Healthcare-Oriented Characterisation of Human Movements
by Means of Impulse-Radar Sensors
and by Means of Accelerometric Sensors
Paweł Mazurek
1
, Jakub Wagner
1
, Andrzej Miękina
1
, Roman Z. Morawski
1
and Frode Fadnes Jacobsen
2
1
Institute of Radioelectronics and Multimedia Technology, Faculty of Electronics and Information Technology,
Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
2
Bergen University College, Center for Care Research, Møllendalsveien 6-8, 5020 Bergen, Norway
Keywords: Healthcare, Impulse-Radar Sensor, Accelerometer, Measurement Data Processing, Uncertainty Estimation.
Abstract: This paper is devoted to the healthcare-oriented characterisation of the human movements by means of the
accelerometric and impulse-radar sensors – the sensors that may be employed in care services for
monitoring of elderly and disabled persons. Characterisation of the movements in terms of the so-called
self-selected walking velocity can be used by the medical and healthcare personnel to assess the overall
health status of a monitored person. The quality of the characterisation, based on the measurement data from
accelerometric and impulse-radar sensors, has been assessed in a series of real-world experiments which
involved the estimation of the instantaneous and mean walking velocity of a person moving according to
predefined patterns. Some indicators of uncertainty of the velocity estimation, determined with respect to
assumed predefined velocity values, have been used for comparison of the performance of both types of
sensors. The experiments have shown that impulse-radar sensors enable one to estimate the mean walking
velocity more accurately than the accelerometric sensors: the estimates obtained on the basis of data from
the latter sensors are affected by larger bias and are more widely spread around their mean values.
1 INTRODUCTION
The life expectancy has been growing in Europe for
many years, while the healthy life expectancy has
been slightly diminishing since the last decade of the
XXth century (cf. http://www.healthy-life-years.eu/).
Hence the growing importance of research on new
technologies that could be employed in monitoring
systems supporting care services for elderly and
disabled persons. The capability of those systems to
detect dangerous events, such as person’s fall, is of
key importance (Hamm et al., 2016). However,
those systems are expected not only to detect
dangerous events, but also to predict those events on
the basis of acquired data. The analysis of gait, as
well as of the itinerary and timing of activities of the
monitored persons, may thus contribute to the
prevention (Baldewijns et al., 2016a). The relevance
of features related to gait analysis in monitoring of
elderly persons, and in particular – in fall
prevention, has been emphasised in several recent
papers (Buracchio et al., 2010, Studenski et al.,
2011, Lusardi, 2012, Egerton et al., 2014, Stone et
al., 2015, Thingstad et al., 2015, Baldewijns et al.,
2016b).
So far, the most popular monitoring technique,
already applied in healthcare practice, is based on
wearable devices (Bulling et al., 2014, Cola et al.,
2014, Luque et al., 2014, Brodie et al., 2015). Those
devices do not require a pre-built infrastructure and
thus may be used outdoor. The signals from
movement sensors (mainly accelerometers and
gyroscopes), worn by a monitored person, are
transmitted via radio links to a computer and
analysed. This solution makes also possible the
acquisition of physiological data (such as values of
blood pressure, ECG data or EEG data).
Recently, numerous attempts have been made to
apply various radar techniques for monitoring of
elderly and disabled persons (Cuddihy et al., 2012,
Liu et al., 2012, Tomii and Ohtsuki, 2012, Jian et al.,
2014, Su et al., 2015, Miękina et al., 2016b). Those
attempts are mainly motivated by the conviction that
128
Mazurek P., Wagner J., MiÄ
´
Zkina A., Morawski R. and Jacobsen F.
Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors.
DOI: 10.5220/0006154201280138
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 128-138
ISBN: 978-989-758-213-4
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
those techniques may be less intrusive than vision-
based solutions (e.g. digital cameras), less
cumbersome than wearable solutions (e.g.
accelerometers and gyroscopes), and less invasive
with respect to the home environment than
environmental solutions (e.g. pressure sensors).
This paper is devoted to the assessment of the
uncertainty of the estimation of the walking velocity,
on the basis of data acquired by means of impulse-
radar sensors and by means of accelerometric
sensors. As suggested in the literature, e.g. (Fritz and
Lusardi, 2009), the walking velocity is highly
informative for healthcare experts; for example:
the velocity lower than 0.6 m/s enables them to
predict an increase in the risk of falls and
hospitalisation of a monitored person;
an improvement in walking velocity of at least
0.1 m/s is a useful predictor for well-being;
a decrease of the same amount is correlated with
deterioration of the health status or advancement
of disability.
The comparative study, reported in this paper, is
based on an extensive set of real-world experiments
which comprise:
simultaneous recording of measurement data
from both types of sensors, representative of the
gait characteristics of a person moving according
to predefined patterns;
statistical analysis of those data, aimed at
determination of certain indicators of uncertainty
of the velocity estimation.
Due to the operation principle of both types of
sensors, one may expect that the position of a
monitored person can be better estimated on the
basis of the data from impulse-radar sensors
(hereinafter called radar data for brevity), and its
acceleration – on the basis of data from the
accelerometric sensors (hereinafter called
accelerometric data for brevity). Therefore, despite
the fact that both the position and the acceleration
may also be of interest for the healthcare personnel,
this study is confined to the uncertainty of the
estimation of the velocity, which requires similar
degree of the measurement data preprocessing for
both types of sensors.
2 METHODOLOGY OF
EXPERIMENTATION
2.1 Data Acquisition
The raw measurement data for experimentation have
been acquired by means of the APDM Opal
accelerometric sensor (cf. http://www.apdm.com/
wearable-sensors/) attached to the waist of a
monitored person, and by means of a pair of
synchronised impulse-radar sensors – cf. (Morawski
et al., 2014) – whose location is shown in figure 1. A
monitored person has moved at the distance of ca.
1–6.5 m from each of them.
The walking velocity has been assessed on the
basis of real-world data acquired when an
experimenter has been walking at various constant
velocities, ranging from 0.5 m/s to 1.0 m/s, forth and
back along a straight line –
20R
times along the
x-axis, between points (0,3) and (4,3), and
R
times
along the y-axis, between points (2,1) and (2,5) (cf.
figure 1). In order to assure a known constant
walking velocity, a metronome has been used.
Figure 1: Experimental setup; the crosses indicate the
reference points, i.e. the points where marks have been
placed on the floor.
2.2 Data Preprocessing
2.2.1 Radar Data
The measurement data from a pair of impulse-radar
sensors – after preliminary preprocessing, as
described in (Miękina et al., 2016a) – take on the
form of a sequence of numbers representative of the
x-y coordinates of a monitored person.
A sequence of the estimates of the instantaneous
walking velocity may be obtained by numerical
differentiation of the sequence of the position
estimates, e.g. by means of the central-difference
Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors
129
method (Wagner et al., 2015), defined by the
formula:
(1)
11
ˆ
for 1, , 1
nn
n
n
dd
dnN
t


