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Assessment of Biofeedback Training for Emotion Management Through
Wearable Textile Physiological Monitoring System
Article in IEEE Sensors Journal · December 2015
DOI: 10.1109/JSEN.2015.2470638
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IEEE SENSORS JOURNAL, VOL. 15, NO. 12, DECEMBER 2015
7087
Assessment of Biofeedback Training for Emotion
Management Through Wearable Textile
Physiological Monitoring System
Wanqing Wu+ , Heye Zhang+ , Sandeep Pirbhulal, Subhas Chandra Mukhopadhyay, Fellow, IEEE,
and Yuan-Ting Zhang
Abstract— Negative emotion has a wide range of pernicious
impacts on people, ranging from the failure in real-time task
performance to the development of chronic health conditions.
An unobtrusive wearable biofeedback system for personalized
emotional management has been designed and presented in
this paper. The system integrated heart rate variability (HRV)
biofeedback to wearable biosensor platform, which could
function both as an early stress warning system as well as
a visual interface to manipulate subject’s affective state. The
designed and developed system would help subject to transform
the negative emotion state into positive through real-time HRV
biofeedback training. The results indicated that the real-time
HRV biofeedback is significantly effective in cases of negative
emotion. With the aid of the developed biofeedback system, the
subhealth subjects could transform heart rhythm from negative
emotion to positive emotion-related oscillation mode.
Index Terms— HRV biofeedback, wearable biosensor, heart
rate variability, heart rhythm pattern, emotional management.
I. I NTRODUCTION
IOFEEDBACK has been described as a ‘psychophysiological mirror’, allowing the patients to monitor and
learn from physiological signals produced by the body [1], [2].
The clinical efficacy of biofeedback has been investigated
in a range of psychiatric disorders, including anxiety [3],
depression [4], and schizophrenia [5]. Biofeedback was
B
Manuscript received June 15, 2015; revised August 9, 2015; accepted
August 10, 2015. Date of publication September 2, 2015; date of
current version October 8, 2015. This work was supported in part
by in part by the Key Laboratory for Health Informatics, Chinese Academy of Sciences, and in part by the Science Technology and Innovation Committee of Shenzhen for Research Projects
(CXZZ20140909004122087 and JCYJ20140901003939025). The associate editor coordinating the review of this paper and approving
it for publication was Prof. Ignacio R. Matias. (Wanqing Wu and
Heye Zhang contributed to this work equally.) (Corresponding author:
Subhas Chandra Mukhopadhyay.)
W. Wu, H. Zhang, and S. Pirbhulal are with the Institute of Biomedical and Health Engineering, and also with the Key Laboratory for
Health Informatics in Shenzhen Institutes of Advanced Technology Chinese
Academy of Sciences, Shenzhen 518055, China (e-mail: wq.wu@siat.ac.cn;
hy.zhang@siat.ac.cn; sandeep@siat.ac.cn).
S. C. Mukhopadhyay is with the School of Engineering and Advanced
Technology, Massey University, Palmerston North 4442, New Zealand
(e-mail: s.c.mukhopadhyay@massey.ac.nz).
Y. T. Zhang is with the Key Laboratory for Health Informatics, Shenzhen
Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen
518055, China, and also with the Joint Research Centre for Biomedical
Engineering, Department of Electronic Engineering, The Chinese University
of Hong Kong, Hong Kong (e-mail: ytzhang@ee.cuhk.edu.hk).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/JSEN.2015.2470638
found to be cost-effective on all dimensions reviewed with
cost/benefit ratios ranging between 1:2 and 1:5, which
provided high-efficiency in clinical settings, such as the reduction in medical care costs to patients, decrease in frequency
and duration of hospital stays and re-hospitalization, more
important, biofeedback treatment was verified for decreasing in mortality and increasing in quality of life [6], [7].
Despite of the 27% of people in Europe suffering from mental
health problems each year (Lancet Global Mental Health
Group, 2007), 74% of these people receive no pharmaceutical
or traditional psychological treatment from mental health-care
services, often due to multiple barriers in accessing such
services [1]. For example, previous biofeedback treatment can
be very expensive because it might require a large amount of
training and practice under the professional supervision. Such
treatment processing can hardly conduct by the people in home
environment. Although physiological measurement devices
have been widely used in clinical biofeedback settings for
many years, little work has focused on automated stress interventions under the non-clinical environment. Hence, different
types of biofeedback strategies, such as heart rhythm coherence feedback, oscillatory feedback and resonance frequency
training feedback (RFT) [8]–[13] have recently been used for
self-regulation of central and autonomic nervous system. And,
more and more researches have been dedicated to raise living
quality in terms of health through designing and fabricating
intelligent sensors that are characterized by unobtrusive and
non-invasive. Advances in the fields of electronics and instrumentation technology, together with novel textile materials and
novel textile-electronic integration techniques have boosted the
development and implementation of garments based on sensors
for wearable bio-signal measurement systems [1]. As a result
of extensive and numerous research efforts, several wearable
and textile-enable monitoring systems for the performance
of non-invasive measurements have been designed and
implemented. For example, a Zigbee smart noninvasive
wearable physiological parameters monitoring device use
biofeedback is developed to monitor physiological parameters,
such as temperature and heart rate. The system consists of
an electronic device that is worn on the wrist and finger,
by an at-risk person [14]–[16]. To assess the elderly activities
at home in real-time, a low cost, robust, flexible and efficiently
home monitor system is developed using Wireless Sensor
Network (WSN) [15], [17]–[19]. The simplicity of binary
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IEEE SENSORS JOURNAL, VOL. 15, NO. 12, DECEMBER 2015
Fig. 1.
