Applicability of multiple impulse-radar sensors for the recognition of a person’s action


ACTA IMEKO 
ISSN: 2221-870X 
June 2023, Volume 12, Number 2, 1 - 7 

 

ACTA IMEKO | www.imeko.org June 2023 | Volume 12 | Number 2 | 1 

Applicability of multiple impulse-radar sensors for the 
recognition of a person’s action 

Paweł Mazurek1, Szymon Kruszewski1 

1 Warsaw University of Technology, Faculty of Electronics and Information Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland 

 

 

Section: RESEARCH PAPER  

Keywords: measurement data processing; impulse-radar sensors; position estimation; action recognition; healthcare 

Citation: Paweł Mazurek, Szymon Kruszewski, Applicability of multiple impulse-radar sensors for the recognition of a person’s action, Acta IMEKO, vol. 12, 
no. 2, article 30, June 2023, identifier: IMEKO-ACTA-12 (2023)-02-30 

Section Editor: Eric Benoit, Université Savoie Mont Blanc, France  

Received July 17, 2022; In final form May 16, 2023; Published June 2023 

Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, 
distribution, and reproduction in any medium, provided the original author and source are credited. 

Corresponding author: Paweł Mazurek, e-mail: pawel.mazurek@pw.edu.pl  

 

1. INTRODUCTION 

It is expected that the share of European population aged at 
least 65 years will reach 25% in 2050 [1]. The problem of 
organised care over elderly persons is, therefore, of growing 
importance. This, in turn, creates the demand for various 
technical solutions which could be applied for non-intrusive 
monitoring of elderly persons in home environments and 
healthcare facilities. The systems for monitoring of elderly 
persons are expected to predict and detect dangerous events, 
such as falls and harmful long lies after the falls. The falls of 
elderly persons belong to the most frequent reasons of their 
admission and long-term stay in hospitals [2]. 

Possible solutions that could be applied for non-intrusive 
monitoring of elderly persons are radar-based techniques – both 
narrow-band [3]–[8] and broad-band [9]–[13]. The most 
attractive feature of these techniques is the possibility of the 
through-the-wall monitoring of human activity. A review of the 
relevant literature, including articles in scientific journals and 
conference papers, which appeared in the years 2019–2022, has 
revealed that the vast majority of researchers use radar sensors 
of both types for estimation of the heart rate, breathing rate and 

position (in two dimensions), while the attempts to detect falls 
are based on sensors using the Doppler principle – with two 
exceptions: 

• In [14] an attempt to detect falls on the basis of three-
dimensional movement trajectory obtained by means 
of the three impulse-radar sensors is presented. In the 
reported approach, the monitored person's movement 
is compared with two model movements: a movement 
with a constant speed, and a movement towards the 
ground with an acceleration equal to the gravitational 
acceleration, and the classification of the movement is 
based on the reliability function. Unfortunately, no 
systematic tests of the effectiveness of the method are 
presented in that paper. 

• In [15] the results of the simulation studies focused on 
the impact of the number of the impulse-radar sensors 
on the accuracy of the estimation of the three-
dimensional position is presented. Unfortunately, the 
simulated setup is based on an unrealistic assumption 
that the sensors are placed in random locations within 
a monitored area. 

In the authors’ recent conference papers [16], [17], the results 
of the studies on the applicability of multiple impulse-radar 

ABSTRACT 
The research reported in this paper is devoted to the impulse-radar technology when applied for non-intrusive monitoring of elderly 
persons. Specifically, this study is focused on a novel approach to the interpretation of data acquired by means of multiple impulse-
radar sensors, leading to the determination of features to be used for the recognition of a monitored person’s actions. The measurement 
data are first transformed into the three-dimensional coordinates of the monitored person; next, those coordinates are used as a basis 
for determination of features characterising the movement of that person. The results of the experimentation, based on the real-world 
data, show that multiple impulse-radar sensors may be successfully used for highly accurate recognition of actions such as walking, 
sitting, and lying down, although this accuracy is significantly affected by the quality of the three-dimensional movement trajectories 
which in turn is affected by the configuration of the impulse-radar sensors within the monitored area. 

