This summarizes a research paper that presents a classification approach called MMSE-CBR to classify physiological parameters of wheel loader operators. It combines case-based reasoning (CBR) with multivariate multiscale entropy analysis (MMSE) to fuse data from multiple physiological sensors. The MMSE algorithm is used to extract features from signals measured by sensors like ECG, finger temperature, skin conductance, and respiration rate. These features are used to form cases in a case library. The CBR approach then classifies new cases by retrieving similar past cases. The approach was evaluated on data from 18 professional drivers and achieved 83.33% accuracy in classifying subjects as "stressed" or "healthy", comparable to an expert's

1. Classification of Physiological Signals for Wheel Loader Operators Using
Multi-Scale Entropy Analysis and Case-Based Reasoning
Shahina Begum1
, Shaibal Barua1
, Reno Filla2
and Mobyen Uddin Ahmed1,3
1
School of Innovation, Design and Engineering, Mälardalen University, Sweden
{firstName.lastName@mdh.se}
2
Emerging Technologies, Advanced Engineering, Volvo Construction Equipment, Sweden
{firstName.lastName@volvo.com}
3
Center for Applied Autonomous Sensor Systems, Örebro University, Sweden
{firstName.lastName@oru.se}
Abstract
Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today,
clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological
sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor could
easily be vulnerable to uncertain noises/interferences in such domain multiple sensors could provide more robust and reliable decision.
Therefore, this paper presents a classification approach i.e. MMSE-CBR that classifies physiological parameters of wheel loader operators
by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm
supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval
(IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR).
Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is
applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE-CBR has
been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the
proposed system that fuses information at data level could classify ‘stressed’ and ‘healthy’ subjects 83.33% correctly compare to an
expert’s classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different
conditions ‘adapt’ (training) and ‘sharp’ (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE-CBR could
support in classification of operators and may be of interest to researchers developing systems based on information collected from
different sensor sources.
KEYWORDS: SENSOR FUSION; CASE-BASED REASONING; MULT-SCALE ENTROPY ANALYSIS; PHYSIOLOGICAL SIGNALS; CLASSIFICATION
1. Introduction
Operating working machines in construction, mining,
agriculture and forestry requires much mental and physical
effort. The efficiency of these machines depends on the
performance of the human operators/drivers. Monitoring and
diagnosing the operators when they are exhausted with
mental/physical workload is important feedback for an
operator, especially a professional operator where an
accident could have large consequences both on lives and
economical costs. However, identification of
mental/physical state and generating alarm due to stress,
sleepiness, fatigue etc. is difficult while driving and still a
scientific challenge. Today, different sensors enable
clinician to determine psychological status with high
accuracy. However, since there are large individual
variations analyzing data from a single sensor source could
deteriorate the classification result. Data that are collected
from multiple sensor sources could provide us more reliable
and robust information of these psychophysiological
parameters. For instance if one sensor measurement is
influenced by a certain noise or interference other sensor
measurements could still support the system. As human
beings, we have the natural ability to fuse signals that are
coming from different sources and supports in reliable and
feature-rich judgment. Using multiple sensor signals to
achieve more reliable assessment of diagnosis this is what
(i.e., naturally performed multisensory data fusion) experts’
are doing in real life while diagnosing these
psychophysiological parameters.
This paper investigates sensor signal fusion in a case-
based classification scheme by means of MMSE algorithm
[27][28]. Here, five sensor measurements i.e., Inter-beat-
Interval (IBI) and Heart Rate (HR) from Electrocardiogram
(ECG), Finger Temperature (FT), Skin Conductance (SC)
and Respiration Rate (RR) HR, IBI signal from ECG, FT,
SC and RR are combined at low-level fusion applying
MMSE algorithm to classify physiological parameters
stressed and healthy of wheel loader operators. The proposed
system has been evaluated with the data collected from
eighteen 18 wheel loader operators. The main goal is to
investigate whether the proposed system MMSE-CBR is
able to classify the operators despite large individual
variations and noises/interferences of the environment.
The rest of the paper is organized as follows: Section 2
presents the background of physiological sensor signals

2. fusion. It also discusses about CBR and entropy analysis.
Section 3, describes related work. Section 4, illustrates the
study design for the data collection. In section 5, the
methods are described in detail. The experimental work is
presented in section 6. Section 7 discusses the evaluation
results. Finally, Section 8 ends with concluding remarks.
2. Background
2.1 Sensor signal fusion
Sensor signal fusion is a method that gives us the resulting
information while using multiple sensors. According to [31],
“Sensor fusion is the combining of sensory data or data
derived from sensory data such that the resulting
information is in some sense better than would be possible
when these sources were used individually.” Commonly,
sensor signal fusion can be achieved either by combining
multiple sensor data sources or data from a single source
over a period of time could also take part in the fusion
process. Signals can be fused or combined in three-level
models [23]:
1) Low-level or Data level fusion combines raw
(unprocessed) sensor data,
2) Intermediate-level or Feature level fusion combines the
representative features extracted from sensor signals
3) High-level or Decision level fusion fuses findings (or
detection probabilities) of multiple sensors.