Architecture of proposed biofeedback system.
passive infrared (PIR) sensors allowed us to collect the
movements of the elderly, which are supportive in the daily
living (ADL) assessment and the subsequent wellness determination process [20]–[23] Also experiments showed visual
relation between emotions (i.e. happy, sad, angry and neutral)
and physiological signals, which are obtained from a heart
rate sensor, a skin temperature sensor and a skin conductance
sensor [24], [25]. With the increasing availability of sensors
and methods for detecting stress, some unique features of
unobtrusive and wearable devices due to recent advances in
sensing, networking and data fusion have broadened the range
of biofeedback application. It is has become one trend to integrate intellectual sensors and biofeedback theory to exercise
stress management. Consequently, the studies on the integration of wearable technology and biofeedback procedures for
psychiatric disorders is one undoubtedly demand to overcome
the limitation of conventional biofeedback treatment.
Hence, the purpose of this study was to implement a new
biofeedback methodology for personalized stress management,
which is based on theoretical basis of biofeedback strategies
and the construction of an unobtrusive wearable biosensor
platform, the block diagram of system architecture is displayed
in Fig.1.
Given that the primary application of the system is to help
the user to self-correct and regulate his/her physiological functions both in static and dynamic conditions, the requirements
taken into account for the system design are: i) wearability
and unobtrusiveness; ii) low-power consumption and low-cost;
iii) flexibility and easy integration with other sensors and
actuators; iv) easy maintenance and update of components; and
v) reliability. Through the convenient, reliable and accurate
wearable platform, we can instruct the person to take the
proper action to release the stressful mental functions using
collected multi-scenario physiological data and biofeedback
theory.
II. S YSTEM D ESIGN
The model of our biofeedback methodology is built on
the theoretical basis of the biofeedback closed loop model,
autonomic cardiovascular control model. Furthermore,
respiration training closed loop is also incorporated to
theoretical model. The structure of proposed theoretical
model in our biofeedback methodology is illustrated in
the Fig.2.
This proposed three-layer system model is directly
associated with three interventions involved in biofeedback
Fig. 2.
The theoretical three-layer closed loop model of proposed system.
Fig. 3. The implemented principle framework of proposed theoretical model.
closed loop model: human, machine and environment
(individual/external environment), and accordingly brought out
three challenges in this thesis:
Correlations between physiological markers and stress
level;
Functionality and performance of the biofeedback system;
Effectiveness of respiration training strategy.
The brain and the spinal cord comprise the central
nervous system (CNS), which through the Autonomic Nervous
System (ANS) and peripheral innervation of organs and glands
controls the heart’s electrical activity, gland secretion, blood
pressure, and respiration function among others to preserve
the homeostasis of the organism. Analysis of ANS activity
regarding Sympathetic Nervous System (SNS) and Parasympathetic Nervous System (PNS) is a common practice for
assessing stress [26]. In order to realizing stress evaluation and
feedback training simultaneously, we designed an unobtrusive
wearable biofeedback system for recording physiological variables (ECG and Respiration) that would allow studying the
response of the ANS during stressful tasks in a non-invasive
manner, the principle framework of this study is illustrated
in Fig.3.
A. Description of System Architecture
The system architecture follows the principle framework
of novel biofeedback methodology as shown in Fig.3,
which basically contains a network of sensor nodes,
cross-platform intelligent software system, personalized
emotion assessment and biofeedback training model. The
analog front end (AFE) of proposed system is based on
the capacitive sensing principle, and comprises of textile
based polymer fabrics electrode array, general structure analog
WU et al.: ASSESSMENT OF BIOFEEDBACK TRAINING FOR EMOTION MANAGEMENT
7089
Where C [F] is capacitance of the coupling, R[] is
resistance of the inserted insulator (clothes) and f [Hz] is
frequency of the body source signal. Since R is so high in
this study that it can be regarded as infinity, and C can be
represented by using coupling area S [m2 ], distance d [m]
between textile electrode and the skin, and permittivity ε [F/m]
of the insulator (clothes), so the equation (1) can be expressed
by equation (2):
d
1
×
(2)
Z R=∞ =
2π
f εS
Fig. 4. Block diagram of hardware platform based on capacitive sensing
principle.
conditioning module, universal digital signal processing unit
and wireless transmitter module, which amplifies, filters and
then relays the signals to the computer. Emotion assessment
algorithm and biofeedback mechanism are installed in the
PC/smart phone, which initiates and loops the audio/video
cues till the abnormal stress level is detected. Such system
is capable of driving different actuators as biofeedback
generators and provides a promising methodology for emotion
management, and has the potential to integrate more sensing
nodes as different actuators for biofeedback regularization.
B. Hardware Platform Description
Because our system needs to record ECG and respiratory
data continuously for at least one week, we chose a conductive
textile material as the electrodes in our system, which can
extract breathing activity and ECG signal from individual
simultaneously. The AFE consisted of a differential separation
filter and a common signal conditioning part, and the
block diagram of whole hardware platform was illustrated
in Fig. 4.
1) Principle and Construction of Capacitive Sensing for
ECG and Respiration Measurement: The proposed approach
of capacitive sensing by using wearable textile electrode for
ECG and breathing activity measurement is an expansion of
the principle of the capacitive (or insulator) electrode, the
coupling is composed of a conductive fabric electrode, clothes
and the skin of the subject, as shown in Fig.4. According to
the equivalent circuit elements, impedance Z [] of coupling
is expressed by equation (1):
Z=
R
1 + (2π f C R)2
= 1
1
R2
+ (2π f C)2
(1)
As we known, the capacitive sensing is susceptible to body
motion, which is considered as the geometric parameters
S and d have been changed, so the obtained signals had
contained a periodic variation involving low frequency component and had seemed to be caused by breathing activity in
the position of chest. Based on all these facts, a separation
filter (1Hz Differential Separation Filter) is employed in our
proposed measuring system to divide the original signal into
a high frequency component containing ECG signal (>1Hz)
and a low frequency component including respiratory
signal (<1Hz), which was constructed of two sets of subtracter, amplifiers and integrators according to DC suppression
circuit.