mailto:pawel.mazurek@pw.edu.pl


 

ACTA IMEKO | www.imeko.org June 2023 | Volume 12 | Number 2 | 2 

sensors for estimation of the three-dimensional movement 
trajectories – which could be used for detection of dangerous 
events such as falls – are presented. These results show that the 
impulse-radar sensors may be used for accurate estimation of the 
three-dimensional position of a monitored person if the sensors 
are properly located within the monitored area. In this paper, the 
applicability of the impulse-radar sensors for recognition of 
person’s action, on the basis of the estimates of the three-
dimensional movement trajectories, is investigated. 

The novelty of the research presented in this paper consists 
in an algorithmic basis for recognition of actions of a person, in 
a monitoring system based on multiple impulse-radar sensors. 
The processing of the raw measurement data acquired by means 
of the impulse-radar sensors is divided into three stages: the 
transformation of the measurement data into the three-
dimensional coordinates of the monitored person, the 
calculation of the features characterising the three-dimensional 
movement trajectories, and the classification of the movement 
trajectories. The usability of the proposed features is assessed in 
an experiment based on a set of real-world data sequences 
representative of three activities of daily living: walking, sitting 
and lying down. Moreover, the influence of the configuration of 
the impulse-radar sensors within the monitored area on the 
accuracy of the action recognition is investigated. 

2. ESTIMATION OF MOVEMENT TRAJECTORIES 

The measurement data used for the experimentation were 
acquired by means of six X4M02 impulse-radar sensors 
manufactured by Novelda [18], [19]. An exemplary data frame, 
acquired by means of one of these sensors, is shown in Figure 1. 

To properly estimate a three-dimensional movement 
trajectory of a monitored person, the measurement data, 
acquired by means of the impulse-radar sensors, have to be 
subjected to processing comprising [20]: the estimation of 
parameters of the impulse-radar signal, the smoothing of several 
one-dimensional trajectories of the distance between the 
monitored person and the corresponding impulse-radar sensors, 
and the transformation of the smoothed distance trajectories into 
the three-dimensional movement trajectory. In the research 
presented here: 

• The parameters of the impulse-radar signal have 
been estimated by means of a method consisting in 
computing the correlation function for the received 
signal and a known template of the emitted pulse, 
and the estimation of the coordinates of the 
maximum of this function [20]. 

• The distance trajectories have been smoothed by 
means of a method based on weighted least-squares 
estimator, consisting in the approximation of a 
sequence of data by means of a linear combination 

of basis functions, with the number of these 
functions determined automatically [17]. 

• The three-dimensional movement trajectories have 
been obtained by means of a method consisting in 
solving a set of equations modelling the geometrical 
relationships between the three-dimensional 
coordinates of a person and the distances between 
that person and the impulse-radar sensors [17]. 

3. METHODOLOGY OF EXPERIMENTATION 

3.1. Acquisition of measurement data 

The measurement data used for the experimentation aimed at 
the assessment of the accuracy of the recognition of the 
monitored person’s actions, were acquired by means of six 
impulse-radar sensors located at various positions. Two 
configurations of the sensors have been considered (see 
Figure 2): 

• Configuration #1 according to which the impulse-
radar sensors (R1, …, R6) were located at positions 
whose x-, y- and z-coordinates (in meters) were 
respectively: [0.00, 1.70, 0.93], [0.00, 1.70, 1.43], 
[2.20, 1.70, 1.45], [2.20, 1.70, 0.95], [0.20, 4.50, 0.82], 
[2.00, 4.50, 0.83]; 

• Configuration #2 according to which the impulse-
radar sensors (R1, …, R6) were located at positions 
whose x-, y- and z-coordinates (in meters) were 
respectively: [0.20, 4.50, 0.82], [0.60, 2.65, 2.76], 
[0.00, 1.70, 0.93], [2.20, 1.70, 0.95], [2.00, 4.50, 0.83], 
[0.60, 3.31, 2.76]. 

