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Abstract—This work proposes the development of a sociometric
sensor capable of detecting proximity, movement and verbal
interaction between people. The sensor will allow measuring the
level of existing interrelations between the collaborators of an
organization, in order to determine and to measure the
organizational climate. The way to evaluate the current
organizational climate is subjective, through surveys that are not
accurate and that make the results are not adequate. The sensors
developed until now focus on proximity or movement, but not on
the 3 proposed variables: movement, proximity and speech. The
proposed portable device consists of a proximity detector using
RSSI (Received Signal Strength Indicator) based on the
NodeMCU WIFI ESP 8266 module. It also has a motion detector
that uses an accelerometer and finally a voice detection algorithm,
acquired via microphone, aimed at determining verbal interaction
between people. The validation of the equipment was done by
measuring the 3 variables in a sample of 50 people. The results
show a percentage of proximity detection of 90% up to a distance
of 3 meters between individuals, a percentage of success of 92.5%
in the detection of posture and physical activity and a percentage
of success of 87.5% in the detection of verbal interaction between
people.
Keywords- sociometric sensor, RSSI, voice detection, motion
detection, proximity detection, organizational climate.
I. INTRODUCTION
The organizational climate is defined as a collective
emotional state that is determined by the perceptions that
people have about the organizations in which they work and, in
turn, these perceptions depend on the interactions that occur
between the members of the organization and the activities that
take place there. A variable that evaluates the organizational
climate is the number of interactions that exist among the
members of the organization.
Globally, organizations are betting on organizational
climate assessment, as there is a relationship between
organizational climate and performance. For example, in Peru,
there are at least 27 human management consulting firms that
provide organizational climate measurement services.
However, none of these services have technological devices to
measure interactions in workspaces. Therefore, the imprecision
of current methodologies for the evaluation of the study of the
work environment, because they are based on the subjectivity
of the specialist at the time of taking surveys and focuses mainly
on observable experiences. Data collected in surveys and other
organizational climate assessment methods are subject to the
veracity of the responses and the collaboration of the person
being assessed. Therefore, the results obtained from the
psychological assessment of the organizational climate are not
absolutely accurate and may be biased by the subjective nature
of the data.[1].
A solution proposal has been made at the commercial level
of products such as Humanyze that was created by students of
MIT (Massachusetts Institute of Technology), which offers
companies to analyze social interaction data for organizational
climate, business optimization processes and work strategy.
However, this type of product does not exist in Latin America
and is not customized for the research area of verbal interactions
On the other hand, scientific literature has made proposals
such as those described below.
Daniel Olguín et al. present in [2] the design,
implementation of a device that measures and analyzes human
behavior. They propose the use of electronic modules capable
of measuring face-to-face interaction, conversation time,
physical distance from other people and motor activity in order
to measure individual and collective behavior. In addition, his
proposal was submitted to an experiment in the Marketing area
of a company for a period of one month, in which data was
collected for 2200 hours. Each employee was instructed to use
them during the workday and that they were subject to a
psychological study so that there was no violation of privacy.
The experiment yielded results of how employees on the same
floor related to other floors. On the other hand, this design has
a final weight of 110 grams, which is greater than the proposal
put forward.
On the other hand, Oscar Mozos et al. presents in [3] an
article that aims to detect stress-related behaviors, analyzing
data that are provided by a non-invasive sensor. In this case, a
portable sensor is used to record electrodermal activity (EDA)
or a photopletismograma (PPG), which are sensors of brain
activity in real time. In addition, a sociometric sensor is used to
measure social activity including movement of the person and
voice. It can be concluded, based on this article, that the
development of sociometric sensors is very useful for
psychologists because the data are measurable making
subsequent evaluations more reliable and above all of higher
quality. However, this sensor does not contain a proximity
measurement which does not allow us to know how sociable
one person is with another.