Multi-sensor information fusion is the process of
integrating data or information from multiple sensors to
improve quality and accuracy of the information that cannot
be obtained using the sources individually. The main
advantage of using data/information from all available
sources is that it helps to enhance the diagnostic visibility,
increases diagnostic reliability and consequently reduces the
number of diagnostic false alarms. Some of the traditional
methods for sensor fusion are: Kalman filter [8], Weighted
decision methods (voting techniques), Neural networks [9],
Clustering algorithms [10], Bayesian inference [15],
Dempster-Shafer’s methods [14]. Information fusion is
available in the vehicle research in many cases for automatic
vehicle control systems to increase reliability, efficiency and
security [8][29][30][32]. However, to our knowledge,
research on multi-sensor data fusion for monitoring or
classifying drivers’ status is limited in the literature. Yet the
greater the necessity of monitoring drivers using multiple
physiological sensors in a reliable and autonomous fashion
the more valuable the area of research would be.
2.2 Case-based reasoning
CBR is a methodology for problem solving and learning.
According to Kolodner [38] “In case-based reasoning, a
reasoner remembers previous situations similar to the
current one and uses them to help solve the new problem”.
So, learning from the past and solving new problems based
on previously solved cases is the main approach of CBR.
The first step in developing a CBR system is to formulate a
case. These cases can be instances of things or a part of a
situation that we have experienced. The case comprises
unique features to describe a problem. In CBR, past cases
are stored in a case library or case base. A reasoning cycle of
CBR with 4 Re-s: Retrieve, Reuse, Revise and Retain is
commonly used to implement such a cognitive model [18].
1) Retrieval it searches the case-library for cases similar to
the problem description and retrieves the most similar cases.
2) Reuse it uses the solution of a previous case. However,
usually the best matching case does not always provide a
complete solution for a new problem case and therefore
adaptation of the solution is often required to use it for a new
case. This adaptation or change of the best matching cases
usually complex and requires domain knowledge.
3) Revise the proposed solution from the reuse step is
evaluated and if necessary repaired in the revise step.
4) Retain the confirmed solution is saved into the case
library as learned case.
In reality, experts in diagnosing human behaviour i.e.,
stress, tiredness, drowsiness etc. rely heavily on their past
memory to solve a new case. Knowledge elicitation is
another problem in such domain, as human behaviour or our
responses to these psychological parameters is not always
predictable. Even an experienced clinician in this domain
might have difficulty to articulate his knowledge explicitly.
Sometimes experts make assumptions and predictions based
on experiences or old cases. In CBR, this elicitation can be
performed with the previous cases in the case base. Thus,
CBR is especially suitable for instance for
psychophysiological stress diagnosis when the domain is
difficult to formalize and is empirical.
2.2 Entropy
Entropy and complexity measures have widely been applied
for the analysis of time series signals. Entropy is introduced
by Shannon [24] for information theory. It is considered as a
generic measure of system disorganization and the basis of
the concept connected to thermodynamics “Any macroscopic
system which is in time t0 in given time-invariant outer
conditions will reach after a relaxation time the so-called
thermodynamic equilibrium. It is a state in which no
macroscopic processes proceed and the state variables of
the system gains constant time-invariant values.” While the
system reaches at thermodynamic equilibrium the entropy of
the system reaches its maximum. Later, in 1984 it has been
applied to a power spectrum of a signal [25]. In signal
processing, entropy shows the irregularity, complexity or
unpredictability characteristics of a signal. The expected
value of information contained in a message can be
quantified by entropy. If X is a single discrete random
variable than the entropy H(X) is measure of its average
uncertainty. Entropy can be calculated using Equation (1)
( ) ( ) ( )∑=
−=
n
i
ii xpxpXH
1
log (1)
Where, X is the random variable with n outcomes that is
and ( )ixp is the probability mass
function of . Equation (1) refers to Shannon Entropy.

3. Entropy has received much attention to quantify
complexity of physiological signals in healthy and diseased
systems. Healthy systems have greater adaptability and
functionality than diseased systems. In many studies it has
been found that disease, aging, drug toxicities degrade the
physiologic information content and reduce adaptive
capacity of the individual. Therefore, loss of complexity
becomes a common feature in pathologic systems analysis
[16] and it can be expressed by the condition that the
complexity of healthy systems is greater than the complexity
of pathologic systems.
A signal with sequentially generated random numbers
has greater complexity and higher entropy value. The
traditional entropy-based algorithms are used to quantify
regularity of time series on a single scale, such algorithms
are Shannon entropy, Kolmogorov-Sinai (KS) entropy,
Approximate entropy and Sample entropy. Pincus has
developed the Approximate Entropy (ApEn) which has
widely been applied in clinical cardiovascular studies [26].
However, traditional entropy methods applied in the analysis
of physiological or biological sensor signals support entropy
estimation only on univariate/single channel data. Costa et
al. [16] have proposed a Multiscale Entropy Analysis (MSE)
and quantify measure of complexity. This complexity, when
evaluated over larger scales, is small for both deterministic
and uncorrelated random signals and large for correlated
(linear and/or nonlinear) stochastic processes. Nevertheless,
increase in entropy does not necessarily always associate
with the increase in dynamical complexity [16]. Researchers
observed that traditional single scale entropy estimate tends
to yield lower entropy in time-series data than for their
surrogate series data. Surrogate series data can be generated
shuffling the original data. The shuffled data are more
irregular and less predictable than the original data and
correlation commonly encompasses several time scales.
Thus, the surrogate data generating process destroys the
correlation and degrades the information content of the
original time series data. MSE which by discovering the
dependency of entropy estimates on multiscale shows that
original time series are more complex than their surrogates.
Also, in the study [16] the authors have found that MSE
strongly separates healthy and diseased groups.
The MMSE, which is an extension of MSE, supports
entropy estimation of multivariate/multichannel data.