The signal conditioning unit with general structure for
sensing ECG signal consisted of an instrumentation amplifier,
a high-pass filter (HPF), a low pass filter (LPF) and two
inverting amplifiers. The circuit elements of the HPF and the
LPF were designed in order to obtain a cutoff frequency of
0.5 and 40Hz, respectively. Although electrocardiograph for
diagnostic purpose requires a bandwidth from 0.01 to 100Hz,
we narrowed the bandwidth of the developed part for decreasing noise interference. The signal conditioning unit for sensing
breathing activity consisted of an instrumentation amplifier, a
high pass filter and an inverting amplifier.
2) Construction of Capacitive Driven-Right-Leg (DRL) for
ECG Measurement: For the reduction of common-mode
noise level in capacitive ECG measurement, a driven-rightleg (DRL) circuit was employed in this study. Generally,
ECG measurements are carried out by the differential method,
which is very effective in reducing the common mode noise.
However, in real measurement, this method does not work
as well as expected due to the large asymmetry in the two
signal path. Therefore, DRL circuit is a must-have component
in AFE for common mode noise reduction, which is usually
attached on the right leg and connected to the instrument
ground. And the common electrode is driven by the inverse
of common mode noise component, and then the impedance
between the body and the sensing instrument is reduced. In this
study, we have carefully considered the material and wearable
modality of textile electrodes, and designed two rectangular
lead electrodes with 3cm width, and a rectangular third electrode for DRL circuit with 5cm width. The DRL was applied
to our system and the equivalent circuit of the system has been
illustrated in Fig.5 (a) and (b).
From the equivalent circuit in fig.5 (b),
VO = −G × VC M , G =
2R F
RE
(3)
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IEEE SENSORS JOURNAL, VOL. 15, NO. 12, DECEMBER 2015
Fig. 5. (a) The configuration of textile electrode-based wearable system
including driven right leg circuit and (b) is the equivalent circuit of (a).
Fig. 6.
Equivalent circuit for skin-electrode impedance [27].
VO = VC M − (R O + Z C G ) × i 1
(4)
where VC M is the common mode voltage, G is the resistor
ratio of the inverting amplifier (A3), ZCG is the impedance of
textile electrode, and R O is a current limiting resistor. From
equation (3) and (4),
R O + Z CG
(5)
G+1
Equation (5) shows that if the third textile electrode
is connected to a driven right leg circuit instead, and
if G > Ro/ZCG , then this reduces the effective resistance to
common RCM and thus reduces common mode voltage VC M .
3) Technical and Electronic Method for Variation of
Skin-Electrode Impedance: In this study, how varying
skin-electrode interface impedance affects the bio-signal
sensing is also an important issue with regard to textile
electrode-based ECG measurement. In the literature [27],
Swanson and Webster have developed a model for
skin-electrode impedance, as shown in Fig. 6.
The model can be expressed by equation (6):
w2 Cd2 Rd2 Rd
|Z (w)| = Rs +
−j
1 + w2 Cd2 Rd2
1 + w2 Cd2 Rd2 VC M = RC M × i 1,
RC M =
S
(6)
d
Where Rs represents the electrolyte gel (if any), sweat, and
the underlying skin tissue, generally, the Rs is constant for an
individual. Rd stands for the resistance that occurs between
the skin and electrode during charge transfer. In this equation,
while the capacitance Cd represents the electrical charge
between the electrode and skin, which can be calculated by
using coupling area S, distance d between textile electrode and
the skin, and permittivity ε. In this work, we have used a smart
textile electrode with good comfort and characteristic, and can
w = 2π f, Cd = ε ×
adapt to different shapes of body with single size. Therefore,
the permittivity ε, and the resistance of fabric electrode (Rd )
is also constant, furthermore, the textile electrodes’ size (S)
have enlarged and been configured to the rectangular with
3cm width for achieving reliable capacitive coupling [27].
Consequently, in the equation (6), the skin-electrode
impedance varies with the distance between skin and conductive textile electrodes and the external pressure applied to the
electrode surface.
Therefore, in this work, the textile electrode we opted
for ECG measurement has elastic characteristics to make a
better contact between the electrode and the skin that leads to
smaller skin-electrode impedance. And, the electronic method
we selected to decrease the effect of varying skin electrode
impedance as time passes is to install a high input resistance
pre-amplifier (active electrode A1, A2) on the rear side of
the conductive textile electrode. The pre-amplifier converts
the high impedance between the fabric electrode and skin
to low output impedance required by subsequent circuitry.
The voltage variations on the skin sensed by the two fabric
electrode through the capacitive couplings are amplified by
each pre-amplifier.
C. Software Platform Description
The software platform has three main components:
a physiological signal-processing unit, a physiological signalanalysis unit, and a biofeedback regulation unit.
The signals collected in this work can be quite weak and
easily destroyed by a variety of noises, which can come from
Electromagnetic interference of power line, poor quality of
contact between the electrode and the skin, baseline wander
caused by respiration, noises generated by hardware platform
or electrosurgical instrument, movement of body position, and
any other possible noise sources. Most of these noises cannot
be filtered out completely over the hardware-processing unit.
Therefore, it is necessary to filter out these noises as much
as we can in the software platform. We adopted Butterworth
Notch Filter (BNF) and finite impulse response (FIR)
band-pass filter to eliminate the power line interference
and baseline wander, and a novel multi-scale mathematical
morphology (3M) filter to reduce the impact of the nonlinear noises caused by poor electrode contact and motion
artifacts [28], [29]. In the end, we use a differential operation
method (DOM) [30] to smooth and normalize the physiological signal collected by our hardware platform in this
signal-processing unit.
In the physiological signal-analysis unit, we use an
adaptive QRS waveform identification algorithm developed
in our previous work [31] to extract RR interval from
ECG signal. The results generated by this algorithm can
be used to measure the HRV and heart rhythm pattern.