Concurrently, the person was monitored by an infrared depth 
sensor being a part of the Kinect V2 device (cf. [21] for the 
description of the methodology for preprocessing of data from 
depth sensors). The radar sensors and the depth sensor were 
synchronised, and their data acquisition rate was set to 30 Hz. In 
the experimentation, three movement scenarios were considered: 

• According to the first scenario, two persons walked 
along three predefined trajectories: an oval-shaped 
trajectory, a straight-line trajectory and a sine-shaped 
trajectory; each person repeated the action 10 times 
for each trajectory. 

• According to the second scenario, two persons sat 
on a chair located in three different places within the 
monitored area; each person repeated the action 10 
times for each position of the chair. 

• According to the third scenario, two persons lay 
down on a mattress, approaching it from two 
different sides; each person repeated the action 15 
times for each side of the mattress. 

Thus, the whole programme of experimentation comprised 
the acquisition of: 

• 180 three-dimensional movement trajectories 
obtained on the basis of the data acquired by means 
of the impulse-radar sensors located according to 
Configuration #1; 

• 180 three-dimensional movement trajectories 
obtained on the basis of the data acquired by means 
of the impulse-radar sensors located according to 
Configuration #2; 

• 180 three-dimensional movement trajectories 
obtained on the basis of the data acquired by means 
of the depth sensor. 

 

Figure 1. The example of raw measurement data.  



 

ACTA IMEKO | www.imeko.org June 2023 | Volume 12 | Number 2 | 3 

3.2. Generation of features 

In the experimentation, the features for classification have 
been determined on the basis of the sequences of the estimates 

of the z-coordinate of the position the persons, i.e. {�̂�𝑛}, as well 
as the sequences of the estimates of the velocity and acceleration 
along the z-axis (i.e. the first and second derivatives of the z-
coordinate, obtained by means of the forward difference 

method), denoted with {�̂�𝑧,𝑛 } and {�̂�𝑧,𝑛}, respectively. 
All the features are presented in Table 1. For the sake of 

simplicity, two operators have been introduced – the operator 

returning the empirical mean value of a data sequence {𝑝𝑛 }: 

𝑚[{𝑝𝑛}] ≡
1

𝑁
∑ 𝑝𝑛

𝑁

𝑛=1

 (1) 

and the operator returning its empirical variance: 

𝑠2[{𝑝𝑛 }] ≡
1

𝑁 − 1
∑(𝑝𝑛 − 𝑚[{𝑝𝑛 }])

2

𝑁

𝑛=1

 . (2) 

Moreover, the velocity in the vertical dimension and the 

acceleration in that dimension are denoted with �̂�𝑣,𝑛 ≡ {|�̂�𝑧,𝑛|} 

and �̂�𝑣,𝑛 ≡ {|�̂�𝑧,𝑛|}, respectively. 

3.3. Classification 

In this study an error-correcting output codes classifier 
(ECOC) – suitable for multiclass classification problems – has 
been used [22]. The ECOC classifier has been based on multiple 
support vector machines (SVM) – each designed to distinguish 
between two selected actions. The implementation of the ECOC 
classifier, available in the MATLAB Statistics and Machine 
Learning Toolbox [23], has been used for this purpose. Before 
the training of the classifier, the values of the features have been 
standardised. The performance of the classifier has been assessed 
using the 10-fold cross-validation technique. 

The assessment of the accuracy of the classification has been 
based on the inspection of: 

• the receiver operating characteristic (ROC) curves 
illustrating the relationship between the true 
positive rate (TPR) the false positive rate (FPR), i.e. 
two indicators defined as follows: 

TPR =
TP

TP + FN
 (3) 

FPR =
FP

FP + TN
 , (4) 

where – for example, in the case of walking – TP 
(true positive) is the number of walks classified as 
walks, TN (true negative) is the number of non-
walks classified as non-walks, FP (false positive) is 
the number of non-walks classified as walks and FN 
(false negative) is the number of walks classified 
non-walks; the area under the ROC curve (AUC) is 
a single scalar value representing the performance; 

• the confusion matrices visualising the results of the 
classification: each row of such matrix represents 
the instances in an actual class while each column 
represents the instances in a predicted class. 