Sample Entropy algorithm introduced by Richman et al. [26]
is the basis of the MMSE algorithm. In our proposed system,
for the data level fusion, MMSE algorithm is applied to
measure complexity. A detailed description of MMSE
algorithm is available in [27][28]. Multivariate sample
entropy is a precondition for MMSE analysis. There are two
main steps to calculate the MMSE:
1) Define temporal scales by averaging p-channel time
series using the coarse graining method.
2) Evaluate multivariate sample entropy (MSampEn) for
each coarse grained multivariate data.
3. Related work
Sensor data fusion is becoming an emerging technology. In
the health sciences, synergy between CBR and other
techniques is common [20][21][42][45][46][47][48].
Nevertheless, following are some of the few attempts that
apply fusion techniques in the CBR systems. Case-based
data fusion to improve clinical decision support in coronary
heart disease is reported in [3]. The authors have proposed
three different data fusion models: two models fuse
information at the information retrieval-outcome level and
the other combines data at the knowledge discovery input
level. Performances of the proposed three-model framework
for information fusion are compared with the single source
model. Their study indicates that fusion of information at the
retrieval outcome level outperforms the single source
information. Policastro et al. [2] proposed a hybrid CBR
system for monitoring water quality based on chemical and
physical parameters and algae population. In the system,
three Machine Learning algorithms are employed for the
sensor signal fusion. The evaluation results indicate that the
hybrid system improves the accuracy of the system. A case-
based data fusion scheme is applied in the offshore wind
energy mapping in [3]. Radar scatterometersa has low spatial
resolution but have a sufficient representativeness to get
wind observations. On the other hand, synthetic aperture
radars have high spatial resolution but don’t have enough
representativeness. Therefore, the low spatial resolution and
high spatial resolution data sets from the both sources are
fused together to get a more reliable assessment of wind
resource. Fluid dynamics law and CBR provide additional
information in the process. The fusion generates a wind
resource mapping as a major outcome. Song et al. [4] fuse
the retrieved cases by using the Dempster-Shaffer theory in
the dose plan for prostate cancer in radiotherapy. Here, a
single case might have different dose recommendations due
to variety of uncertainties in decision-making in such cases
multiple cases are fused to get a reliable dose plan for a new
case. Thus, the author argued that this decision level fusion
supports to reduce the conflicts among the oncologists for a
specific case. A data fusion approach in the domain of
business listings using CBR is described in [5]. The paper
shows that combination of CBR, joint probability, and
domain-specific rules improves the result significantly.
In recent years, the area of sensor data fusion is evolving
and researchers are applying new methods/techniques to
introduce sensor fusion in various domains. Entropy
estimation technique has been applied in sensor fusion for
example in [11][12[13][22]. Besides, entropy estimation has
widely been applied in clinical studies including biological
signal processing [17], breast cancer diagnosis and EEG
signal analysis of sleep stages [19].
4. Study design and data collection
Psychophysiological measurements of wheel loader
operators have been collected in two phases; 1) during
conducting a Psychophysiological Stress Profile (PSP) and
2) during operating a wheel loader. During the study each
operator was given an exclusive 2.5 hours session, starting

4. with the Psychophysiological Stress Profile (PSP) described
in subsection 4.1. Afterwards, the machine testing was
performed, with the traction force setting as the independent
variable. In order to minimise the skewing influence of
learning effects, each operator was given ten minutes’ self-
training (called “adapt”) to familiarise himself with the
specific characteristics of the current machine setup before
five minutes of live test-driving (called “sharp”).
(a) Right hand controls
(b) Left hand controls
Fig. 1. Sensor placement on both a) right and b) left wrists and
fingers and in the nose [34]
Altogether, eighteen people have been participated as
test operators in this study – all male, mostly in order to
exclude possible skewing due to gender differences. The
operators were pre-selected according to our preliminary
judgment of their wheel loader operating skills, ranging from
professional experts to less experienced operators. In our
study the psychophysiological measurements were
conducted using the cStress software from PBM
Stressmedicine Systems, running on a second laptop
installed in the cab of the wheel loader. FT, HR (via ECG)
and SC were acquired using a C2 physiological monitoring
system from J&J Engineering and a LifeSense AirPas
oxycapnograph handled data acquisition and pre-analysis of
the operator’s respiration. The ECG sensors were placed on
both wrists (see Fig. 1(a) and Fig. 1(b)), using isotonic gel.
These sensors needed to be secured against relative
movement in order to avoid data artefacts. Reliable data
could be acquired when using the suspended chair’s
integrated arm rests to support the operator’s lower arms,
despite the operator in a wheel loader cab otherwise being
exposed to vibrations and accelerations of various
frequencies and magnitudes.
4.1. Using Psychophysiological Stress Profile (PSP)
approach
Each human being has an individual response to workload,
which necessitates a specific reference or calibration in order
to be able to correctly evaluate the results of psychophysio-
logical measurements. While not common procedure in the
research community, establishing a PSP has proven to be
valuable in clinical work in patients with stress-related dys-
functions.
The PSP shown in Table 1 is essentially taken from [35]
and has been implemented in the cStress software. It
contains 15 minutes of data recording, guiding the patient
(or in our case the wheel loader operator) through six steps.