We analyze the heart rhythm pattern using the morphological
inspection and quantitative method [32] simultaneously.
The calculated time domain (Mean RR, SDNN, rMSSD,
pNN50), frequency domains (VLF, absolute and normalized
LF, HF, total power), geometric (TINN, HRV TI) and nonlinear
measures (Poincare plot & Detrended Fluctuation Analysis) of
HRV are obtained according to the standards of measurement,
WU et al.: ASSESSMENT OF BIOFEEDBACK TRAINING FOR EMOTION MANAGEMENT
7091
proposed by the Task Force of the European Society of
Cardiology and the North American Society of Pacing and
Electrophysiology [33], [34], which describes the detail of
physiological correlates of HRV and calculation methods.
The biofeedback module was built on the theoretical basis
of RFT and heart rhythm pattern. We utilized these two
efficient biofeedback mechanisms simultaneously to alter the
physiological and emotional state through respiratory control.
The biofeedback module basically included two parts: an
algorithm module and a GUI module (integrated into the
bio-signal monitoring module). There are two procedures in
the algorithm designed for biofeedback application in this
work. In the first procedure, cyclic measurements were collected at different frequencies of respiratory (4–7 breaths per
minute and in increments of 0.5), and the respiratory rate was
recorded when HRV and CR reached the maximum gains, and
heart rate curve reached maximum oscillation amplitude. After
collecting cyclic measurements, the subject in this experiment
was asked to gradually adjust breathing to his/her resonant
frequency with the help from our biofeedback regulation unit.
Finally, the subjects were asked to keep the depth of
respiration in an approximately constant frequency. In the
second procedure, the subject in this experiment might use
the resonant frequency in self-regulation to control emotion
and stress.
III. E XPERIMENT R ESULTS AND D ISCUSSION
In this study, textile electrodes and hardware platform were
integrated into an elastic belt that could be comfortable worn
around thorax or abdomen (chest strap or waist belt) for physiological monitoring and biofeedback training, furthermore, we
also afforded a reflective photo-sensor to detect PPG signal
for the future study, which appears as the form of wrist strap.
Based on such wearable components, we could construct a
miniaturized body sensor network (BSN) [35] in combination
with the base station node (or BT dongle) and smart terminals
to provide an inexpensive, unobtrusive, and unsupervised
ambulatory monitoring during normal daily activities. To aim
at existed challenging issues of BSN, this proposed wearable
biofeedback system was constructed by aforementioned smart
textile electrodes, biosensor node platform, and associated
biosensors, meanwhile, a battery board has been well-designed
to provide selectable power solutions for the purpose of low
consumption. In addition, a base station node with Bluetooth
function was developed to aggregates information from the
distributed biosensor nodes and ultimately conveys it across
existing networks to other stakeholders or intelligent terminals,
such as smart phone, PAD and notebook. Inherently, this
proposed wearable biofeedback system was based on sophisticated circuit design, configuration and integration; it not only
facilitates non-expert users for self-emotion management,
but also allows the caregiver to select appropriate biofeedback
strategies for individuals with enough user-specific options.
Consequently, the proposed wearable biofeedback is of great
importance to be a crucial component in BSN framework for
realizing ubiquitous, affordable and customized self emotionmanagement. The system implementation was detail illustrated
in Fig.7.
Fig. 7.
System implementation of proposed biofeedback methodology.
A. Evaluation of HW/SW System
We performed the in-situ dynamic respiration experiments
in a closed room with constant temperature controlled by the
air conditioner. All the subjects were required to lie down at
supine positions when they wore our device on the wrist or
elbow. During the experiment, we also used one commercially
available ECG measurement kit named “PolyG-A” (LAXTHA
Inc.) to collect the signal under the same conditions, and its
results would be compared to our device so as to evaluate
the performance of our device. In conducted experiment, the
grounds of Poly G-A system and designed biofeedback system
were connected, so to measure the respiration signal and
ECG signal l from electrodes (Lead II) under the 512 Hz
sampling rate. Based on the waveforms of ECG and respiration signals acquired by both systems shown in Fig.8,
we can concluded that the quality of ECG and respiration
signals was very close to that from PolyG-A. We also displayed the HRV curves, respiration curves, and PSD analysis
results, which were generated by Telescan software packaged
(LAXTHA Inc.) and our physiological signal analyzer
respectively, the comparative results as shown in Fig.8 demonstrated the performance of wearable biofeedback system is
very close to the commercial bed-side medical monitoring
system.
Because the low-power consumption has been one of
significant challenges in wireless sensor network, we collect
the information of the power consumption by measuring the
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IEEE SENSORS JOURNAL, VOL. 15, NO. 12, DECEMBER 2015
TABLE I
C OMPARISONS OF R ESPIRATORY PATTERN D URING T HREE C ONDITIONS
Fig. 8.
The performances comparison of LAXTHA PolyG-A and our
biofeedback system.
aforementioned three battery power supply solutions for our
experiment respectively: one normal battery (9V, 350mAh,
3.15Wh), a portable solar battery (3.7V, 350mAh, 1.295Wh),
and a button cell battery (3V, 250mAh, 0.75Wh). During
the experiment, our hardware platform ran the ECG and
respiration sensors in a full capacity. Therefore, the data
throughput of two channels was 64 kbps with sampling rate
and length in 512Hz and 12bit respectively. Consequently, our
platform could run about 10 hours with the support of 9V
battery, 5 hours with the support of solar battery and 3.5 hours
with the support of button cell battery respectively for ECG
and respiration data collection.
B. Evaluation of Biofeedback Mechanism
1) Experiment Procedure: 15 healthy, right-handed subjects
(all male ranging from 20 to 28 years of age, with no history
of cardiovascular disease) recruited from a local university
took part in this pilot study, which has been approved by the
Institutional Review Board (IRB) of Pusan National University
Hospital. After the informed written consent was collected
from all the participants, they were asked to complete
questionnaires for examining their psycho-physiological
assessment.