In the experiments based on the real-world data the use of the 
approximations of the movement trajectories is necessary since 
their reference shapes cannot be properly defined: a human body 
has a considerable volume and generates complex echoes which 
cannot be attributed to any of its specific points (e.g. to plexus 
solaris). An arbitrary choice of such a reference point would lead 
to an arbitrary definition of the systematic error which could be 
misleading. Fortunately, such a definition is not necessary for 
extraction of the features which are used for classification of the 
actions of a monitored person – the features characterising the 
dispersion of the values of the z-coordinate, the vertical velocity 
and the vertical acceleration. 

4. RESULTS OF EXPERIMENTATION 

In Figure 2, the examples of the estimates of the three-
dimensional movement trajectories of a monitored person, 
obtained by means of the procedure described in Section 2, for 
two configurations of the impulse-radar sensors – together with 
the projections on the three two-dimensional planes – are shown; 
in Figure 3, the dispersion of the subset of the estimates of the 
z-coordinate, representative of walking, sitting and lying down, is 
presented. 

In Figure 4, the ROC curves obtained for the classification of 
all the three-dimensional movement trajectories, are shown; the 
confusion matrices are presented in Figure 5. 

The analysis of the presented results is leading to the 
following conclusions: 

• Multiple impulse-radar sensors may be successfully 
used for estimation of the three-dimensional 
position of a moving person, although the 
configuration of those sensors has significant 
influence on the uncertainty of the estimation. To 
properly estimate the height-component of the 
position of a monitored person, few impulse-radar 
sensors should be located at a greater height than the 
rest of those sensors (compare Figure 2c with 
Figure 2d as well as Figure 2e with Figure 2f). 

 

Table 1. The features used in the experimentation. 

# Feature 

1 
Standard deviation of the z-coordinate:  

𝜎 = √𝑠2[{�̂�𝑛 }] 

2 
Difference between extreme values of the z-coordinate:  
Δ = max[{�̂�𝑛 }] − min[{�̂�𝑛 }] 

3 
Mean vertical velocity:  

𝜇𝑣 = 𝑚[{𝑣𝑣,𝑛 }] 

4 
Maximum vertical velocity:  

𝑣max = max[{𝑣𝑣,𝑛 }] 

5 
Standard deviation of the vertical velocity:  

𝜎 𝑣 = √𝑠2[{�̂�𝑣,𝑛 }] 

6 
Difference between extreme values of the vertical velocity:  

Δ𝑣 = max[{𝑣𝑣,𝑛 }] − min[{𝑣𝑣,𝑛 }] 

7 
Mean vertical acceleration:  

𝜇𝑎 = 𝑚[{�̂�𝑣,𝑛 }] 

8 
Maximum vertical acceleration:  

𝑎max = max[{�̂�𝑣,𝑛 }] 

9 
Standard deviation of the vertical acceleration: 

𝜎 𝑎 = √𝑠2[{�̂�𝑣,𝑛 }] 

10 
Difference between extreme values of the vertical acceleration:  

Δ𝑎 = max[{�̂�𝑣,𝑛 }] − min[{�̂�𝑣,𝑛 }] 



 

ACTA IMEKO | www.imeko.org June 2023 | Volume 12 | Number 2 | 4 

 
 

 
 

a) b) 

  

c) d) 

  

e) f) 

  

Figure 2. The examples of the estimates of the three-dimensional trajectories of a moving person, obtained for two configurations of the impulse-
radar sensors: trajectories representative of walking (top row), trajectories representative of sitting (middle row), and trajectories representative of 
lying down (bottom row). The movement trajectories obtained for Configuration #1 are depicted in the left column, while the movement trajectories 
obtained for Configuration #2 are depicted in the right column. Blue lines denote radar-data-based trajectories, grey lines denote depth-data-based 
trajectories while blue triangles indicate the positions of the radar sensors. 



 

ACTA IMEKO | www.imeko.org June 2023 | Volume 12 | Number 2 | 5 

a) 

 

b) 

 

c) 

 

 

 

Figure 3. The dispersion of the estimates of the z-coordinate of a moving 
person, obtained for two configurations of the impulse-radar sensors, for 
walking (a), sitting (b) and lying down (c). 