Table 1. Psychophysiological Stress Profile (PSP)
# Designation Observation
time
Description
1 Base line 3 min Relaxed silent reading of a
neutral text
2 Deep
breathing
2 min Deep breathing under guidance,
approx. 6 bpm
3 Nonverbal
stress
2+2 min Two periods of thinking about a
stressful situation, feedback and
guidance in between
4 Relaxing 2 min Relaxing with closed eyes,
normal breathing
5 Math stress 2 min Counting aloud backwards from
2500 in steps of 7
6 Relaxing 2 min Relaxing with closed eyes,
normal breathing
4.2. Using training and testing wheel loader
The measurements used in our research were conducted on
operators of a wheel loader performing bucket loading of
gravel. This is a very typical application for such a working
machine with a cycle time of 25-35 seconds, depending on
working place setup and how aggressively the operator uses
the machine. A significant amount of training is required for
operators to use a wheel loader efficiently. Even for
experienced professionals operating such a working machine
for several hours is exhaustive, as it involves both physical
and mental workload. Even though the operator sits still and
the controls do not demand a large amount of power the
operator has to actively control the system all the time and
maintain situational awareness.
In this study we have focused specifically on the bucket
filling phase. Here the challenge for the operator is to
balance traction force on the wheels and bucket forces from
the hydraulic lift and tilt functions and coordinate the
motions in order to fill the bucket fast and efficiently. A
detailed description of wheel loader working cycles, together
with the challenges they present for machine and operator,
can be found in [34]. In order to increase the effect of these
challenges the wheel loader’s control software was modified
to limit engine speed, and thus traction force, during bucket
filling.
In the most severe modification, engine speed was
limited so that maximum traction force was reduced to 47%,
which is below what was needed for the working task to be

5. performed efficiently. Any operator should have experienced
this as clearly negative, with hypothetically a higher
workload as a result.
In the moderate scenario, traction force was limited to
62%, which is comparable to what expert operators required
on average for this particular working task. It should have
been hardly noticeable for an expert, but does provide
advantages such as prevention of wheel spin to less
experienced operators.
The technical aspects are described in more detail in
[33]. In both conditions, 47% and 62% setup, the
counteracting effect of the traction force is clearly less
pronounced so that even at maximum use of the traction
force available the lifting force is never cancelled out
completely. This should be experienced positively,
especially by less experienced operators (but the question is
whether this positive aspect outweighs the artificial
limitation of maximum available traction force).
During all testing sessions various machine data were
also recorded off the wheel loader’s CAN bus, enhanced by
additional data, either calculated or acquired from externally
mounted sensors. All tests were also recorded on video using
an externally placed digital video camera and later
synchronised with the acquired data from the CAN bus and
the psychophysiological measurements.
5. Methods
The methods are presented in two-folds, 1) presents the
proposed approach i.e. MMSE-CBR and 2) other traditional
CBR approaches.
5.1. The proposed approach (MMSE-CBR)
In order to perform classification of physiological signals for
wheel loader operators, the proposed approach combines
MMSE and CBR. Here, the MMSE helps to perform a signal
level fusion and the CBR approach supports in classification
by retrieving most similar cases. A case is formulated with a
feature vector and features are extracted by applying MMSE
where the algorithm fuses a number of sensor signals. The
steps in the proposed system are presented in Fig 2.
In the proposed system, MMSE algorithm is applied on
five sensor signal measurements (i.e., HR, IBI, FT, SC and
RR) to quantify complexity of the sensor signals. So, the
sensor signals are sensing from the operators as presented in
Step1. In Step2 a pre-processing is done on each sensor
signal before the signals are fused by the MMSE algorithm
and the preprocessing of data is discussed in 5.1.1.Step3 runs
the MMSE algorithm and it considered a scale factor up to
10 levels. Step4 and Step5 are used to extract features and
formulate a case for CBR approach. The description of
MMSE, feature extraction and case formulation are
presented in section 5.1.2. Finally, Step6 is the CBR
classification, here case similarity is calculated and based on
similarity value the most similar case is retrieved. The
solution of the retrieved case is considered as classification
for the new case, the details of case similarity and retrieval
are presented in 5.1.3.
5.1.1 Data pre-processing
Before feature extraction to handle artifacts we have applied
the k-nearest neighbors (K-NN) based interpolation
algorithm described in [40] in the data sets to handle
artifacts. This algorithm works in two steps: first it detects
artifacts from the signal which is done mainly based on the
windowing technique and in the second step interpolation is
performed using K-NN algorithm to replace artifacts from
the signal.
In the proposed CBR system, in PSP data set, each case
consists of 10 features extracted from the 15 minutes signal
recordings. An expert has classified all the reference cases.
The 15 minutes recoding contains six sessions data which
are baseline, deep breathing, verbal stress, relax, math stress
and relax [35]. As the scale factor in MMSE depends on the
length of data set (changes become significant for a large
datasets) for a reliable estimation we have considered the
whole 15 minutes recording instead of taking session-by-
session data.
In the training and testing wheel loader i.e., sharp and
adapt phases, for each case there are three data sets (for three
different traction force settings). The adapt data set consists
of three 10 minutes recoding and sharp data set contains
three 5 minutes recoding. For each sharp and adapt cases, to
extract features the three data sets are combined. Now, the
O
P
A
R
A
T
O
R
O
R
U
S
E
R
Step1: Sensor signals
FT IBI HR RR SC
Step2: Pre-processing
The signals are processed to be equal
in length and cleaned by using a k-NN
based artefact handling algorithm.
Step3: Sensor fusion
A data level fusion has been conducted
using MMSE algorithm where 10 factor
scales is considered.
Step4: Feature extraction
Output from the MMSE algorithm
provided a feature vector with 10
entropy values as features.
Step5: Case formulation
A new problem case is formulated
considering extracted features by
MMSE algorithm.