All subjects were tested under standard conditions between
1:00 and 5:00 P.M., at a room temperature of 22-26 °C
after abstaining from smoking and coffee consumption for
6 hours before participation in the experiment, and asked
to wear the device comfortably with regular electrodes.
Lead II ECG signals and respiration signal were collected
for each subject simultaneously. Visual stimuli pictures from
the International Affective Picture System (IAPS) [36] were
selected randomly to evoke negative emotional responses. The
experiment procedure includes three sessions:
1) Measurement of HRV parameters, oscillate amplitude
of heart rate, and coherence ratio under the rest state
(baseline condition, BL);
2) In the absence of resonant frequency-based respiration biofeedback, evaluating the physiological responses
(HRV, Respiration and heart rhythm pattern) under the
negative emotion (NE_nBF);
3) Evaluating the physiological responses under the
negative emotion (NE_BF) with resonant frequencybased respiration biofeedback training.
In this pilot study, HRV temporal parameters included:
SDNN, which is a global index of HRV and reflects all
long-term components and circadian rhythms responsible for
variability in the recording period; rMSSD, which reflects
parasympathetic nerve activity, and the HRV triangular
index (HRV TI), which serves as an estimate of the
overall HRV. In the HRV PSD analysis, the low frequency
(LF, 0.04-0.15Hz) and high frequency (HF, 0.15-0.4Hz)
components were measured in normalized units; the former
reflects respiration and is associated solely with activity in
the parasympathetic nervous system, whereas the latter is
associated with sympathetic and parasympathetic modulation
of RR intervals and is strongly influenced by baroreflex
activity. Correspondingly, the LF/HF ratio was calculated
to estimate the sympatho-vagal balance. Moreover, we also
measure the coherence ratio (CR) that reflects the degree of
ANS synchronization.
Pair wise comparisons were made to compare the parameters difference between the NE_BF and NE_nBF by using
the paired t-test. And one-way ANOVA for repeated measures was also performed in order to evaluate the effects
of the resonant frequency-based respiration biofeedback on
HRV parameters and heart rhythm pattern. Probability values
less than 0.05 were considered statistically significant
(SPSS v19.0, Chicago, IL, USA).
C. Analyses of Respiratory Pattern
Table 1 demonstrates the mean value of respiratory rate,
expiration (Exp.) time, inspiration time (Insp.) and the ratio
of expiration to inspiration time (E/I ratio). As expected, the
three trials differed significantly in the respiration measures,
illustrating that the experimental manipulation of the
respiration pattern was successful. In the NE_nBF trail, the
respiratory rate was 15.3cpm, whereas in the NE_BF trail
the respiratory rate was 6.5cpm. Compared with BL, the
respiratory rate was significantly increased during NE_nBF
WU et al.: ASSESSMENT OF BIOFEEDBACK TRAINING FOR EMOTION MANAGEMENT
7093
TABLE II
C OMPARISONS OF H EART R ATE VARIABILITY
U NDER T HREE C ONDITIONS
Fig. 9. Three representative samples of heart rhythm pattern analysis and
corresponding coherence ratio (CR).
(p<0.05) mainly due to the influences of negative emotion,
and obviously decreased during NE_BF (p<0.01) condition
attributed to conscious respiratory control. The expiration
times were approximately 3-fold and 2-fold larger in the
NE_BF than NE_nBF and BL condition. Although there was
no obvious difference between BL and NE_nBF trials, the
inspiration time during NE_BF was significantly higher than
NE_nBF (p<0.05). Moreover, the repeated measures ANOVA
showed that volitional respiratory control could change the
respiratory pattern greatly during NE_BF trial.
D. Analyses of Heart Rhythm Pattern
The pattern of heart rhythm looks like periodic sine waves
during process of converting the negative emotion into positive emotion. Fig.9 showed three typical examples of heart
rhythm patterns derived from the subject. The morphological
differences of these heart rhythm patterns happened during
NE_BF, NE_nBF and BL trials. The negative emotion would
introduce a disjointed and unpredictable heart rhythm pattern together with low oscillation-amplitude during NE_nBF.
However, more regular and coherent heart rate rhythm, with
a concomitant increase in oscillation amplitude, has been
observed in the NE_BF trial, which reflected increased ANS
synchronization and manifested a sine-wave-like oscillating
mode of heart rhythm (positive emotion).
E. Analyses of Heart Rate Variability
In Table 2, compared with BL condition, significantly
increased CR (p<0.01), SDNN (p<0.01), rMSSD (p<0.01),
HRV TI (p<0.05) and normalized HF (p<0.05) have been
observed during NE_BF trial, and decreased significantly in
the NE_nBF trial correspondingly. Also, with or without the
resonant frequency-based respiratory control, these measures
differed greatly in the presence of negative stimuli. Significantly increases in CR, SDNN and HRV TI during NE_BF
trial implied enhanced total HRV and responsiveness of the
cardiac autonomic system to psycho-physiological stimuli,
and increased rMSSD and normalized HF, together with
significantly decreased LF (p<0.05) indicated the inhibition
of sympathetic nervous system coupled with the activation of
parasympathetic nervous system. In the NE_nBF trial, except
normalized LF, all of the time domain, frequency domain
and geometric measures were much lower than BL section,
which indicated diminished total HRV and over-activation
of sympathetic output flow. With resonant frequency based
respiratory control, the subjects could speed up recovery of the
cardiovascular system from the adverse impacts of negative
emotions by themselves. Moreover, the LF/HF ratio during
NE_BF trial was much lower than NE_nBF also demonstrated
that resonant frequency based respiratory control produced a
higher sympatho-vagal balance under the negative emotion.
The repeated measures ANOVA further revealed that resonant respiratory frequency was capable of profoundly affecting
the time domain, frequency domain and geometric measures
in the presence of negative emotion, and shifting heart rhythm
toward a positive emotion related oscillation mode.