 

a) 

 

 

b) 

 

 

c) 

 

Figure 4. The receiver operating characteristic (ROC) curves obtained for the 
classification of all the three-dimensional movement trajectories: radar-data-
based trajectories obtained for Configuration #1 (a), radar-data-based 
trajectories obtained for Configuration #2 (b), depth-data-based trajectories 
(c). 

 
  



 

ACTA IMEKO | www.imeko.org June 2023 | Volume 12 | Number 2 | 6 

• The configuration of the impulse-radar sensors 
affects the estimates of the coordinates of a 
monitored person. In the case of Configuration #2 
the estimates of the z-coordinate are ca. 0.5 m 
greater than in the case of Configuration #1; 
moreover, the x-y projections of the trajectories, 
obtained for Configuration #2, seem to be scaled-
down versions of the analogous projections 
obtained for Configuration #1 (compare Figure 2a 
with Figure 2b). These discrepancies can be 
explained by a non-negligible volume of a human 
body: the impulse-radar sensors located at a greater 
height – and, therefore, oriented differently than the 
rest of those sensors – receive echoes reflected from 
different parts of the body of a monitored person. 
Moreover, in the case of Configuration #1 the 
changes in the z-coordinate, associated with the 
movement of the body towards the ground during 
sitting or lying down, may not be properly reflected 
in the estimates of the movement trajectory (see 
Figure 2c and Figure 2d). This phenomenon may be 
explained by the fact that when the person is moving 
towards the ground, the changes in the distance 
between that person and the impulse-radar sensors 
placed on the sides of the monitored area are not 
significant. In the case of the impulse-radar sensors 
placed on the ceiling, these changes are much 
greater. 

• The proposed features, characterising the monitored 
person’s movement in the vertical dimension, are 
sufficient to recognise walking, sitting and lying 
down with high accuracy, although this accuracy is 
significantly affected by the quality of the three-
dimensional movement trajectories which in turn is 
affected by the configuration of the impulse-radar 
sensors (compare Figure 5a with Figure 5b). 

The results of the classification of the three-dimensional 
movement trajectories obtained on the basis of the data acquired 
by means of the depth sensor, are the best because the depth 
sensor provides the most accurate estimates of the three-
dimensional position of the monitored person. Nevertheless, the 
results of the classification of the trajectories obtained on the 
basis of the data acquired by means of the impulse-radar sensors 
located according to Configuration #2, are only slightly worse 
and can be likely improved by the application of more 
sophisticated methods for impulse-radar data processing. 

5. CONCLUSIONS 

The novelty of the research presented in this paper consists 
in an approach to the interpretation of measurement data 
acquired by means of the impulse-radar sensors, leading to the 
determination of features to be used for recognition of actions 
of three types: walking, sitting and lying down. The data are first 
transformed into the three-dimensional coordinates of the 
monitored person; next, those coordinates are used as a basis for 
calculation of kinematic features characterising the monitored 
person’s movement in the vertical dimension. 

The results of the experimentation based on real-world data 
show that multiple impulse-radar sensors may be successfully 
used for highly accurate recognition of walking, sitting and lying 
down of a monitored person. It has to be noted, however, that 
the accuracy of the recognition is affected by the quality of the 

three-dimensional movement trajectories which in turn is 
affected by the configuration of the impulse-radar sensors. To 
properly estimate the height-component of the position of the 
monitored person, few impulse-radar sensors should be located 
at a greater height than the rest of those sensors. 

The results encourage the authors to focus on the 
development of the methods for processing of the impulse-radar 
data, enabling the detection of falls of the monitored person. 

