Measurement of the Reaction Time in the 30-S Chair Stand Test
using the Accelerometer Sensor Available in off-the-Shelf Mobile
Devices
Ivan Miguel Pires
1,2,3
, Diogo Marques
4
, Nuno Pombo
1,3,5
, Nuno M. Garcia
1,3,5
, Mário C. Marques
4,6
and Francisco Flórez-Revuelta
7
1
Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal
2
Altranportugal, Lisbon, Portugal
3
ALLab - Assisted Living Computing and Telecommunications Laboratory, Computing Science Department,
Universidade da Beira Interior, Covilhã, Portugal
4
Department of Sports Sciences, Universidade da Beira Interior, Covilhã, Portugal
5
Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal
6
Research Centre in Sport, Health and Human Development, Covilhã, Portugal
7
Department of Computer Technology, Universidad de Alicante, Alicante, Spain
Keywords: Reaction Time, Accelerometer, Mobile Devices, 30-S Chair Stand Test, Elderly People.
Abstract: The 30-s Chair Stand Test (CST) is commonly used with elderly people for assessing the lower limbs
strength, which can provide sufficient information regarding the general mobility and fall risk. The mobile
devices are widely used for the acquisition of the different physical and physiological data from the sensors
available, including the accelerometer. In this way, the aim of the present study consisted on the
development of an automatic method for the measurement of the reaction time (RT) based on the 30-s CST
using a mobile device. Besides that, the data acquisition through an accelerometer allows the assessment of
different variables, such as the maximum values of the acceleration, the instant velocity, the maximum force
and the peak power, that may contribute to a better understanding of the physical demands during the 30-s
CST performance. The results presented in this study demonstrated that the calculation of the RT and the
different variables during the 30-s CST performance is possible, opening new possibilities for the
development of scientific projects, namely those that encompasses the motor and cognitive training of
elderly people.
1 INTRODUCTION
The ageing process involves several transformations
within the human body, including a decline of the
skeletal muscle tissue, described as sarcopenia
(Walston, 2012), and a decrease in the cognitive
function. Thus, contributing to a slower processing
speed and motor performance (Bautmans et al.,
2011), and an increased reaction time (RT), both in
elderly people with and without cognitive
impairments (Arnold et al., 2015).
According to Wong, Haith, and Krakauer (2015)
the RT means the time from the stimulus onset to the
beginning of the motor action. It includes the time to
capture the stimulus, process the information and
initiate a motor response (Bautmans et al., 2011). It
is current practice to evaluate the RT through a
visual and/or an acoustic stimulus either from a
computer software (Jain, Bansal, Kumar, & Singh,
2015) or mobile devices (Mulligan, Arsintescu, &
Flynn-Evans, 2016). The latter are equipped with
several sensors, e.g., Global Positioning System
(GPS) receiver, gyroscope, accelerometer,
magnetometer, and microphone, which enable the
acquisition of different types of data, including those
related to physical activities.
Following previous work regarding the
development of methods for the acquisition of
physical and physiological parameters using off-the-
shelf mobile devices, a method for the measurement
of the jump flight time was previously developed by
Pires, Garcia, and Canavarro Teixeira (2015), as
well as a method for the recognition of Activities of
Pires, I., Marques, D., Pombo, N., Garcia, N., Marques, M. and Flórez-Revuelta, F.
Measurement of the Reaction Time in the 30-S Chair Stand Test using the Accelerometer Sensor Available in off-the-Shelf Mobile Devices.
DOI: 10.5220/0006813102930298
In Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2018), pages 293-298
ISBN: 978-989-758-299-8
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
293
Daily Living (ADL) and their environments (I. Pires,
N. Garcia, N. Pombo, & F. Flórez-Revuelta, 2016;
Pires, Garcia, & Flórez-Revuelta, 2015; I. M. Pires,
N. M. Garcia, N. Pombo, & F. Flórez-Revuelta,
2016).
The development of systems aimed to
monitoring the lifestyle is an emerging topic,
included in the Ambient Assisted Living (AAL) and
Enhanced Living Environments (ELE) systems
(Botia, Villa, & Palma, 2012; Dobre,
Mavromoustakis, Garcia, Goleva, & Mastorakis,
2016; Garcia, Rodrigues, Elias, & Dias, 2014).