Step6: CBR classification
A most similar previous case is
retrieved & the classification of the
previous case is considered.
Sensing 5
signals
User is notified
about the
classification
Fig. 2. Steps of the proposed MMSE-CBR classification system

6. adapt data set has total recoding length of 30 minutes and
sharp has 15 minutes recording.
For the comparative study, we need data with same
length. Therefore, the adept’s signal recording length is
downsized to 15 minutes to make it same as sharp. In adapt,
each recording length is 10 minutes. This 10 minutes data set
is then divided into two 5 minutes and finally, mean of the
two 5 minutes recording has been calculated for each
subject’s recording. Thus, there is 15 minutes data set for
adapt cases to extract features using MMSE analysis. Like
the PSP data set, for the sharp and adapt data sets, features
are extracted up to the scale factor 10.
5.1.2 Feature extraction and case formulation based on
MMSE
In the MMSE analysis, the first step is to define temporal
scales by averaging p-channel time series using the coarse
graining method (see Equation 3) and then in the second
step, evaluate MSampEn for each coarse grained
multivariate data. To calculate MSampEn the algorithm
forms a composite delay vector from the coarse grained data
and it’s two important embedding parameters are mk and τk.
A detail description is available in [27][28].
In this study, for MMSE analysis, we consider the
embedding vector parameters, mk=2 and τk=1. The coarse-
grained process is obtained by the following Equation (2)
( ) εε
ε
ε
ε N
jwhεrεab
j
ji ikjk ≤≤= ∑ +−=
1
1
11 ,, (2)
Where, N is number of data points in every channel,
{ } pka
N
iik ........2,11, ==
, is a p-varieties time series, 𝛆𝛆 is the
scale factor, k =1,….,p is the channel index and ε
jkb ,
is the
coarse-grained data.
The MMSE analysis returns a linear vector based on the
scale factor. To calculate MSampEn, for each p-variate time
series a composite delay vector has been constructed using
Equation (3)
( ) ( )
( ) ( ) ppp mipipipmi
iimiiim
aaaa
aaaaaia
τττ
τττ
1,,,1,2
,2,21,1,1,1
,,,,,
,,,,,,
22
2111
−++−+
+−++=
2222
22
(3)
Where M = [m1, m2, m3,….,mp] ∈ Rp
is the embedding
vector, = [ 1, 2,…… p] the time lag vector and composite
delay vector ( ) m
m Ria ∈ , where ∑ =
=
p
k kmm 1
Estimate of MSampEn is presented in Equation (4)
( ) ( )
( ) 





−=
+
rB
rB
NrMMSampEn m
m 1
ln,,,τ (4)
Where M is embedding vector, is time lag vector, r is
threshold and N is multivariate time series Bm
and Bm+1
are
the frequency of occurrence for the length m and m+1
respectively.
The scale factor is highly dependent on the length of data.
However, the MMSE estimates are consistence for data
length N ≥ 300 [27]. Here, for the croase-grained process the
scale factor is considered 10 because of at the scale 10 the
shortest data series has greater than 300 data points. An
example of the coarse-grained process up to scale factor 2 is
shown in Fig. 3. As we measure MMSE for scale factor up
to 10 it returns a vector of length 10 as a result of MMSE
estimation for each recording. Thus, these 10 values are
considered as the features for each recording.
Fig. 3. Illustration of coarse-grained process in MMSE for scale
factor 2
5.1.3 Similarity matching and case-based retrieval
In the proposed system, the similarity of a feature between
two cases was measured using modified Euclidean distance
and fuzzy similarity. However, the experimental work has
been conducted using fuzzy similarity function. The modified
Euclidean distance function to calculate similarity of a
feature between two cases is shown in Equation (5),
(5)
Where, Ti and Si are feature values of the target and source
cases respectively. The function returns 1 if the values are
same and returns 0 if the values are completely dissimilar.
In fuzzy similarity, a triangular membership function
(mf) replaces a crisp value of the features for new and old
cases with a membership grade of 1. In both the cases, the
width of the membership function is fuzzified by 50% in
each side. Fuzzy intersection is employed between the two
fuzzy sets to get a new fuzzy set which represents the
overlapping area between them.
(6)
The similarity between feature values of the old case (Sf)
and the new case (Tf) is now calculated using Equation (6)
where m1, m2 and om is the area of each fuzzy set [36].
The similarity between two cases is measured using the
average of all the features that are to be considered. The
function for calculating the similarity between two cases is
presented in Equation 7.
),(*),(
1
ff
n
f
f STsimwSTSimilarity ∑=
= (7)
Where T is a current/target case, S is a stored case in the case
base, n is the number of the attributes/features in each case, i
is the index for an individual attribute/feature and sim (Ti, Si)
is the local similarity function for attribute i in cases T and S.
It is noted that here for all the extracted features weight
values are equal.
5.2. Traditional CBR approaches
In the CBR system for single data source, the features are
extracted considering each of the signal (i.e., IBI, FT, SC

7. and RR). From the IBI signal, Heart rate variability (HRV)
analysis is considered and features are extracted in both time
and frequency domains. Here, statistical analysis and Fast
Fourier Transformation are considered and the feature
extraction process is presented in our previous study in [39].
Similarly, features for RR are also obtained in both time and
frequency domains. In time domain, a mean respiration rate
is calculated as feature. Also a Dominant respiration
frequency (DRF) [41] analysis has been considered in the
frequency domain for the feature extraction. In order to
extract features from FT and SC, a derivative of each step
i.e. “degree of changes” is introduced as a measurement
[36]. The difference between FT and SC is that when FT
increases than SC decreases and vice versa. Therefore, the
SC features are also calculated using the same method as FT.