IV. C ONCLUSIONS
The wearable device with biofeedback function has been
one emerging researching application, which can integrate
modern biofeedback theories into the state-of-the-art sensing
and BSN technologies. The wearable device developed in this
work is generic with low power-cost and low complexity, and it
can be expanded to build more wearable biofeedback-training
devices. This pilot study used wearable training patterns and
resultant HRV to exercise the biofeedback functions.
HRV dynamics can be easily affected by the human’s
physiological and emotional states. For example, positive and
negative emotions can be distinguished by smooth or erratic
heart rhythm patterns, respectively. Particularly, heart rhythms
become more irregular during the experience of negative
emotions, which can imply less synchronization in the reciprocal action between the sympathetic and parasympathetic
branches of the autonomic nervous system. Correspondingly,
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IEEE SENSORS JOURNAL, VOL. 15, NO. 12, DECEMBER 2015
sustained positive emotion was associated with greater synchronization between the two branches, which can indicate
a highly coherent heart rhythm pattern. We used the realtime morphological characteristic of HRV curve as a visual
tool to evaluate the effects of resonance frequency based
respiratory feedback training. In addition, the CR was adopted
to assess the destructive influence of negative emotion on heart
rhythm pattern, which derived from spectral measures of HRV.
Also, CR is very effective in quantifying the performance of
resonance frequency respiratory training.
The results of statistical analysis in our experiments
can confirm that the relationship between negative emotion
and HRV. These findings were also supported by the lower
value of CR, SDNN, rMSSD, HRV-TI, and higher LF/HF
ratio under NE_nBF trial compared with BL and NE_BF
trail (as shown in Table 2). Meanwhile, the graphical analysis
results provided the evidence for the effectiveness of resonance
frequency based respiratory training. As shown in Fig.9,
during the NE_BF trial, the heart rhythm pattern of individuals
were more regular and coherent (higher CR value) compared to
NE_nBF trial, which implied that resonance respiratory training could decrease the harmful influences of negative emotion
to ANS modulation. Therefore, all analysis results suggested
that resonant frequency based respiratory combined with heart
rhythm pattern feedback was appropriate to decrease sympathetic arousal, increase parasympathetic activity, and enhance
overall capability of ANS modulation in the presence of
negative emotion, including time domain, frequency domain,
quantified heart rhythm pattern and morphological recognition
of HRV curves. However, the number of samples used in this
study was small. We will collect a large-scale experimental
data using wearable textile physiological monitoring system
in the future study.
BSN is the network of several associated sensor nodes on,
inside or around human body to monitor vital signals, which
has showed enormous potential for ubiquitous and low-cost
healthcare. Therefore, we might investigate more advanced
infrastructure of the BSN-based biofeedback systems by using
multiple physiological parameters, and explore the data confidentiality and security (biometric security) based on HRV
features to attain privacy during physiological information
processing in BSN.
Competing Interests
The authors declare that they have no conflict of interest.
R EFERENCES
[1] P. L. Schoenberg and A. S. David, “Biofeedback for psychiatric disorders: A systematic review,” Appl. Psychophysiol. Biofeedback, vol. 39,
no. 2, pp. 109–135, 2014.
[2] S. Elmer and L. Jäncke, “Intracerebral functional connectivity-guided
neurofeedback as a putative rehabilitative intervention for ameliorating
auditory-related dysfunctions,” Frontiers Psychol., vol. 5, Oct. 2014,
Art. ID 1227.
[3] E. Peper, R. Harvey, and N. Takabayashi, “Biofeedback an evidence
based approach in clinical practice,” Jpn. J. Biofeedback Res., vol. 36,
no. 1, pp. 3–10, 2009.
[4] A. J. Beckham, T. B. Greene, and S. Meltzer-Brody, “A pilot study of
heart rate variability biofeedback therapy in the treatment of perinatal
depression on a specialized perinatal psychiatry inpatient unit,” Arch.
Women’s Mental Health, vol. 16, no. 1, pp. 59–65, 2013.
[5] J. E. Walker and R. Lawson, “FP02 beta training for drug-resistant
depression—A new protocol that usually reduces depression and keeps
it reduced,” J. Neurotherapy, vol. 17, no. 3, pp. 198–200, 2013.
[6] M. S. Schwartz and F. Andrasik, Biofeedback: A Practitioner’s Guide,
3rd ed. New York, NY, USA: Guilford Press, 2003.
[7] L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces:
A review,” Sensors, vol. 12, no. 2, pp. 1211–1279, 2012.
[8] G. E. Prinsloo, H. G. Rauch, and W. E. Derman, “A brief review
and clinical application of heart rate variability biofeedback in sports,
exercise, and rehabilitation medicine,” Phys. Sportsmed., vol. 42, no. 2,
pp. 88–99, 2014.
[9] P. Lehrer, “History of heart rate variability biofeedback research:
A personal and scientific voyage,” Biofeedback, vol. 41, no. 3,
pp. 88–97, 2013.
[10] P. Lehrer et al., “Protocol for heart rate variability biofeedback training,”
Biofeedback, vol. 41, no. 3, pp. 98–109, 2013.
[11] E. G. Vaschillo, B. Vaschillo, R. J. Pandina, and M. E. Bates, “Resonances in the cardiovascular system caused by rhythmical muscle
tension,” Psychophysiology, vol. 48, no. 7, pp. 927–936, 2011.
[12] W. Wu, Y. Gil, and J. Lee, “Combination of wearable multi-biosensor
platform and resonance frequency training for stress management of the
unemployed population,” Sensors, vol. 12, no. 10, pp. 13225–13248,
2012.
[13] A. P. Sutarto, M. N. A. Wahab, and N. M. Zin, “Heart rate variability
(HRV) biofeedback: A new training approach for operator’s performance
enhancement,” J. Ind. Eng. Manage., vol. 3, no. 1, pp. 176–198,
2010.