The evaluation of the physical fitness in elderly
people is usually done through the Senior Fitness
Test (SFT), which is a battery of 7 tests developed
by Rikli and Jones (2001). One of the tests, the 30-s
Chair Stand Test (CST) is used to assess the lower
body strength, enabling also to detect changes in
mobility and fall risk (Pereira et al., 2012). In the
literature, there are several studies that used the
accelerometer in the 30-s CST to assess the physical
condition of elderly people (Millor et al., 2017;
Regterschot et al., 2014). . The accelerometer sensor
allows the measurement of different real-life
situations and includes the concept of the
development of a personal digital life coach, which
can be adapted to monitoring the physical activity of
elderly people (Garcia, 2016). This sensor have been
successfully implemented in different tests and age
groups, like the Time-Up and Go (Reis, Felizardo,
Pombo, and Garcia, 2016) and the Heel-Rise Test
(Pires, Andrade, Garcia, Crisóstomo, and Florez-
Revuelta, 2015).
Nevertheless, to the best of our knowledge, the
measurement of the RT in the 30-s CST using the
accelerometer sensor available in off-the-shelf
mobile devices is not yet implemented. Thus, the
aim of our study is to develop an automatic method
to measure the RT based on an acoustic stimulus in
elderly subjects, performing the 30-s CST. Besides
that, it is our intention to validate this protocol for a
future use on studies related to the effects of high-
speed resistance training on the velocity of the body
movement and the RT in elderly people.
This paragraph ends the introductory section.
Section 2 presents the methodology of this study.
The results are shown in the section 3 and discussed
in section 4. Finally, the conclusions are presented in
section 5.
2 METHODS
2.1 Study Design
For the measurement of the RT in the 30-s CST, we
developed an application for mobile phones that
acquires the accelerometer data during the
movement. A subject (68kg; 1.70m) volunteered to
be assessed on the 30-s CST using the mobile device
in the front pocket of the jeans for further calculation
of the RT. The subject repeated the test 3 times.
It is important to note that in future experimental
studies, this test protocol will be applied with elderly
people.
The present study is structured in three stages:
1. The development of a mobile application
for capturing the sensors' signal, with the
transmission of an acoustic signal (beep)
to initiate the 30-s CST.
2. The implementation of the 30-s CST to
evaluate the RT and the respective ability
based on an acoustic signal.
3. The analysis of different variables (i.e.,
velocity, force, power) based on the
accelerometer data.
2.2 Description of the Modified 30-S
CST
This test was originally designed by Jones, Rikli,
and Beam (1999). The test procedure consists in the
following:
In a sit position on a chair (next to a wall) with
the back straight and the arms crossed over the
chest, after a verbal permission (“1, 2, 3, go”), the
subjects had to stand up and sit down from a chair as
many times as possible within 30 s. In the present
study, the modification consisted in the introduction
of an acoustic signal (a beep), instead of a verbal
signal to initiate the test.
2.3 Data Acquisition
In the present study the off-the-shelf mobile device
used was a Huawei Y530-U00. This device has an
accelerometer sensor incorporated that acquires data
at 60 Hz frequency (Huawey, 2013). The data
acquisition was performed with a mobile
application, whose function is to save the outputs of
the accelerometer sensor into a text file for further
processing. The data were acquired with the off-the-
shelf mobile device placed in the front pocket of the
pants for the correct acquisition of the accelerometer
outputs.
HSP 2018 - Special Session on Healthy and Secure People
294
The data acquisition was started by pressing a
start button in the application with the subject sit and
not moving. After 10 s of the start, an acoustic signal
indicates that the subject should start the test and
data continues to be acquired for more 30 s.
2.4 Data Analysis
2.4.1 Data Processing
After the data acquisition process, the text files
stored in the mobile devices were processed in a
computer.
The accelerometer measures the three
components of the acceleration, but since it was not
possible to ensure that the device position remained
unchanged through the test duration, it was decided
to compute the total acceleration from the three
components.
In order to have the gravity acceleration value
when the subject is not moving, the following
correction was introduced (Bennett, Jafari, & Gans,
2014):



 (1)
where a
corr
is the corrected acceleration, a
meas
is the
acceleration measured, and a
ref
is a reference value
equal to the average value of the acceleration
measured in the 5 s previous to the acoustic signal
(i.e., starting of the test). This reference value was
needed since the signal presents small oscillations
even when there is no movement.
2.4.2 Calculation of the Sensor-based 30-S
CST Variables
After the data processing, and based on the
magnitude of the acceleration signal, the following
variables, presented in the Figure 1, were assessed:
1. Reaction Time (RT): the time between the
acoustic signal and the initiation of the
movement;
2. Movement Time (MT): the time from the
beginning of the motor action to the end of
the movement (i.e., return to the sit
position);
3. Total Time (TT): the time from the acoustic
signal to the return to the sit position.