For CBR classification, each signal is classified separately
by considering fuzzy similarity as local similarity and k-NN
as global similarity as discussed earlier.
However, the CBR approach for multi-source signal two
fusion methods (i.e. considering maximum similarity known
as voting techniques and weighted average similarity) are
considered at the decision level. For a given case, 1st
a
similarity value for each signal source is calculated
separately and then fusion method is applied as a 2nd
level
similarity calculation. The method maximum similarity
provides the maximum similarity value among the four
similarity values. The function is expressed as in Equation
(8).
sim S = max(α, β, γ , δ) (8)
Here, considering a target case T and the stored case S, the
similarity values of HRV, FT, SC and RR features are α, β, γ
and δ respectively obtained from the CBR classification.
Similarly, for the method weighted average, some weights
have been considered between scale 1 to 10 for each
individual similarity value of HRV, FT, SC and RR based on
the individual classification accuracy rate presented in the
evaluation section. The weighted average method can be
expressed by the Equation (9).
(9)
Here, w is the weights, multiplied with associated similarity
value and sum of the multiplied similarity values are divided
by the sum of weights. In the both methods, the new
similarity value is used in the final CBR retrieval for
classification.
6. Evaluation
The classification accuracy has been evaluated considering
the 2 data sets as discussed earlier; 1) PSP data set and 2)
training and testing wheel loader data set i.e., sharp and
adapt. An evaluation of CBR system classification
integrating sensor fusion (MMSE-CBR) has been conducted
using each data set. Finally, the proposed approach is
compared with other single source system and fusion
methods i.e. 3) MMSE-CBR and other single source
parameters and fusions. Note that, only PSP data have been
classified by an expert as in stressed and healthy classes. The
training and testing wheel loader data set have not been
classified by the expert. However, the data are controlled
and separated by training and testing conditions.
6.1. Using the PSP data set
Here, the experimental work has been conducted in two
folds; a) observation of the MMSE analysis and b)
observation of the MMSE-CBR classification.
a) Observation of MMSE analysis: In the experimental
work, all the 5 sensor signals are fused and computed the
entropy complexity up to scale factor 10. An average of
entropy complexity value for each scale factor is calculated
both for the stress and healthy cases. Fig. 4 shows the
average of MMSE analysis for 7 healthy and 11 stressed
cases (classified by an expert) [37].
Fig. 4. Average of MMSE analysis and error bars the standard
deviation for 7 healthy and 11 stressed cases
As can be seen from the figure, stressed cases possess a
lower complexity value than the healthy cases. However, it
is not always true for individual recordings. Fig. 4 showed
the MMSE analysis results for the 18 cases and it can be
seen that MMSE varies a lot depending on individuals. This
shows clearly the necessary and motivation of applying CBR
approach since the CBR can work well when there is a large
variation in the cases.

8. Fig. 5. MMSE analysis for 18 cases
b) Observation of MMSE-CBR classification: The main
goal of this experimental work is to see the classification
accuracy of the MMSE-CBR approach compare to the
expert. Here, the experimental work has been carried out
with 18 cases where 7 of them are classified as ‘healthy’ and
11 of them are classified as ‘stressed’ by an expert. For the
the evaluation, the retrieved top 2 most similar cases are
considered i.e., if both the query and one of the two retrieved
cases are belongs to a similar class then the number of
correctly classification of cases is counted as 1. In Fig.4 the
weight values for all extracted features are set as follows: 1
for scale 1, scale 2 and scale 4; 8 for scale 3 and scale 9; 10
for scale 5, scale 6, scale 8 and scale 10; and 5 for scale 7.
Two similarity functions Euclidian distance and Fuzzy
similarity functions are applied for the case matching. The
comparison results of these two similarity methods are
presented in detailed in previous paper [22]. It has been
observed from the result in [22], the CBR approach performs
better classification while considering fuzzy similarity
function. Table 2 depicts the summary results of the
classification accuracy by MMSE-CBR considering the
fuzzy similarity function.
Table 2. Percentage of correctly classified cases by MMSE-CBR
considering fuzzy similarity function and PSP data
Case classes
MMSE-
CBR
11 stressed cases 100.0%
7 healthy cases 57.10%
Total on 18 cases 83.33%
In Table 2, the results of MMSE-CBR considering
fuzzy similarity function achieved 100% for stressed cases,
≈ 57% for healthy cases and in total the accuracy reaches ≈
83%.
6.2. Using training and testing wheel loader data set
This experimental work has been carried out same as
previous experiment considering a) observation of the
MMSE analysis and b) observation of the MMSE-CBR
classification. The only difference is that here the expert
does not classify the cases rather they are divided into two
classes; sharp and adapt according to the condition in the
data collection.
a) Observation of MMSE analysis: In this experiment, 3
sharp measurements (i.e. using 47%, 62% and 100% traction
force) are combined to get one 15 minutes length sharp case
for each subject. Again, one 15 minutes length adapt case
for each subject is produced as discussed earlier in (section
5.1.1). Then, all the 5 sensor signals for each adapt and
sharp data are fused and computed the entropy complexity
up to scale factor 10. An average of entropy complexity
value of each scale factor has been calculated both for adapt
and sharp cases. The results for the MMSE analysis together
with error bar considering adapt and sharp cases are
presented in Fig. 6.
