Journal of Robotics, Networking and Artificial Life
Vol. 6(4); March (2020), pp. 209–212
DOI: https://doi.org/10.2991/jrnal.k.200221.004; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Design and Development of Intelligent Wearable Biofeedback
Fuzzy Logic-based Device for Monitoring and Treatment
of Voice Loudness
Ali S. AlMejrad*
Department of Biomedical Technology, King Saud University, P.O. Box 10219, Riyadh 11433, Kingdom of Saudi Arabia

ARTICLE INFOABSTRACTArticle History

Development of medical devices are expanding rapidly. This is occurring as results of development of high tech electronics,information technology and wireless communication technology. Integrating these technologies helps to provide good, less costand more effective healthcare services. In this paper a novel intelligent medical biofeedback device is developed for accuratemonitoring of the voice loudness using fuzzy logic compared with proportional control. This accurate biofeedback monitoring
device then enhanced treatment of people who are unable to control excessive or low loudness due to intellectual disability or
aggressive behavior causing loudness levels rejected by others. These people with this behavior may often be unaware of theirproblems and hence an intelligent biofeedback device is necessary to be used by the patients outside the clinic for self-controlling
excessive or low loudness without the need to follow-up in speech pathology clinic for the continuous therapeutic. The use of
the intelligent wearable biofeedback device proved to be an effective due to its enhanced features. The design considerations,enhanced features, simulation and evaluation of the device will be presented.

Received 13 November 2019
Accepted 12 December 2019
KeywordsBiofeedback device
fuzzy logic
voice loudness© 2020 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
There are many voice disorders such as vocal cord nodules, vocal
loading and many other speech disorders [1]. These disorders arecaused by excessive loudness or speaking loudly due to aggressive
behavior or intellectual disability [2]. This vocal abuse may resultin vocal cord pathologies such as vocal nodules. This pathology
occurs in high percentage of children with voice disorders [3].
On the other side inadequate volume, weak or soft voice due toreduced vocal intensity are classified as voice disorders such as in
Parkinsonian patients who are unable to regulate the volume of
their speech [4].
Usually the patients with the above behaviors may often be
unaware of their problems. So intervention is required to limit
these negative outcomes. This intervention using simple verbal
prompting by therapist can help patients if instructed to regulate
the speaking volume by controlling their loudness but unfortunately such improvement generally is not long-term one. So, voice
monitoring is necessary in addition to the use of biofeedback
training. The use of biofeedback control instead is more effective
compared with the simple verbal control. Feedback using microcomputer-based system and some hardware 1 kg weight was used
for speech therapy with Parkinson’s disease patients as in Sicker
et al. [5]. Schliesser [6] used the biofeedback training employing electromyography to voice disorders. Another limited functions system using biofeedback analog device is developed by
Email: amejrad@ksu.edu.sa*

McGillivray et al. [7] to reduce loudness for treating a child with
soft nodules on her vocal cords via interrupting child conversation by the therapist with a loud tone when her speech exceeded
threshold intensity level. A portable auditory-feedback andvibratory-feedback devices for reducing excessive loudness in adeaf woman with mental retardation were used by Lancioni andMarkus [8].
To limit these negative outcomes and enhance the features of thefeedback device, a novel intelligent biofeedback device is developed
for accurate monitoring and feedback of the voice loudness using
fuzzy logic compared to proportional control [9]. This accuratemonitoring and feedback with variable alarm device will enhancetreatment of people who are unable to control excessive or low
loudness accurately. This device is portable and can be used athome without the need to the continuous therapeutic follow-up inspeech pathology clinic.

2. MATERIALS AND METHODS
2.1. Device Design Hardware
The intelligent Wearable Biofeedback Fuzzy Logic-based Device for
Monitoring and Treatment of Voice Loudness in this work consistsof many components including: throat microphone, signal conditioning including amplifiers, filters, rectifier, unity gain buffer, inProgrammable System on Chip (PSoC) microcontroller; auditory
alarm feedback. The device is battery operated as shown in Figure 1.