In the present study the focus is on the RT
evaluation and therefore the analysis will be mostly
based only on the first stand up and sit down
movement of the several repetitive movements that
occur during the 30-s CST.
Figure 1: Signal Plot of the acceleration during the stand-
up phase from a sit position on the chair.
It is important to note that in future studies, for
example related to the influence of high-speed
resistance training programs on the RT in elderly
people, other variables can be assessed through the
accelerometer data, such as:
1. Maximum absolute acceleration;
2. Maximum absolute velocity
3. Mean velocity;
4. Maximum absolute Force;
5. Maximum absolute Peak power.
As a demonstration, we calculated those
variables, which can be seen in the results section.
The velocity was obtained by numerical
integration of the acceleration, for example using the
trapezoidal rule (Davis and Rabinowitz, 2007).

(2)
where V is the velocity, a is the acceleration, t is the
time, and subscripts 1 and 2 correspond to previous
and actual time, respectively. The force was
calculated with Newton’s second law

(Keller, 1987), where the relative acceleration is


 (Tao, 1967). The power is
simply (Tihanyi, Apor, and Fekete,
1982).
3 RESULTS
3.1 Prototype
The mobile application developed for this study has
an easily usage adapted for elderly people, designed
for different ages, as presented in the Figure 2. The
usage of the mobile application consists only in
pressuring a large button enabling and stopping the
data acquisition. At this stage of the project, the
mobile application only performs the data
acquisition of the accelerometer data, but, in the
Measurement of the Reaction Time in the 30-S Chair Stand Test using the Accelerometer Sensor Available in off-the-Shelf Mobile Devices
295
future, the calculation of the RT and the other
variables should be calculated at real time within the
mobile application. The files created by the mobile
application are stored in the mobile device for
further processing and validation.
Figure 2: Prototype of the mobile application developed.
3.2 Validation
The relative acceleration is presented in Figure 3.
The instant velocity is represented in the Figure 4.
The maximum force produced during the stand up
phase was also calculated, taking into account that
the weight of the subject was 68 kg (Figure 5).
Finally, the maximum peak power value for the first
instant was also determined (Figure 6).
Figure 3: Time-evolution of the relative acceleration.
The calculation of the proposed variables in this
study is proved possible with the data represented in
the Figures 1, 3, 4, 5 and 6. As example, the values
of the calculated variables were:
1. RT: 0.460 s;
2. TT: 2.881 s;
3. MT: 2.421 s;
4. Maximum acceleration: 13.66 m/s
2
;
5. Maximum velocity: 0.702 m/s;
6. Maximum Force: 929 N;
7. Maximum Peak Power: 495 W.
Figure 4: Time-evolution of the velocity.
Figure 5: Time-evolution of the force.
Figure 6: Time-evolution of the power.
4 DISCUSSION
Following the results highlighted in the previous
section, the RT of the subject used as an example
was 0.460 s. This value is higher than the RT values
referred in the literature (around 0.2 s, Wong et al.
(2015)). Two possible explanations can be stated: i)
in the present study an acoustic signal was used
instead of a visual stimulus; and ii) the 30-s CST
involves to stand up from a chair, which requires the
HSP 2018 - Special Session on Healthy and Secure People
296
overcoming of a much higher inertia than, for
example, the one of a rapidly touch of a button.
The maximum velocity (0.702 m/s) seems a
reasonable value by comparison with the value of
0.52 ± 0.13 m/s obtained by Regterschot et al.
(2014) with elderly people in the sit-to-stand test.
Moreover, the time-evolution of the velocity (Figure
4) clearly shows a pattern that should be expected
for a stand-up and sit-down movement. Namely, it is
seen a first peak with positive velocity
corresponding to the stand-up phase, followed by a
small plateau with velocity zero, corresponding to
the moment when the subject is standing vertically,
afterwards the velocity is negative, corresponding to
the sit-down phase. The process is continued with
the repetition of the stand-up and sit-down
movement.
The maximum peak power (495 W) is also in the
range of values reported Regterschot et al. (2014)
(423.4 ± 141.1 W) with elderly people.
The measurement of the RT and other variables
using the data acquired with the mobile device was
successful, although several constraints should be
mentioned, such as the reduced memory, low power
processing and low battery capabilities, as well as
the positioning of the mobile device during the data
acquisition process. In addition to the above-
mentioned limitations, the RT may also vary with
several conditions including healthy state, age and
physical condition (Bautmans et al., 2011).