Fig. 6. Average of MMSE analysis and error bars the standard
deviation for adapt and sharp conditions
As can been seen in Fig. 6, the MMSE algorithm can
fairly distinguish between sharp and adapt cases while
considering the average entropy complexity value.
Moreover, it shows that the complexity value is higher in
adapt cases and comparatively lower in sharp cases. The
contextual information is that during adapt condition the
operators are getting training and during sharp condition
they were in real life testing. However, this result is not true
if the analysis is done within subjects. Such analysis
observation is presented in Fig. 7.

9. As can be seen from Fig. 7, the multivariate sample
entropy values for scale factor 10 varies for adapt and sharp
for most of the subjects and the entropy values of adapt
measurements for 13 subjects out of 18 are higher than
sharp measurements. According to the individual subjects
entropy value, the sharp and adapt shows totally different
for most of the cases.
b) Observation of MMSE-CBR classification: The main
goal of this experimental work is to see the classification
accuracy of the MMSE-CBR approach i.e. how accurate the
MMSE-CBR approach can distinguish the cases between
sharp and adapt. Here, 18 sharp + 18 adapt =36 total cases
is considered and same as previous experiment retrieved top
2 most similar cases and fuzzy similarity function in
MMSE-CBR have been considered. The weight values for
all the extracted features between the scale factor 1 and 7
are set to 1. However, the weight value was higher for the
scale factor between 7 and 10, i.e. the scale factor 8 is set as
8 and scale factor 9 and 10 are set as 10. The summary of
the results are presented in Table 3.
Table 3. Percentages of correctly classified cases by MMSE-CBR
considering fuzzy similarity function and adapt/sharp data
Case classes
MMSE-
CBR
18 adapt cases 77.78%
18 sharp cases 94.40%
Total 36 cases 83.3%
Fig. 7. Individual subjects’ MMSE analysis result for adapt and sharp condition

10. It can be seen from Table 3, considering top two similar
cases, ≈ 78% of adapt cases are correctly classified as adapt
and ≈ 94% of sharp cases are correctly classified as sharp. In
total i.e. out of 36 cases, the percentage of correctly
classified cases is achieved around ≈ 83% for both the
conditions.
6.3. MMSE-CBR and other single source parameters
and fusions
This experiment aims for comparison of the proposed
method and the other approaches. For example, CBR is used
in the classification either considering a single source
measurement i.e. finger temperature or multiple source
measurement with maximum or weighted average similarity
functions discussed earlier in 5.2. Here, again the
experiment compares the proposed approach MMSE-CBR
considering the two data sets i.e. a) PSP data set and b)
training and testing wheel loader data set
a) Using Psychophysiological Stress Profile (PSP) data
set
In PSP, 18 driver’s data have been classified in two
classes (i.e. stressed and healthy) by an expert. At first, the
CBR with fuzzy similarity function has been for
classification on each single parameter i.e. FT, HRV, SC
and RR. The classification accuracy has been summarized in
Table 4.
Table 4. Classification accuracy on PSP data (i.e. stressed/healthy)
considering CBR with single parameter vs MMSE-CBR. Here,
HRV=Heart Rate Variability, FT=Finger Temperature,
RR=Respiration Rate and SC=Skin Conductance.
Case classes
CBR
with
HRV
CBR
with
FT
CBR
with RR
CBR
with SC
MMSE-
CBR
11 stressed cases 100.0% 90.90% 81.81% 100.0% 100.0%
7 health cases 57.10% 71.4% 85.7% 42.8% 57.10%
Total 18 cases 83.33% 83.33% 83.33% 77.77% 83.33%
As Table 4 presents, MMSE-CBR, CBR with HRV,
CBR with FT and CBR with RR have the same
classification accuracy i.e. ≈ 83% for total 18 cases.
However, the classification accuracy is low for all of them
while considering the healthy cases. Nevertheless, the
classification accuracy is 100% for most of them while
considering stressed cases.
Table 5. Comparison results of correctly classified cases using
MMSE-CBR and other fusion approaches on PSP data i.e.
stressed/healthy. Here, ‘MAX Similarity’ considers maximum
similarity value, ‘WA Similarity’ considers weighted average of all
the similarity values.
Case classes
CBR
(MAX
Similarity)
CBR
(WA
Similarity)
MMSE-
CBR
11 stressed cases 72.22% 100.0% 100.0%
7 health cases 85.7% 42.8% 57.10%
Total 18 cases 77.77% 77.77% 83.33%
It can be seen from Table 5, both for the CBR with
maximum similarity and weighted similarity, the
classification accuracy is achieved ≈ 78% whereas using the
proposed approach the classification accuracy is achieved ≈
83%. Nevertheless, the classification accuracy in healthy
class is relatively lower for all the CBR systems and better
for the stressed class. Here, MMSE-CBR and CBR with
weighted similarity have achieved 100% classification
accuracy.
b) Using training and testing wheel loader data set i.e.
sharp and adapt.
Same as the above experiment, this also evaluates the
proposed MMSE-CBR method with other CBR approaches,
for example, CBR system with HRV. Table 6 presents the
classification accuracy in percentage considering both single
source sensor measurements in CBR and multiple sensor
sources in MMSE-CBR.
Table 6. Classification accuracy for adapt/sharp data considering
CBR with single source measurement vs MMSE-CBR. Here,
HRV=Heart Rate Variability, FT=Finger Temperature,
RR=Respiration Rate and SC=Skin Conductance.