[14] K. Malhi, S. C. Mukhopadhyay, J. Schnepper, M. Haefke, and H. Ewald,
“A Zigbee-based wearable physiological parameters monitoring system,”
IEEE Sensors J., vol. 12, no. 3, pp. 423–430, Mar. 2012.
[15] N. Samanta, A. Chanda, and C. RoyChaudhuri, “An energy efficient,
minimally intrusive multi-sensor intelligent system for health monitoring
of elderly people,” Int. J. Smart Sens. Intell. Syst., vol. 7, no. 2,
pp. 762–780, 2014.
[16] X. Zhenghua et al., “The implementation for the intelligent home control
system based on the android and Zigbee,” Int. J. Smart Sens. Intell. Syst.,
vol. 7, no. 3, pp. 1095–1113, 2014.
[17] N. K. Suryadevara and S. C. Mukhopadhyay, “Wireless sensor network
based home monitoring system for wellness determination of elderly,”
IEEE Sensors J., vol. 12, no. 6, pp. 1965–1972, Jun. 2012.
[18] R. C. King, L. Atallah, B. Lo, and G.-Z. Yang, “Development of a
wireless sensor glove for surgical skills assessment,” IEEE Trans. Inf.
Technol. Biomed., vol. 13, no. 5, pp. 673–679, Sep. 2009.
[19] N. K. Suryadevara, S. C. Mukhopadhyay, R. Wang, and R. K. Rayudu,
“Forecasting the behavior of an elderly using wireless sensors data in a
smart home,” Eng. Appl. Artif. Intell., vol. 26, no. 10, pp. 2641–2652,
2013.
[20] N. K. Suryadevara and S. C. Mukhopadhyay, “Determining wellness
through an ambient assisted living environment,” IEEE Intell. Syst.,
vol. 29, no. 3, pp. 30–37, May/Jun. 2014.
[21] N. K. Suryadevara, M. T. Quazi, and S. C. Mukhopadhyay, “Intelligent
sensing systems for measuring wellness indices of the daily activities
for the elderly,” in Proc. 8th Int. Conf. Intell. Environ. (IE), Jun. 2012,
pp. 347–350.
[22] S. C. Mukhopadhyay, “Wearable sensors for human activity monitoring:
A review,” IEEE Sensors J., vol. 15, no. 3, pp. 1312–1330, Mar. 2015.
[23] H. Jing, “Coverage holes recovery algorithm based on nodes balance
distance of underwater wireless sensor network,” Int. J. Smart Sens.
Intell. Syst., vol. 7, no. 4, pp. 1890–1907, 2014.
[24] M. T. Quazi, S. C. Mukhopadhyay, N. K. Suryadevara, and
Y. M. Huang, “Towards the smart sensors based human emotion recognition,” in Proc. IEEE Int. Instrum. Meas. Technol. Conf. (I2MTC),
May 2012, pp. 2365–2370.
[25] S. C. Mukhopadhyay, N. K. Suryadevara, and R. K. Rayudu,
“Are technologies assisted homes safer for the elderly?” in Pervasive
and Mobile Sensing and Computing for Healthcare. Berlin, Germany:
Springer-Verlag, 2013, pp. 51–68.
[26] E. Vaschillo, P. Lehrer, N. Rishe, and M. Konstantinov, “Heart rate
variability biofeedback as a method for assessing baroreflex function:
A preliminary study of resonance in the cardiovascular system,” Appl.
Psychophysiol. Biofeedback, vol. 27, no. 1, pp. 1–27, 2002.
[27] H. Miller and D. C. Harrison, Biomedical Electrode Technology: Theory
and Practice. New York, NY, USA: Academic, 1974.
[28] S. Prakash and V. Venkatesh, “Real time monitoring of ECG signal using
PIC and Web server,” Int. J. Eng. Technol., vol. 5, no. 2, pp. 1047–1053,
2013.
WU et al.: ASSESSMENT OF BIOFEEDBACK TRAINING FOR EMOTION MANAGEMENT
[29] C. Zhu and F. Tian, “An ECG detection algorithm using wavelet and
autocorrelation transform,” in Proc. Int. Conf. Wireless Commun. Signal
Process. (WCSP), 2013, pp. 1–6.
[30] Y.-C. Yeh and W.-J. Wang, “QRS complexes detection for ECG signal:
The difference operation method,” Comput. Methods Programs Biomed.,
vol. 91, no. 3, pp. 245–254, 2008.
[31] W. Wu and J. Lee, “Improvement of HRV methodology for positive/negative emotion assessment,” in Proc. 5th Int. Conf. Collaborative Comput., Netw., Appl. Worksharing (CollaborateCom), Nov. 2009,
pp. 1–6.
[32] S. Kim et al., “Heart rate variability biofeedback, executive functioning
and chronic brain injury,” Brain Injury, vol. 27, no. 2, pp. 209–222,
2013.
[33] F. X. Gamelin, S. Berthoin, and L. Bosquet, “Validity of the polar S810
heart rate monitor to measure R-R intervals at rest,” Med. Sci. Sports
Exercise, vol. 38, no. 5, pp. 887–893, 2006.
[34] M. Malik, “Heart rate variability standards of measurement, physiological interpretation, and clinical use,” Circulation, vol. 93, no. 3, pp. 354–
381, 1996.
[35] S. Pirbhulal, “An efficient biometric-based algorithm using heart rate
variability for securing body sensor networks,” Sensors, vol. 15, no. 7,
pp. 15067–15089, 2015.
[36] N. Suvorov, “Psychophysiological training of operators in adaptive
biofeedback cardiorhythm control,” Spanish J. Psychol., vol. 9, no. 2,
pp. 193–200, 2006.