Due to the lack of studies in the literature that
use the accelerometer sensors for the measurement
of the RT, the comparison of our method with others
is not possible. However, based on the present
results of this study and the study of Regterschot et
al. (2014), the use of the accelerometer data for the
measurement of several variables during the
performance on the 30-s CST seems reliable.
5 CONCLUSIONS
The present study highlights a novel approach on the
measurement of the RT in the 30-s CST. The results
obtained through an easy use mobile application can
be extrapolated to different research areas, including
the computer science and sports science, and studied
according to the main interests of the researchers.
The results of this study must be interpreted as
preliminary, since further validation is still needed
(like using different mobile devices and comparing
the values with other measurement methodologies).
Nevertheless, the results presented here are quite
promising and constitute an advance on the
measurement of neuromuscular variables.
In conclusion, this study is a start of a project
related to the cognitive function of elderly people,
consisting the evaluation of the 30-s CST with a
simple mobile device. However, for future work, our
method should be validated with more elderly
people.
It is also our intention to integrate this test
protocol on a scientific study, whose principal aim
will consist in the analysis of the influence of a high-
speed resistance training program on the RT and the
velocity of the movement of institutionalized elderly
people with and without cognitive disorders.
ACKNOWLEDGEMENTS
This work was supported by FCT project
UID/EEA/50008/2013 (Este trabalho foi suportado
pelo projecto FCT UID/EEA/50008/2013).
The authors would also like to acknowledge the
contribution of the COST Action IC1303
AAPELE Architectures, Algorithms and Protocols
for Enhanced Living Environments.
REFERENCES
Arnold, P., Vantieghem, S., Gorus, E., Lauwers, E.,
Fierens, Y., Pool-Goudzwaard, A., and Bautmans, I.
(2015). Age-related differences in muscle recruitment
and reaction-time performance. Experimental
Gerontology, 70, 125-130. doi:10.1016/
j.exger.2015.08.005.
Bautmans, I., Vantieghem, S., Gorus, E., Grazzini, Y. R.,
Fierens, Y., Pool-Goudzwaard, A., and Mets, T.
(2011). Age-related differences in pre-movement
antagonist muscle co-activation and reaction-time
performance. Exp Gerontol, 46(8), 637-642.
doi:10.1016/j.exger.2011.03.002.
Bennett, T. R., Jafari, R., and Gans, N. (2014). Motion
based acceleration correction for improved sensor
orientation estimates. Paper presented at the Wearable
and Implantable Body Sensor Networks (BSN), 2014
11th International Conference on.
Botia, J. A., Villa, A., and Palma, J. (2012). Ambient
Assisted Living system for in-home monitoring of
healthy independent elders. Expert Systems with
Applications, 39(9), 8136-8148. doi:10.1016/
j.eswa.2012.01.153.
Davis, P. J., and Rabinowitz, P. (2007). Methods of
numerical integration: Courier Corporation.
Dobre, C., Mavromoustakis, C. x., Garcia, N., Goleva, R.
I., and Mastorakis, G. (2016). Ambient Assisted Living
Measurement of the Reaction Time in the 30-S Chair Stand Test using the Accelerometer Sensor Available in off-the-Shelf Mobile Devices
297
and Enhanced Living Environments: Principles,
Technologies and Control: Butterworth-Heinemann.
Garcia, N. M. (2016). A Roadmap to the Design of a
Personal Digital Life Coach ICT Innovations 2015:
Springer.
Garcia, N. M., Rodrigues, J. J. P. C., Elias, D. C., and
Dias, M. S. (2014). Ambient Assisted Living: Taylor &
Francis.
Huawey. (2013). HUAWEI Ascend Y530 Smart Phone
V100R001 - Product Description Retrieved from
http://consumer.huawei.com/download/downloadCent
er?downloadId=26875&version=49612&siteCode=my.
Jain, A., Bansal, R., Kumar, A., and Singh, K. D. (2015).
A comparative study of visual and auditory reaction
times on the basis of gender and physical activity
levels of medical first year students. International
Journal of Applied and Basic Medical Research, 5(2),
124-127. doi:10.4103/2229-516X.157168.
Jones, C. J., Rikli, R. E., and Beam, W. C. (1999). A 30-s
chair-stand test as a measure of lower body strength in
community-residing older adults. Res Q Exerc Sport,
70(2), 113-119. doi:10.1080/02701367.1999.