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The signal conditioning as shown in Figure 2. It consists of preamplifier, band pass filter to eliminate unwanted noise, main amplifier
with an adjustable gain to make the signal bigger, rectifier to turnnegative signal values to positive and integrator to evaluate voiceintensity over a set time period. A 500 Hz corner frequency low
pass filter is considered because this frequency is low enough toinclude the volume or intensity of the voice. It cuts off the highfrequencies associated with pitch, quality, or uniqueness of a particular speech. The gain of the amplifier is 100 since the typical values
expected are in the order of 10 mV [10].
The voice signal is firstly conditioned using analog circuitary in thePSoC microcontroller and then it passes through 8-bit A/D converter to be digitized for intelligent processing using Fuzzy Logic
System (FLS). The feedback is consisting of an audio alarm withduty cycle determined by FLS depends on the low or high loudness
levels. The purpose of the feedback device is to warn the patientwhen his or her speech intensity had gone below or above the specified threshold as input to the FLS based on therapist suggestion.
With auditory feedback, the patient would be notified that vocal
changes either decreasing or increasing voice level as regulation ofvoice is needed to be made. Finally, the fuzzy logic in PSoC microcontroller is programmed to process the voice data acquired andto determine when feedback to the patient is necessary as will be
discussed in the following section.

First, fuzzy-based system was designed using a graphical user interface with the fuzzy rules stored in the fuzzy system editor as shownin Figure 4.
The designed fuzzy logic system is a Multiple Input and Single
Output system where the difference between the measured intensity (LD) and threshold intensity level and change of intensity tonormal level (LDC). The output variable is alarm of up-normalcases calculated as duty cycle (AlarmDC).
Our objective is to check that intelligent system behaves better
than conventional system to control the loudness level using fuzzy
logic. The fuzzy system is designed with two input variables andone output variable. For the classical fuzzy logic system, the input
variables are LD and LDC and the output variable is AlarmDC.
Mamdani’s method is used to control loudness level by creating
the linguistic rules which is considered easy to interpret by humanoperator. Triangular and trapezoid shaped membership functions
are used for assigning the values to the input variables and theoutput variable. The universe of discourse of intensity difference,change of intensity and AlarmDC are (−80 to 80), (−100 to 100)and (0–100%), respectively.
The membership functions for LD and LDC inputs consist ofNegative Large (NL), Negative Small (NS), zero (Z), Positive Large(PL) and Positive Small (PS) as shown in Figures 5 and 6.

2.2. Design of Loudness Fuzzy
Logic-based SystemFuzzy logic-based system using graphical programming method,
can be designed with qualitative description for the system andavoiding the tedious method of developing tuning parameters forthe system via emulating the human linguistic expressions using
If-Then rules [11].
The loudness biofeedback fuzzy logic-based monitoring system isshown in Figure 3. FLS is well indicated in all the situations andprovides a reasonable and effective method compared with traditional systems.

Figure 3 | The loudness biofeedback fuzzy logic based monitoring system.

Figure 4 | Fuzzy system editor.
Figure 1 | Block diagram of the intelligent wearable biofeedback fuzzy
logic-based device for monitoring and treatment of voice loudness.

Figure 2 | Block diagram of the signal conditioning of voice signal.

Figure 5 | The membership functions for LD input.

A.S. AlMejrad / Journal of Robotics, Networking and Artificial Life 6(4) 209–212

The output AlarmDC variable value was chosen in the range of0–100% with variable frequency output based on percentage of highor low loudness. The membership functions for AlarmDC output aszero (Z), small (S), medium (M) and large (L) as shown in Figure 7.
In the design of the fuzzy inference system was formulated which
does the mapping from the given input to an output using fuzzylogic as shown in Figure 8. For evaluating the linguistic rules of thefuzzy logic Max–Min inference system has been used. In developed
FLS, the centroid defuzzification method has been used.

3. SIMULATION AND VALIDATION
A user interface Virtual Instrument (VI) is designed to simulate
and validate FLS using graphical language as shown in Figure 9
which shows the front panel of VI in testing mode. In the simulation

Figure 6 | The membership functions for LDC input.