Trinh Do et al. present in [4] an analysis of the social
activities through bluetooth and infrared sensors of 50 people,
which used the sociometric sensor for 6 weeks. The article
makes 3 important contributions; first they create an algorithm
to manage in space-time context the social interactions with the
Bluetooth and IR sensor data. Second, an automatic method for
defining activities is introduced. And, finally, the article
Jorge Tuesta1, Demetrio Albornoz2, Guillermo Kemper3and Carlos A. Almenara4
1,2,3 Faculty of Engineering - School of Electronic Engineering
4Faculty of Health Sciences
Universidad Peruana de Ciencias Aplicadas, Lima, Peru
1u201412611@upc.edu.pe, 2u201212724@upc.edu.pe, 3pcelgkem@upc.edu.pe, 4carlos.almenara@upc.pe
A Sociometric Sensor Based on Proximity,
Movement and Verbal Interaction Detection
216
2019 International Conference on Information Systems and Computer Science (INCISCOS)
978-1-7281-5581-4/19/$31.00 ©2019 IEEE
DOI 10.1109/INCISCOS49368.2019.00042
Authorized licensed use limited to: Auckland University of Technology. Downloaded on May 23,2020 at 04:07:16 UTC from IEEE Xplore. Restrictions apply.
presents a case study of interactions in a real organization in
which they give real details of the quality of the sensors and that
precision can reach the recognition of activities. The
presentation of the information is rescued from this article by
means of interaction networks in people sensed by Bluetooth
and infrared sensors. The final product does not include a voice
activity register, which is necessary, since the most common
interaction between people is verbal.
Oren Lederman et al. present in [5] a prototype of badge that
records social interaction through the RSSI of Bluetooth and a
microphone to detect if the person interacts with speech. In
addition, they also developed software for phones where they
try to reduce the cost of hardware by making the sociometric
sensor over the phone. This prototype is very small and has
duration of 40 hours. However, it does not have an
accelerometer to capture the person's physical activity, nor an
SD module to store the information.
Jun Watanabe et al. presents in [6] an approach to the study
of the work environment with a sociometric sensor in a call-
center company, which demonstrated, through the data
recorded by the sociometric sensors, that the level of activity
during working hours is not correlated with the performance of
the work team. They also came to the conclusion that face-to-
face interaction leads to a high level of activities, much more
than teamwork. The authors made use of a sociometric sensor
and an IR (infrared) module to capture the signals of social
interaction and thus be able to make the analysis of
organizational climate. However, this proposal consists of two
separate modules, so this depends more on the user's use.
Based on the above, this paper proposes a sociometric
equipment weighing less than 100 grams, capable of jointly
detecting the level of proximity, verbal interaction and motor
activity. The acquired information is stored in a microSD
memory for later offline analysis. In addition, an important
contribution is the exchange of the Bluetooth module for a WIFI
module, which offers greater range and the possibility of easily
connecting to a base station.
The parts that make up the proposed sensor, as well as the
validation and results obtained are described in the following
sections.
II. DESCRIPCIÓN DEL MÉTODO PROPUESTO
The good organizational climate generates positive
consequences; the main ones are productivity, integration,
talent retention and a positive image of the company among
others.
The study of the organizational climate provides the
specialist psychologist, information to detect the origin of
problems such as maladjustment, absenteeism, low productivity
and discomfort at work among others.
The sociometric sensor will acquire information on the
proximity between the individuals evaluated, the motor activity
in the work schedule, the verbal interaction of each one of them
and the time in which each variable is recorded. Therefore, the
sensor will provide the specialist with very reliable data to
improve his interpretation of the organizational climate and
take the corresponding corrective actions.
Fig. 1 shows a sociometric sensor acquiring social
interaction data in a work area. Note that each person must
previously authorize the use of the sensor (for privacy reasons)
in order to be able to install it preferably hanging from the neck
and attached to the chest.
Fig. 1. Final product working in the application scenario.
Fig. 2 shows the block diagram of the proposed sensor. It is
important to note that the central processor of the sociometric
sensor is a WIFI NodeMCU ESP8266 module that has a clock
frequency of 80/160 and a Tensilica Xtensa LX3
CPU. The CPU is 32-bit and ultra-low power [7]. This last one
was very taken into account for the choice of the processor since
the sociometric sensor is portable and therefore the
consumption of energy must be very controlled.
The details of the parts that make up the proposed sensor
will be described in the following sections.
Fig. 2. Block diagram of the proposed system.
A.
Proximity data acquisition.
ESP8266 WIFI NodeMCU module enables easier collection
of proximity data and saves physical space because it includes
a WIFI device and a sufficiently fast processor on the
development board.