10608028.
Keller, J. B. (1987). Newton’s second law. American
Journal of Physics, 55(12), 1145-1146.
Millor, N., Lecumberri, P., Gomez, M., Martinez, A.,
Martinikorena, J., Rodriguez-Manas, L., . . . Izquierdo,
M. (2017). Gait Velocity and Chair Sit-Stand-Sit
Performance Improves Current Frailty-Status
Identification. Ieee Transactions on Neural Systems
and Rehabilitation Engineering, 25(11), 2018-2025.
doi:10.1109/tnsre.2017.2699124.
Mulligan, J. B., Arsintescu, L., and Flynn-Evans, E. E.
(2016). Measurement of Visual Reaction Times using
Hand-held Mobile Devices. Journal of Vision, 16(4),
41-41. doi:10.1167/16.4.42.
Pereira, A., Izquierdo, M., Silva, A. J., Costa, A. M.,
Bastos, E., Gonzalez-Badillo, J. J., and Marques, M.
C. (2012). Effects of high-speed power training on
functional capacity and muscle performance in older
women. Experimental Gerontology, 47(3), 250-255.
doi:10.1016/j.exger.2011.12.010.
Pires, I., Garcia, N., Pombo, N., and Flórez-Revuelta, F.
(2016). From Data Acquisition to Data Fusion: A
Comprehensive Review and a Roadmap for the
Identification of Activities of Daily Living Using
Mobile Devices. Sensors, 16(2), 184.
Pires, I. M., Andrade, M., Garcia, N. M., Crisóstomo, R.,
and Florez-Revuelta, F. (2015). Measurement of Heel-
Rise Test Results using a Mobile Device. Paper
presented at the Proceedings of PhyCS 2015, 2nd
International Conference on Physiological Computing
Systems, Angers, France.
Pires, I. M., Garcia, N. M., and Canavarro Teixeira, M. C.
(2015, 12-15 January 2015). Calculation of Jump
Flight Time using a Mobile Device. Paper presented at
the Proceedings of the HEALTHINF 2015 8th
International Conference on Health Informatics,
Lisbon, Portugal.
Pires, I. M., Garcia, N. M., and Flórez-Revuelta, F. (2015).
Multi-sensor data fusion techniques for the
identification of activities of daily living using mobile
devices. Paper presented at the Proceedings of the
ECMLPKDD 2015 Doctoral Consortium, European
Conference on Machine Learning and Principles and
Practice of Knowledge Discovery in Databases, Porto,
Portugal.
Pires, I. M., Garcia, N. M., Pombo, N., and Flórez-
Revuelta, F. (2016). Identification of Activities of
Daily Living Using Sensors Available in off-the-shelf
Mobile Devices: Research and Hypothesis. Paper
presented at the Ambient Intelligence-Software and
Applications7th International Symposium on
Ambient Intelligence (ISAmI 2016).
Regterschot, G. R. H., Folkersma, M., Zhang, W., Baldus,
H., Stevens, M., and Zijlstra, W. (2014). Sensitivity of
sensor-based sit-to-stand peak power to the effects of
training leg strength, leg power and balance in older
adults. Gait & Posture, 39(1), 303-307.
doi:10.1016/j.gaitpost.2013.07.122.
Reis, S., Felizardo, V., Pombo, N., and Garcia, N. (2016).
Elderly mobility analysis during Timed Up and Go test
using biosignals. Paper presented at the Proceedings
of the 7th International Conference on Software
Development and Technologies for Enhancing
Accessibility and Fighting Info-exclusion, Vila Real,
Portugal.
Rikli, R. E., and Jones, C. J. (2001). Senior fitness test
manual. Champaign, IL: Human Kinetics.
Tao, D. C. (1967). Fundamentals of applied kinematics:
Addison-Wesley Pub. Co.
Tihanyi, J., Apor, P., and Fekete, G. (1982). Force-
velocity-power characteristics and fiber composition
in human knee extensor muscles. European journal of
applied physiology and occupational physiology,
48(3), 331-343.
Walston, J. D. (2012). Sarcopenia in older adults. Current
opinion in rheumatology, 24(6), 623-627.
doi:10.1097/BOR.0b013e328358d59b.
Wong, A. L., Haith, A. M., and Krakauer, J. W. (2015).
Motor Planning. Neuroscientist, 21(4), 385-398.
doi:10.1177/1073858414541484.
HSP 2018 - Special Session on Healthy and Secure People
298