Wanqing Wu received the B.S. degree in computer
science and technology from Hunan Normal
University, Hunan, China, in 2004, the M.E. degree
in computer science from Chongqing University,
Chongqing, China, in 2007, and the Ph.D. degree
from the Pusan National University of Computer
Enigneering, Korea, in 2013. He is currently an
Associate Professor with the Institute of Biomedical
and Health Engineering, Shenzhen Institutes of
Advanced Technology, Chinese Academy of
Sciences, Shenzhen, China. He is also with the Key
Laboratory for Health Informatics of the Chinese Academy of Sciences,
China. His research interests include wearable medical device, biomedical
signal sensing and processing, body sensor networks, physiological health
informatics, and biofeedback theory.
Heye Zhang received the B.S. and M.E. degrees
from Tsinghua University, Beijing, China,
in 2001 and 2003, respectively, and the Ph.D. degree
from the Hong Kong University of Science and
Technology, Hong Kong, in 2007. He is currently an
Associate Professor with the Institute of Biomedical
and Health Engineering, Shenzhen Institutes
of Advanced Technology, Chinese Academy of
Sciences, Shenzhen, China. He is also with the Key
Laboratory for Health Informatics of the Chinese
Academy of Sciences, China. His research interests
include cardiac electrophysiology and cardiac image analysis.
Sandeep Pirbhulal received the B.E. degree in
telecommunication engineering from the Mehran
University of Engineering and Technology, Pakistan,
in 2011, and the M.S. degree in telecommunication and networks from the Karachi Institute of
Economics and Technology, Pakistan, in 2014.
He is currently pursuing the Ph.D. degree with the
Shenzhen Institutes of Advanced Technology,
Chinese Academy of Sciences. He has authored four
SCI journal papers and one book chapter. His current
research includes biomedical signal processing and
biometrics-based information security.
View publication stats
7095
Subhas Chandra Mukhopadhyay (M’97–SM’02–
F’11) received the (Hons.) degree from the Department of Electrical Engineering, Jadavpur University,
Kolkata, India, the master’s degree in electrical
engineering from the Indian Institute of Science,
Bangalore, India, the Ph.D. (Eng.) degree from
Jadavpur University, and the Dr.Ing. degree from
Kanazawa University, Kanazawa, Japan.
He is currently a Professor of Sensing Technology with the School of Engineering and Advanced
Technology, Massey University, Palmerston North,
New Zealand. He has over 25 years of teaching and research experiences.
He has authored or co-authored four books and over 300 papers in different
international journals, conferences, and book chapters. He has edited
12 conference proceedings. He has also edited 12 special issues of international journals as a lead Guest Editor and 20 books, in which 18 are with
Springer-Verlag. He has delivered 246 seminars as keynote, invited, tutorial,
and special lectures in 24 countries. His fields of interest include sensors and
sensing technology, instrumentation, wireless sensor networks, electromagnetics, control, electrical machines, and numerical field calculation.
He received numerous awards throughout his career and attracted over
NZ U.S. $4.1 million on different research projects. He is a Fellow of
the Institution of Engineering and Technology, U.K., and the Institution of
Electronics and Telecommunication Engineers, India. He is a Topical Editor
of the IEEE S ENSORS J OURNAL, and an Associate Editor of the IEEE
T RANSACTIONS ON I NSTRUMENTATION AND M EASUREMENTS .
He is the Co-Editor-in-Chief of the International Journal on Smart Sensing
and Intelligent Systems. He was the Technical Program Chair of ICARA 2004,
ICARA 2006, and ICARA 2009. He was the General Chair/Co-Chair of ICST
2005, ICST 2007, the IEEE ROSE 2007, the IEEE EPSA 2008, ICST 2008,
the IEEE Sensors 2008, ICST 2010, the IEEE Sensors 2010, ICST 2011,
ICST 2012, ICST 2013, and ICST 2014. He has organized the IEEE Sensors
Conference 2009, Christchurch, New Zealand, in 2009, as the General Chair.
He will organize the Ninth ICST, Auckland, New Zealand, in 2015. He was
a Distinguished Lecturer of the IEEE Sensors Council from 2010 to 2013.
He is the Founding and Ex-Chair of the IEEE Instrumentation and
Measurement Society New Zealand Chapter. He is the Chair of the IEEE
IMS Technical Committee 18 on Environmental Measurements.
Yuan-Ting Zhang received the Ph.D. degree from
the University of New Brunswick, Canada, in 1990.
He was a Research Associate and an Adjunct
Assistant Professor with the University of Calgary
from 1989 to 1994. He joined The Chinese
University of Hong Kong (CUHK) as a Lecturer
in 1994, became an Associate Professor in 1996, and
a Professor in 2002. He serves as the Director of the
Joint Research Centre for Biomedical Engineering.
At CUHK, he has developed and teaches courses
including biomedical modeling, medical instruments
and sensors, and telemedicine techniques and applications. His research
activities have focused on the development of biomodels and biosignal
processing techniques to improve the performance of medical devices and
biosensors, in particular, for telemedicine. His work has been published
in several books, over 20 scholarly journals, and numerous international
conference proceedings.
Dr. Zhang held various positions in professional organizations. He served
as the Technical Program Chair of the 20th Annual International Conference
of the IEEE Engineering in Medicine and Biology Society (EMBS). He was
the Chairman of the Biomedical Division with the Hong Kong Institution
of Engineers in 1996/97 and 2000/01. He was elected and served as an
AdCom Member of the IEEE-EMBS in 1999, and the Vice President of
the IEEE EMBS in 2000 and 2001. He serves as an Associate Editor of
the IEEE T RANSACTIONS ON B IOMEDICAL E NGINEERING and the IEEE
T RANSACTIONS ON M OBILE C OMPUTING, and an Editorial Board Member
for the Book Series of Biomedical Engineering (Wiley and IEEE Press).
He is the General Conference Chair of the 27th Annual International
Conference of IEEE EMBS.