(1)
where
n
d
is a sequence of data to be
differentiated, and
11nn n
tt t


are the
differentiation steps, with
n
t
denoting the time
moments at which the data have been acquired. That
method is, however, very sensitive to errors
corrupting the data used for derivative estimation;
therefore, it should be regularised through, e.g.,
optimisation of the differentiation step. The total
velocity magnitude has been calculated according to
the formula:

22
(1) (1)
ˆˆ ˆ
for 1, ,
nn n
vx y n N
(2)
where
(1)
ˆ
n
x
and
(1)
ˆ
n
y
are estimates of the first
derivatives, computed on the basis of the estimates
of the x- and y-data sequences.
2.2.2 Accelerometric Data
An accelerometric sensor – composed of an
accelerometer, magnetometer and gyroscope –
provides a sequence of data representative of the
monitored person’s instantaneous acceleration in
three directions, viz. magnetic north, magnetic west,
and vertical. A sequence of the estimates of the
instantaneous velocities in these directions can be
obtained by numerical integration of the sequences
of the acceleration values. It must be, however,
taken into account that – since both systematic and
random errors corrupting accelerometric data
propagate through the integration process (Thong et
al., 2004) – the velocity estimates may be subject to
a growing-with-time drift and random errors whose
standard deviation is also growing with time. As a
consequence, non-zero estimates may appear even
when a monitored person is standing still; therefore,
the velocity estimates have to be corrected by means
of a so-called zero-velocity compensation procedure
(Bang et al., 2003). It can be applied to a velocity
trajectory whose first and last values are known to
be zero. In the research reported here, the following
correction formula has been used:
1
21
ˆˆ
nn
nn
vv
nn

for
12
nnn
(3)
where
21
ˆˆ
nn
vv

,
1
n
and
2
n
are the indices of the
first and last time instants of the movement,
respectively; the latter parameters have been
determined experimentally. The corrected velocity
trajectories in the magnetic north and west directions
(denoted with
ˆ
N
n
v
and
ˆ
W
n
v
, respectively) have been
used for computing the total velocity magnitude
according to the formula:

22
ˆˆ ˆ
NW
nn n
vv v
for
1, ,nN
(4)
2.3 Criteria of Performance Evaluation
In each experiment,
R
sequences of the
instantaneous total velocity estimates have been
computed using equations 2 and 4 on the basis of
both radar data and accelerometric data:
()
ˆ
| 1,...,
r
n
vn N
for
1, ,rR
(5)
Prior to the evaluation of the uncertainty of the
estimation, some outlying sequences have been
removed to prevent the misinterpretation of the
results. The outlying sequences have been identified
as those whose mean value:
() ()
1
1
ˆˆ
N
rr
n
n
v
N
(6)
deviated from the group mean value:
()
1
1
ˆˆ
R
r
r
R

(7)
by more than three standard deviations:

2
()
1
1
ˆˆˆ
1
R
r
r
R

(8)
Next, the qualitative assessment of the
uncertainty of the estimates has been performed. It
has been based on the inspection of the estimates of
the mean:
()
1
1
ˆˆ
R
r
nn
r
v
R
(9)
and standard deviation:

2
()
1
1
ˆˆˆ
1
R
r
nnn
r
v
R


(10)
of each element of the sequence of the instantaneous
velocity estimates;
R
denotes the number of
sequences in a set under consideration after
removing the outlying sequences.
Finally, the quantitative assessment of the
uncertainty of the estimates of the mean walking
velocity has been done using the following
indicators:
the absolute discrepancy between the mean value
of the estimates of the velocity and the
HEALTHINF 2017 - 10th International Conference on Health Informatics
130
predefined value of that velocity,
the absolute root-mean-square discrepancy of the
estimates with respect to the predefined value,
the lower and upper bounds of the absolute
discrepancy between the estimates and the
predefined value.
The above indicators have been calculated
separately for each set of
R
estimates of mean
walking velocity, obtained in each experiment by
averaging its
N
samples.
3 RESULTS AND DISCUSSION
In figures 2–5, the mean sequences of instantaneous
velocity estimates of a moving person, obtained on
the basis of the radar data and accelerometric data –
for both directions of movement (i.e. along x-axis
and along y-axis) and for all predefined velocity
values – are presented.
It is worth being noticed that the uncertainty of
estimation, based on radar data, is direction
dependent: for the movement along the x-axis and
predefined velocity values from 0.5 m/s to 0.7 m/s,
the estimated mean value of the velocity oscillates
around the predefined value during the movement.
This cannot be observed for the movement along the
y-axis in the same range of velocity values.
Moreover, it may be seen that the standard deviation
of the velocity is greater for the movement along the
x-axis.
Those differences are caused by the fact, that the
calculation of the position of the moving person is
easier when the distance between the person and
each of the radars is equal (i.e. when each radar sees
the same side of the human body).
On the other hand, it may be noticed that the
uncertainty of estimation, based on accelerometric
data, is direction independent.
a) b)
Figure 2: Uncertainty indicators determined for estimates of the velocity of a moving person, obtained on the basis of the
radar data (a) and accelerometric data (b), for the movement along x-axis with the velocity values ranging from 0.5 m/s to
0.6 m/s. In all sub-figures: the thick solid line denotes the sequence of mean values, while the dotted lines – the sequences
of values that are three standard deviations away from the mean sequence.
456789101112
Time [s]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 0.5 m/s
Predefined value
Mean value
Mean value +/- 3std
345678910
Time
[
s
]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 0.6 m/s
Predefined value
Mean value
Mean value +/- 3std
Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors
131
a) b)
Figure 3: Uncertainty indicators determined for estimates of the velocity of a moving person, obtained on the basis of the
radar data (a) and accelerometric data (b), for the movement along x-axis with the velocity values ranging from 0.7 m/s to
1.0 m/s. In all sub-figures: the thick solid line denotes the sequence of mean values, while the dotted lines – the sequences
of values that are three standard deviations away from the mean sequence.
2345678
Time
[
s
]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 0.8 m/s
Predefined value
Mean value
Mean value +/- 3std
23456
Time [s]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 1 m/s
Predefined value
Mean value
Mean value +/- 3std
23456
Time
[
s
]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 1 m/s
Predefined value
Mean value
Mean value +/- 3std
HEALTHINF 2017 - 10th International Conference on Health Informatics
132
a) b)
Figure 4: Uncertainty indicators determined for estimates of the velocity of a moving person, obtained on the basis of the
radar data (a) and accelerometric data (b), for the movement along y-axis with the velocity values ranging from 0.5 m/s to