REFERENCES 

[1] United Nations, Department of Economic and Social Affairs, 
Population Division (2019), World population prospects 2019: 
Highlights. ST/ESA/SER.A/423. Online [Accessed 15 March 
2022] 
https://population.un.org/wpp/publications  

[2] K. Chaccour, R. Darazi, A. H. E. Hassani, E. Andrès, From Fall 
Detection to Fall Prevention: A Generic Classification of Fall-
Related Systems, IEEE Sensors Journal, vol. 17, 2017, pp. 812-
822.  
DOI: 10.1109/JSEN.2016.2628099  

[3] M. G. Amin, Y. D. Zhang, F. Ahmad, K. C. Ho, Radar Signal 
Processing for Elderly Fall Detection The future for in-home 
monitoring, IEEE Signal Processing Magazine, vol. 33, 2016, pp. 
71–80.  
DOI: 10.1109/MSP.2015.2502784  

[4] O. Boric-Lubecke, V. M. Lubecke, A. D. Droitcour, Byung-Kwon 
Park, A. Singh, Eds., Doppler Radar Physiological Sensing. 
Hoboken (New Jersey): John Wiley & Sons, Inc., 2016. 

[5] B. Y. Su, K. C. Ho, M. Rantz, M. Skubic, Doppler Radar Fall 
Activity Detection Using The Wavelet Transform, IEEE 
Transactions on Biomedical Engineering, vol. 62, 2015, pp. 865-
875.  
DOI: 10.1109/TBME.2014.2367038  

[6] A. Gadde, M. G. Amin, Y. D. Zhang, F. Ahmad, Fall detection 
and classifications based on time-scale radar signal characteristics, 
Proc. SPIE, vol. 9077 'Radar Sensor Technology XVIII', 2014. 
DOI: 10.1117/12.2050998  

[7] D. Y. Wang, J. Park, H. J. Kim, K. Lee, S. H. Cho, Noncontact 
Extraction of Biomechanical Parameters in Gait Analysis Using a 
Multi-Input and Multi-Output Radar Sensor, IEEE Access, vol. 9, 
2021, pp. 138496-138508.  
DOI: 10.1109/ACCESS.2021.3117985  

[8] A.-K. Seifert, M. Grimmer, A. M. Zoubir, Doppler Radar for the 
Extraction Biomechanical Parameters in Gait Analysis, IEEE 
Journal of Biomedical and Health Informatics, vol. 25, 2021, pp. 
547-558.  
DOI: 10.1109/JBHI.2020.2994471  

[9] S. Gezici, H. V. Poor, Position Estimation via Ultra-Wide-Band 
Signals, Proceedings of the IEEE, vol. 97, 2009, pp. 386–403. 
DOI: 10.1109/JPROC.2008.2008840  

[10] P. Mazurek, A. Miękina, R. Z. Morawski, Comparative study of 
three algorithms for estimation of echo parameters in UWB radar 
module for monitoring of human movements, Measurement, vol. 
88, 2016, pp. 45-57.  
DOI: 10.1016/j.measurement.2016.03.025  

 

Figure 5. The exemplary confusion matrices obtained for the classification of 
all the three-dimensional movement trajectories: radar-data-based 
trajectories obtained for Configuration #1 (a), radar-data-based trajectories 
obtained for Configuration #2 (b), depth-data-based trajectories (c).  

https://population.un.org/wpp/publications
https://doi.org/10.1109/JSEN.2016.2628099
https://doi.org/10.1109/MSP.2015.2502784
https://doi.org/10.1109/TBME.2014.2367038
https://doi.org/10.1117/12.2050998
https://doi.org/10.1109/ACCESS.2021.3117985
https://doi.org/10.1109/JBHI.2020.2994471
https://doi.org/10.1109/JPROC.2008.2008840
https://doi.org/10.1016/j.measurement.2016.03.025


 

ACTA IMEKO | www.imeko.org June 2023 | Volume 12 | Number 2 | 7 

[11] J. J. Zhang, X. Dai, B. Davidson, Z. Zhou, Ultra-wideband radar-
based accurate motion measuring: human body landmark 
detection and tracking with biomechanical constraints, IET Radar, 
Sonar & Navigation, vol. 9, 2015, pp. 154-163.  
DOI: 10.1049/iet-rsn.2014.0223  

[12] R. Herrmann, J. Sachs, M-sequence-based ultra-wideband sensor 
network for vitality monitoring of elders at home, IET Radar, 
Sonar & Navigation, vol. 9, 2015, pp. 125-137.  
DOI: 10.1049/iet-rsn.2014.0214  