Proximity is analyzed by obtaining the RSSI that exists
between the WIFI modules that each sociometric sensor will
possess. The RSSI (Relative Received Signal Strength) is an
indicator of the perceived power in a receiver device [8]. The
RSSI unit is the decibel-milivatio () which is calculated
using the following expression:
=10×log()
(1)
Where is received power expressed in milliwatts ().
Theoretically the value of RSSI in at a distance of
meters is calculated as follows [8]:
=−10log+
(2)
Where is the value of RSSI in in the receiver at a
distance of one meter and the loss constant.
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In a real environment, the level of RSSI detected among
sociometric sensor is affected by obstacles in the work area. It
is for this reason that many times the loss constant must be
measured empirically.
Then the distance between the sensors can be calculated
using the following expression [9].
=10
(3)
To map RSSI values to distance values between devices,
tests were performed by acquiring RSSI data every 10 seconds
so as not to saturate the memory. The acquisition was done at
different distances and the values were recorded as shown in
Table I. The distances used were 0.5m, 1.5m, 2.5m and 3.5m.
This is because an interaction is positive for a distance threshold
of 1.5m.
Table II shows the average values resulting from the RSSI
samples received for each distance evaluated. A graph of the
values in Table II is shown in Fig. 3.
TABLE I. TABLE OF SAMPLES RSSI
Samples/Distance
1
2
3
4
5
6
0.5 m
-49
-50
-48
-51
-50
-51
1.5 m
-53
-53
-53
-54
-53
-52
2.5 m
-58
-58
-57
-56
-55
-57
3.5 m
-61
-62
-60
-62
-61
-60
TABLE II. AVERAGE OF RSSI SAMPLES
Distance (m)
Average
0.5
-50
1.5
-53.1
2.5
-56.6
3.5
-61.1
B.
Acquisition of movement data.
The acquisition of motion data is obtained through the
MPU6050 module which is an IMU (Inertial Measurement
Units) with accelerometer and gyro that acquires the change of
speed in the 3 axes [10] in order to detect the degree of activity
possessed by the user of the sociometric sensor. The data are
acquired every 1 second with 16 bits of precision, so as not to
saturate the available memory space.
Fig. 3. RSSI vs Distance Graphic.
The flowchart of the motion data acquisition process is
shown in Fig. 4. The data is acquired by the central processor
of the development board and then stored in text format in SD
memory. In this case the information provided by RTC (Real-
Time Clock) and the accelerometer of the MPU6050 module is
stored.
Fig. 4. Flowchart for acquisition and recording of motion data.
Fig. 5 shows the data obtained from the accelerometer. It
can be seen that in column A is the time register and in column
B the accelerometer data. A movement is considered to have
existed when the values of the accelerometer are much higher
than those recorded at rest, as can be seen in rows 11 and 12.
Fig. 5. Information provided by the accelerometer.
C.
Verbal Interaction Data Acquisition
For voice activity detection (VAD), the MAX9814 module
was used, which includes an omnidirectional microphone and
a low noise amplifier [11].
The microphone of the module was adapted to ensure
unidirectionality and avoid interference from signals that do not
-80
-60
-40
-20
0
0.5 1.5 2.5 3.5
RSSI
DIstance (meters)
RSSI
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correspond to voice signals emitted by the user carrying the
sensor.
In the acquisition, a window of 50 ms was considered for
the taking of each sample sent by the voice module. These
samples were digitized by an ADC of 12 bits of precision. Then
a block of 18 samples is formed which is processed for the
decision of presence or no voice activity, which will be
reflected visually every second with the following words: VAD
(presence of voice activity) or NO VAD (no presence of voice
activity). Two metrics were used for this processing: The short
term average energy and zero cross rate of each of the 18
samples. Fig. 6 shows the flowchart for voice activity detection.
The calculation of the short term quadratic energy [11]
applied to the 18 samples per block is expressed as:
()=
()
=0,1,2…..
(4)
Where is the block index, () is the short term average
energy of the block , () is the acquired voice signal and is
the block size. In this case we have = 18.
The number of zero crosses refers to the number of times
voice samples change sign in a given time [12]:
()= ()−((−1))
2
(5)
Where 1() is the zero cross rate of the block and
() the function sign.