0.8 m/s. In all sub-figures: the thick solid line denotes the sequence of mean values, while the dotted lines – the sequences
of values that are three standard deviations away from the mean sequence.
456789101112
Time [s
]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 0.5 m/s
Predefined value
Mean value
Mean value +/- 3std
345678910
Time [s
]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 0.6 m/s
Predefined value
Mean value
Mean value +/- 3std
3456789
Time [s
]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 0.7 m/s
Predefined value
Mean value
Mean value +/- 3std
2345678
Time [s]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 0.8 m/s
Predefined value
Mean value
Mean value +/- 3std
Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors
133
a) b)
Figure 5: Uncertainty indicators determined for estimates of the velocity of a moving person, obtained on the basis of the
radar data (a) and accelerometric data (b), for the movement along y-axis with the velocity values ranging from 0.9 m/s to
1.0 m/s. In all sub-figures: the thick solid line denotes the sequence of mean values, while the dotted lines – the sequences
of values that are three standard deviations away from the mean sequence.
In figure 6, the so-called box plots representing
the aggregated uncertainty of the estimation of the
mean walking velocity, performed on the basis of
the radar data and accelerometric data, for each
investigated value of the walking velocity, are
presented. Each box plot indicates:
the median value;
the interquartile range (IQR), i.e. range between
the first and third quartile;
the smallest value still within 1.5 IQR from the
first quartile, and the largest value still within 1.5
IQR from the third quartile;
the values lying outside 1.5 IQR from the first
quartile and 1.5 IQR from the third quartile
(marked with crosses).
In table 1 and table 2, the numerical results of all
experiments – performed for various walking
velocities – are collected.
The results presented in tables 1 and 2 show that
the estimates of the mean walking velocity, obtained
on the basis of the radar data, are far more accurate
than those obtained on the basis of the
accelerometric data. For the estimation of the
velocity based on the radar data the mean
discrepancy, i.e. the difference between estimated
mean value and a predefined value of the velocity,
varies from –0.12 to 0.03 m/s, while it varies from
–0.18 to 0.24 m/s for the estimation based on
accelerometric data. Moreover, it can be observed
that the estimates obtained on the basis of the radar
data are more concentrated around their mean values
– the root-mean-square discrepancy of the radar-
data-based velocity estimates varies from 0.02 to
0.12 m/s, while it varies from 0.08 to 0.27 m/s for
the accelerometric-data-based estimates.
It can also be noticed that the estimates of the
mean walking velocity, obtained on the basis of the
radar data, tend to be underrated with respect to the
predefined walking velocity for the movements
along the x-axis, and very accurate for the
234567
Time [s
]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 0.9 m/s
Predefined value
Mean value
Mean value +/- 3std
23456
Time
[
s
]
-0.5
0
0.5
1
1.5
2
Predefined velocity = 1 m/s
Predefined value
Mean value
Mean value +/- 3std
HEALTHINF 2017 - 10th International Conference on Health Informatics
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Figure 6: Box plots representing the uncertainty of the estimation of the walking velocity, based on the radar data (R-
estimates) and accelerometric data (A-estimates); a) x-axis movement, b) y-axis movement.
movements along the y-axis. On the other hand, the
estimates of the mean walking velocity, obtained on
the basis of the accelerometric data, seem to be
underrated for lower walking velocities and
overrated for faster movements.
Lastly, it should be noted that the impact of the
imperfections of the movements of the experimenter,
reproducing the predefined patterns, are the same for
both sensors; so, not changing the result of
comparison.
4 CONCLUSIONS
The novelty of the study, whose results are presented
in this paper, consists in systematic comparison of
two monitoring techniques, viz. impulse-radar
sensors and accelerometric sensors, when applied for
healthcare-oriented characterisation of the human
movements.
The performance of both types of sensors has
been compared on the basis of data acquired by
means of them in a series of real-world experiments
which involved tracking of a person moving
according to predefined patterns. The indicators of
uncertainty of the velocity estimation have been
determined with respect to the assumed predefined
values of velocity.
Prior to the evaluation of the uncertainty, the
measurement data from both types of sensors have
to be adequately processed. The velocity estimates,