Case classes
CBR
with
HRV
CBR
with
FT
CBR
with
RR
CBR
with
SC
MMSE-
CBR
18 adapt cases 77.7% 88.8% 66.6% 83.3% 77.7%
18 sharp cases 77.7% 55.5% 72.2% 50.0% 94.4%
Total 36 cases 77.7% 72.2% 69.4% 66.6% 83.3%
It can be seen from Table 6, considering the all 36 cases,
MMSE-CBR is achieved ≈ 83% classification accuracy and
CBR with HRV is achieved ≈ 78%. However, for the sharp
cases, MMSE-CBR has achieved it’s accuracy as ≈ 94%. In
addition, the others i.e. CBR with FT, CBR with RR and
CBR with SC have shown comparatively lower
classification accuracy. Nevertheless, MMSE-CBR
approach shows better performance in terms of
classification accuracy against the other CBR decision
fusion methods i.e. CBR with maximum similarity and CBR
with weighted average similarity at decision or high-level.
For the both decision fusion methods, the achieved
percentage of correctly classified cases is 72% and 83% for
MMSE-CBR as presented in Table 7.
Table 7. Comparison results of correctly classified cases using
MMSE-CBR and other fusion approaches on adapt/sharp data.
Here, MAX Similarity=considers maximum similarity value, WA
Similarity=considers weighted average of all the similarity values.
Case classes
CBR
(MAX
Similarity)
CBR
(WA
Similarity)
MMSE-
CBR
18 adapt cases 66.66% 88.88% 77.7%
18 sharp cases 77.77% 55.55% 94.4%
Total 36 cases 72.22% 72.22% 83.3%
7. Experimental results and discussions
As discussed in the previous section several experimental
works have been carried out to evaluate the performance of
the proposed system that employs sensor fusion method in a

11. case-based classification system for the wheel loader
operators. Since, in reality clinicians make decision based
on effectively fusing the information collected from
different physiological sensor sources the main goal was to
investigate whether in such classification systems it could
also classify the operators correctly while combining
information from different sources.
In the proposed system, the MMSE algorithm used for
sensor signal fusion was able to discriminate between
stressed and healthy subjects. Fig.4 shows a better
separation of MMSE curves for stressed and healthy
subjects. Further, when assessing the relative complexity of
stressed and healthy subjects it indicates that the complexity
index (area under the MSE curve) is lower for stressed
subjects than for the healthy subjects. This supports the
multiscale complexity loss theory with aging and disease or
when a system is under constraints [44]. The MMSE
analysis on individual subjects (see Fig. 5) reveals that the
individual variation is higher and the underlying complexity
analysis does not explicitly distinguish the separation.
However, data level sensor fusion using MMSE in the
proposed CBR system provides individual classification
with an average accuracy of 83% compare to an expert’s
classification. The accuracy is significantly higher (100%)
for stressed subjects than for healthy (57%) subjects. This
could be due to limited number (7) of available healthy
cases into the system that reduces the probability of
retrieving healthy cases. The evaluation results using
another set of data which were not classified by expert
rather than taken from a controlled study [34] demonstrate
that MMSE distinguishes the ‘sharp’ and ‘adapt’ cases and
error bars in Fig 6 explain this separation more efficiently. It
also reveals that ‘sharp’ (real life driving) exhibits lower
complexity than ‘adapt’ (training). MMSE analysis for
individual subjects in Fig. 7 illustrates that in scale factor 10
multivariate sample entropy values varies for most of the
subjects and the complexity is higher for adapt (training)
cases for most of the subjects (13 out of 18 subjects). The
proposed CBR classification system using sensor fusion
classifies the cases with 83.3% accuracy where 94.4%
correctness has been achieved for sharp cases.
Yet, another study has been conducted to evaluate
performance of the system by using sensor fusing process
compare to the measurements of the individual sensor
sources. It can be observed that the proposed multi-sensor
fusion approach in CBR (MMSE-CBR) performs at least as
well as or better than the best single sensor (see Table 4 and
Table 6). In addition, two methods (section 5.2) namely
maximum similarity and weighted average allow higher-
level or decision level fusion which has also been tested in
the CBR system. Table 5 and Table 7 depict that among the
three approaches the proposed low or data level multi-sensor
fusion approach in CBR (MMSE-CBR) out performs the
others.
8. Summary and conclusions
This paper presented a new approach for multi-sensor fusion
in a CBR system for classification of wheel loader operators
based on 5 sensor measurements i.e., IBI, HR, FT, SC and
RR. Multivariate and mult-scale entropy was applied to
perform a low level or data level sensor fusion in the
system. In the proposed system, the features extracted from
the entropy estimation were used to formulate cases in a
CBR system. The result shows that the system could classify
‘stressed’ and ‘healthy’ subjects 83.33% correctly compare
to an expert’s classification. Moreover, in another study the
achieved accuracy (83.3%) indicated that it could also
classify two different conditions ‘adapt’ (training) and
‘sharp’ (real-life driving) for the wheel loader operators.
Even though we have observed that sensor fusion process
provided better or same classification accuracy than the best
individual signal source we could expect a number of
benefits over single source systems. Being able to cover the
same domain with different sensor measurements the fusion
process in such domain could provide more reliable decision
and increase diagnosis confidence. So, the system described
here could yield significant benefit in applying decision
support is such domain for monitoring operators and could
provide a good means for sensor signal fusion in other
health care domains.
Acknowledgments
The authors would like to acknowledge the Swedish
Knowledge Foundation (KKs) and Volvo Construction
Equipment AB, Sweden for their support of this research.
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