Fig. 6. Verbal Interaction Detection Algorithm Flowchart.
From experimental tests carried out in a working
environment, it can be verified that an energy value greater than
0.2 and the presence of at least one crossing by zero is a
sufficient condition to determine the presence of voice activity
(VAD). Whatever other conditions are present, it will be
considered as no voice activity (NO VAD).
A graph of the detection for VAD and NO VAD over time
is shown in Fig. 7.
Fig. 7. Speech Detection Differentiation Graph.
D.
Data processing and recording.
The algorithms shown in the flow diagrams for proximity
detection, motor activity detection, verbal interaction detection
and data logging are executed using the CPU of the WIFI
NodeMCU module ESP8266.
Data logging is achieved using the DS3231 RTC module
and the microSD card adapter. The first module is used to
organize information with date and time in real time when the
data are acquired. The second module stores proximity,
movement and speech information in a text file that is later
visualized by the specialist psychologist.
III. RESULTS
For the validation of the sensor, a comparison was made of
real data and data sent by the proposed sensor for 40 cases of
possible interaction. The comparison was divided into 3 stages:
register of motor activity, register of voice activity and register
of proximity.
Motor activity detection: These results reflect an
individual's activity during working hours. That is to say the
time that remains seated, stopped or moving. The sensor in this
case detects whether or not there is motor activity. The
percentage of error and success obtained is indicated below:
- Number of cases: 40
- Number of hits: 37
- Number of errors: 3
% =
100
(6)
Where % is the motion detection error
With the values obtained in the tests of register of motor
activity it was obtained:
% = 3
40× 100 = 7.5 %
(7)
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Then the percentage of success in detecting movement was
92.5 %. The errors presented are mainly due to the sampling
times of the information sent by the sensor (1s), i.e. fast
movements are often not detected.
Voice activity recording: These results represent the verbal
activity of an individual with another individual. The
percentage of error and success in the detection of speech was
obtained.
- Number of cases: 40
- Number of hits: 35
- Number of errors: 5
The value of the error percentage in voice detection %
is also obtained from (6):
= 5
40× 100 = 12.5 %
(8)
The value of the percentage of success in speech detection
in this case was 87.5 %. As in the case of motion detection, the
adjustments of the sampling times of the sensor information
generate some detection errors.
Proximity recording: These results represent the proximity
of one individual to another. It was obtained the percentage of
error that the sensor has in the proximity metric.
- Number of cases: 20
- Number of hits: 18
- Number of errors: 2
The value of the percentage error in proximity detection
% is also obtained from (9):
= 2
20× 100 = 10 %
(9)
The percentage of success in proximity detection in this case
is 90%. Proximity errors are mainly due to the presence of
obstacles that alter the signal reception level (RSSI) between
devices.
Among the 40 cases analyzed, 3 circumstances of data
acquisition for the sociometric sensor were established.
Case 1: Possible Interaction, Voice Recording and Close
Proximity:
Fig. 8 shows the data acquired by the sociometric sensor.
Fig. 8. Data in .txt file provided by the sociometric sensor for
case 1.
Case 2: No Interaction, no voice recording and proximity
greater than 1.5 meters:
Fig. 9 shows the data acquired for this case by the
sociometric sensor.
Fig. 9. Data in .txt file provided by the sociometric sensor for case 2.
Case 3: Possibility of the user speaking alone, voice logging
and no one near.
Fig. 10 shows the data acquired for this case by the
sociometric sensor.
Fig. 10. Data in .txt file provided by the sociometric sensor for case 3.
These three cases are the most common ones recorded in a
day-to-day assessment of the organizational climate. The main
contribution of this work is focused on the coupling of the 3
measurement variables for a more complete evaluation of the
organizational climate; in addition to providing a good success
rate for such evaluation.
CONCLUSIONS
In this work it was possible to develop a sociometric sensor
capable of providing additional reliable information for the
decision of the specialist psychologist.
The area of application of organizational climate evaluation
can affect, however, in the percentage of success of the
proximity detection.
Excessive noise in the evaluation application area could
affect the acquisition of voice activity data.
As a future improvement, other hardware modules that
improve the current success rate will be evaluated.
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