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loop there exists one input node and one output node to configure
parameters interactively to determine the alarm output level represented as duty cycle based on the loudness level.
A simulated voice level is used in the VI as an input and thresholdlevel preset by a speech therapist and set prior to use, based on thepatient’s requirements. These two inputs are used as inputs to fuzzy
logic system. The softer or louder the patient speaks, the lower orhigher the intensity measured by FLS. The FLS then utilizes thespecified rules to process the input data and decides whether or
not the led alarm and audio feedback should be sent to the patientin addition to the length of the feedback alarm based on the up
normal level.
First, when the start button is pushed, the code will determine thethreshold level above the background noise. This alarm threshold isthe loudness level the patient needs to speak above or below.
Then, it will start processing using fuzzy logic rules. If the simulated voice signal level received is below or above the speaking threshold, the output audio alarm is activated with message
instruction. During times in which the user is not speaking, thecounter is held constant, while speaking below the threshold, thecounter is tracking the number of both events low and high alarms
in addition to the level of loudness. The test continues by taking
another simulated input sample to define the new level and thendetermine whether to set the alarm or not. This loop repeats until
the user stops the test and exits.

4. RESULTS ANALYSIS AND DISCUSSION

Figure 7 | The membership functions for LDC input.

Figure 8 | Input/Output relationship of FLS.

A complete simulation for evaluation was performed using theinitial designed, simulated and developed and assembled using abreadboard to evaluate the connections to each unit of the system.
Each stage of the intelligent system was implemented, tested andcalibrated separately. The processing stage using the programmablemicrocontroller was simulated first using different levels of speechintensity of speech-like signals. Second, it was tested by different
normal subjects who were asked to speak for few minutes using different levels of speech intensity and test to speak softly or loudly afew seconds and check if the biofeedback activates the audio alarm.
To make accurate tests a sound level meter for voice monitoring
was used to monitor the volume of voice loudness to adjust thebiofeedback to be activated when low or excessive speech intensity. After calibrating the device, the device was tested for different
levels of voice intensity which proves for the different tests that thesystem was successful.

5. CONCLUSION

Figure 9 | Front panel of the loudness monitoring controller.

The Intelligent Biofeedback Fuzzy Logic-based Device for Moni­
toring and Treatment of Voice Loudness developed as a prototype
has achieved the objective of this work. The device achieved theproposed considerations regarding the accuracy, ease of programmability, patient alert, automatic tracking of up normal occasions
during therapeutic sessions. The device features with its portability due to its small size, in addition to low power consumption.
The developed intelligent voice monitoring system performed
satisfactorily as a prototype system for monitoring, to provide a212A.S. AlMejrad / Journal of Robotics, Networking and Artificial Life 6(4) 209–212

patient with information about voice loudness level accurately
outside the clinic and for controlling of the treatment of upnormal vocal loudness levels without the need to the continuous
therapeutic follow-up in speech pathology clinic. The device isable to collect voice loudness levels through a throat microphone
and able to discriminate between voice and silence and is only
active when the subject is speaking. The device activities an audio
alarm as a biofeedback to the subject whenever the voice intensity level falls below or exceeds above adjustable single thresholdfor a preset time interval. The device has USD external memory
to store information concerning time dependent data of theuser’s voice intensity when alarms occurred in and counts of up
normal events. These data can be displayed by a speech therapist to monitor the patient’s progress and make changes to thetherapy program.
To test the device feasibility as a monitoring and therapeutic
device for voice disorders, Clinical evaluations are required tobe done.

CONFLICTS OF INTEREST
The author declares they have no conflicts of interest.

ACKNOWLEDGMENT
I would like to extend my appreciation to the Deanship of Scientific
Research and the Research Center of College of Applied MedicalSciences at King Saud University for funding this research.

AUTHOR INTRODUCTION
Ali S. AlMejrad
He received the B.Sc. with first honour inElectrical Engineering from King Saud
University, Kingdom of Saudi Arabia,M.Sc. in Bioengineering from Strathclyde
University, UK in 1987 and 1990, respectively; and the PhD in Medical engineering from University of Edinburgh, UK, in1996. He is currently an Associate Professorof Medical Engineering and Instrumentation in BiomedicalTechnology in the College of Applied Medical Sciences, KSU,
KSA. His research interests include Development of Biomedical
Instrumentation and Intelligent Systems for Health Care using
advanced Technologies. He is a member of IEEE and IPEM.

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