level oc-

cupancy detection. In (Singhvi et al., 2005), the occu-

pants are equipped with sensor badges, with which it

is possible to achieve relatively accurate localization

using, for example, RFID tags. In SPOTLIGHT (Kim

et al., 2008), the authors present a prototype system

that can monitor energy consumption by individuals

using a proximity sensor. While the authors tackle

the right challenge, the system either requires users

to carry active RFID tags or to explicitly tap tags on

RFID readers, which is cumbersome. In contrast, we

use simple binary sensors.

In summary, most related work either uses rich

sensors (e.g. cameras, RFID tags, wearable sensors)

or simple sensors but providing coarse-grained occu-

pancy information rather than detailed activities into

appliance control strategies. In this paper, our system

provides a ﬁner grained activity recognition solution

with simple sensors exhibiting very good classiﬁca-

tion results.

5 CONCLUSIONS

We believe that one of the key inputs for BAS is de-

tailed and precise user activity information that, in

turn, can drive the control of buildings and decrease

energy consumption while preserving the user com-

fort. Our proposed activity recognition approach is

useful foraccurate activity recognition at the level of

individual ofﬁces. By using low-cost, binary, and

wireless sensors we are able to recognize ﬁve ofﬁce

activities for one person in a standard ofﬁce room.

Through initial experiments, we show that the so-

lution can recognize user activity accurately with a

success rate of 94.81%. More importantly, false-

negatives (which may lead to discomfort) are kept

perfectively at 0%, satisfying user expectations at

work. These promising results suggest to investi-

gate further the ofﬁce activity recognition with sim-

ple sensors. In particular, it is important to enrich

the set of recognized activities, possibly using other

types of simple sensors. It is also essential to con-

sider multi-user in one room and multi-room situa-

tions. Finally, we plan to couple approaches, as the

one presented here, with actual building control, to

measure the amount of actually saved energy and test

the resulting user experience, this in the context of the

GreenerBuildings project.

ACKNOWLEDGEMENTS

We thank Peter Kamphuis, Jeroen Jager and Dim-

itri de Jong for providing data on the survey re-

ported in Section 2. We also thank Sharique Arshi

for useful discussions on acoustic sensing. Tuan Anh

Nguyen is supported by the Vietnam International Ed-

ucation Development program (VIED). The work is

supported by the EU FP7 Project GreenerBuildings,

contract no. 258888 and the Dutch National Research

BEYOND INDOOR PRESENCE MONITORING WITH SIMPLE SENSORS

Council under the NWO Smart Energy Systems pro-

gram, contract no. 647.000.004.

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PECCS 2012 - International Conference on Pervasive and Embedded Computing and Communication Systems