[13] H. Li, A. Mehul, J. Le Kernec, S. Z. Gurbuz, F. Fioranelli, 
Sequential Human Gait Classification With Distributed Radar 
Sensor Fusion, IEEE Sensors Journal, vol. 21, 2021, pp. 7590-
7603.  
DOI: 10.1109/JSEN.2020.3046991  

[14] W. Khawaja, F. Koohifar, I. Guvenc, UWB Radar Based Beyond 
Wall Sensing and Tracking for Ambient Assisted Living, in Proc. 
14th IEEE Annual Consumer Communications & Networking 
Conf., Las Vegas, NV, USA, 8-11 January 2017, pp. 142–147. 
DOI: 10.1109/CCNC.2017.7983096  

[15] T. J. Daim, R. M. A. Lee, Indoor Environment Device-Free 
Wireless Positioning using IR-UWB Radar, in Proc. 2018 IEEE 
Int. Conf. on Artificial Intelligence in Engineering and 
Technology, Kota Kinabalu, Malaysia, 8 November 2018, 4 pp. 
DOI:. 10.1109/IICAIET.2018.8638458  

[16] P. Mazurek, Choosing configuration of impulse-radar sensors in 
system for healthcare-oriented monitoring of persons, 
Measurement: Sensors, vol. 18, 2021, p. 100270.  
DOI: 10.1016/j.measen.2021.100270  

[17] P. Mazurek, Applicability of multiple impulse-radar sensors for 
estimation of person’s three-dimensional position, in Proc. 2020 
IEEE Int. Instrumentation and Measurement Technology Conf., 
Dubrovnik, Croatia, 25-28 May 2020, pp. 1-6.  
DOI: 10.1109/I2MTC43012.2020.9128760  

[18] Novelda, X4M02 Radar Sensor. Online [Accessed 8 November 
2019]  
https://shop.xethru.com/x4m02  

[19] Novelda, X4M02 Radar Sensor Datasheet. Online [Accessed 29 
January 2020]  
https://www.xethru.com/community/resources/x4m02-radar-
sensor-datasheet.115/  

[20] J. Wagner, P. Mazurek, R. Z. Morawski, Non-invasive Monitoring 
of Elderly Persons: Systems Based on Impulse-Radar Sensors and 
Depth Sensors: Springer, Cham, 2022. 

[21] P. Mazurek, J. Wagner, R. Z. Morawski, Use of kinematic and mel-
cepstrum-related features for fall detection based on data from 
infrared depth sensors, Biomedical Signal Processing and Control, 
vol. 40, 2018, pp. 102-110.  
DOI: 10.1016/j.bspc.2017.09.006  

[22] E. L. Allwein, R. E. Schapire, Y. Singer, Reducing multiclass to 
binary: a unifying approach for margin classifiers, Journal of 
Machine Learning Research, vol. 1, 2001, pp. 113–141.  
DOI: 10.1162/15324430152733133  

[23] Mathworks, Matlab Documentation: Statistics and Machine 
Learning Toolbox – ClassificationECOC. Online [Accessed 15 
March 2022]  
https://www.mathworks.com/help/stats/classificationecoc.html  

 

 

http://dx.doi.org/10.1049/iet-rsn.2014.0223
http://dx.doi.org/10.1049/iet-rsn.2014.0214
http://dx.doi.org/10.1109/JSEN.2020.3046991
https://doi.org/10.1109/CCNC.2017.7983096
https://doi.org/10.1109/IICAIET.2018.8638458
https://doi.org/10.1016/j.measen.2021.100270
https://doi.org/10.1109/I2MTC43012.2020.9128760
https://shop.xethru.com/x4m02
https://www.xethru.com/community/resources/x4m02-radar-sensor-datasheet.115/
https://www.xethru.com/community/resources/x4m02-radar-sensor-datasheet.115/
https://doi.org/10.1016/j.bspc.2017.09.006
http://dx.doi.org/10.1162/15324430152733133
https://www.mathworks.com/help/stats/classificationecoc.html