obtained on the basis of the accelerometric data, are
determined by numerical integration of the
sequences of the acceleration estimates and
corrected by means of a zero-velocity compensation
procedure. The velocity estimates, obtained on the
basis of the radar data, are determined using the
regularised numerical differentiation of the sequence
of the position estimates.
Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors
135
Table 1: Uncertainty of mean velocity estimation for the movement along x-axis.
Uncertainty indicators characterising
estimates of mean velocity
Predefined walking velocity [m/s]
0.50 0.60 0.70 0.80 0.90 1.00
Impulse-radar sensors
Mean discrepancy [m/s] 0.03 0.04 0.06 0.08 0.08 0.12
Root-mean-square discrepancy [m/s] 0.04 0.05 0.06 0.08 0.09 0.12
Upper bound of the discrepancy [m/s] 0.01  0.03 0.03 0.06 0.07
Lower bound of the discrepancy [m/s] 0.05 0.07 0.09 0.13 0.12 0.14
Accelerometric sensors
Mean discrepancy [m/s] 0.17 0.06 0.12 0.12 0.18 
Root-mean-square discrepancy [m/s] 0.19 0.17 0.17 0.14 0.23 0.13
Upper bound of the discrepancy [m/s] 0.01 0.27 0.42 0.25 0.44 0.29
Lower bound of the discrepancy [m/s] 0.29 0.41 0.18 0.03 0.04 0.18
Table 2: Uncertainty of mean velocity estimation for the movement along y-axis.
Uncertainty indicators characterising
estimates of mean velocity
Predefined walking velocity [m/s]
0.50 0.60 0.70 0.80 0.90 1.00
Impulse-radar sensors
Mean discrepancy[m/s] 0.02 0.03 0.03   
Root-mean-square discrepancy [m/s] 0.03 0.03 0.03 0.03 0.03 0.02
Upper bound of the discrepancy [m/s] 0.04 0.06 0.07 0.05 0.07 0.04
Lower bound of the discrepancy [m/s] 0.02 0.01 0.01 0.01 0.04 0.03
Accelerometric sensors
Mean discrepancy [m/s] 0.18  0.24 0.12 0.17 0.07
Root-mean-square discrepancy [m/s] 0.19 0.21 0.27 0.13 0.18 0.08
Upper bound of the discrepancy [m/s] 0.06 0.55 0.43 0.21 0.29 0.18
Lower bound of the discrepancy [m/s] 0.27 0.40 0.01 0.03 0.06 0.03
The experiments performed have demonstrated
that impulse-radar sensors enable one to estimate the
walking velocity more accurately than the
accelerometric sensors. The estimates obtained on
the basis of data from the latter sensors are affected
by larger bias and are more widely spread around
their mean values.
Since falls among elderly persons are the main
cause of their admission and long-term stay in
hospitals (Abbate et al., 2010), the systems for
monitoring of elderly and disabled persons are
expected to perform some functions related to fall
prevention and/or fall detection. The functions
related to fall prevention are implemented to
overcome fall risk factors, implied by natural aging-
related physical disabilities, and promptly indicate
the increasing risk of falling; the functions related to
fall detection are to reliably detect falls, when they
occur, and minimise the potential injuries. Sensors
used for fall prevention are expected to be accurate
enough to enable the monitoring system to identify
changes in the monitored person’s health status on
the basis of relatively slow and subtle changes in
his/her gait characteristics, e.g. changes of the mean
HEALTHINF 2017 - 10th International Conference on Health Informatics
136
walking velocity. Sensors used for fall detection
should be selected and optimised with respect to
their sensitivity as to enable the monitoring system
to detect short abrupt changes in person’s velocity or
acceleration.
In light of the results presented in this paper, the
impulse-radar sensors seem to be promising means
for reliable fall prevention since they enable the
through-the-wall monitoring of persons (as the
electromagnetic waves propagate through non-metal
objects) and highly accurate estimation of their
velocity; those sensors are, however, less
appropriate for fall detection because of the
relatively low rate of data acquisition. On the other
hand, the accelerometric sensors appear to be not
well-suited for the long-term monitoring of the
person’s gait characteristics, but better satisfy the
requirements related to fall detection, due to their
higher sensitivity, significantly higher rate of data
acquisition, and suitability for outdoor use.
One may thus conclude that both types of sensors
studied in this paper, viz. impulse-radar sensors and
accelerometric sensors, are in some way
complementary, and therefore the combined use of
both of them may contribute to the increase in the
reliability of the monitoring of elderly and disabled
persons.
ACKNOWLEDGEMENTS
This work has been initiated within the project
PL12-0001 financially supported by EEA Grants –
Norway Grants (http://eeagrants.org/project-portal/
project/PL12-0001), and finished within the
statutory project supported by the Institute of
Radioelectronics and Multimedia Technology,
Faculty of Electronics and Information Technology,
Warsaw University of Technology.
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