Biostatic Approach to Diagnose Diseases, Using Computational ‘Nadi’

Patterns

B.Dheepika1

, V.G.S.Supathma1, R.M.P.M.Samaranayaka

1, M.G.N.A.S.Fernando

1,

N.Karunarathne2, and Sarangee Wimalasiri

2

1 University of Colombo School of Computing, Colombo 7, Sri Lanka.

2 Institutes of Indigenous Medicine, University of Colombo, Sri Lanka.

Abstract. The population in the world is increasing extremely and so the people suffering from all kind of

diseases. In future, there is a need that will arise for a new system which can early detect these diseases.

Nowadays there is a need for methods to detect diseases at early stages. Even though there are various

methods to identify diseases, those methods are expensive and painful. Technology is spreading everywhere

to make human life easier.

This research is based on applying the technology into the traditional medical approach to make the life

better. For the pulse wave’s analysis, there are few devices available at the research level. Using the device,

waves from the Vada, pitha, kafpa nadi are taken and analysis of the wave’s formation is done using

computational models and biostatic approaches to come up with the signal’s status. Using that signal will

provide the stage of the diseases/ diseases.

Approach of Traditional Ayurveda Medicine uses pulse as a means for diagnosis of diseases. These

conventional techniques are nowadays been replaced by devices using various sensors for detecting pulse

signal from radial artery at wrist position. This paper gives a brief review on such techniques developed for

diagnosis of various diseases using the concepts of ‘Tridosha’.

In this research paper we describe the process of designing the pulse detecting systems and selecting the

suitable sensor for the system design.

Keywords: pulse diagnosis, ayurveda, tridosha, data acquisition, pulse diagnosis, photoplethysmograph

1. Introduction

Nadi Pareeksha is the technique can identify the diseases accurately through the pulse. It can exactly

diagnose both physical and mental diseases as well as imbalances. The term Nadi refers to the pulse, nerves,

veins, arteries, and some sort of channel for passage of physiological and biological signals. ‘Nadi

Pareeksha’ is a traditional medical approach for diagnosis of diseases using Nadi patterns. Although in

modern medicine using this pulse inspection technique is used to detect heart rate of a person using modern

devices such as stethoscope, electrocardiography etc.

In most medical traditions, measuring patient’s pulse is considered as a key diseases diagnostic method.

Different traditions looks for different things while testing the pulses. Throughout the previous studies, we

identified few ancient traditional medical techniques such as ‘Traditional Chinese Medicine’, ‘Ayurvedic

Medicine’, ‘Ancient Egyptian Medicine’, ‘Ancient Greek Medicine’, ‘Islamic medicine’, ‘Ancient

Mongolian Medicine’, ‘Arabic medicine’ etc [1]. While checking the pulse in each tradition domain experts

consider various factors to calculate the pulse pattern and to identify the diseases.

2. Review of the Literature

Corresponding author. Tel.: +94 0774700177

E-mail address: 64dheepi@gmail.com

International Proceedings of Chemical, Biological and Environmental Engineering, Vol. 99 (2016)

DOI: 10.7763/IPCBEE. 2016. V99. 4

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A number of nonlinear methods have been introduced long time ago to measure the dynamic of

physiological signal such as ECG, EEG etc and achieved some meaningful result from that. But acquiring

pulse wave is a challenging task to obtain risky result. In this section we are going to analysis prior work

related pulse acquisition, used sensors and device setup.

Table 1: Traditional medical types & techniques used to identify the pulse patterns

Medical Traditions Identification Methods

Traditional Chinese

Medicine

12 basic pulses, six on each wrist,

each linked to a different organ or

organs

Ayurvedic Medicine Rhythmic pattern of a patient’s

pulse - tridoshas

Ancient Egyptians

Medicine

Observed the pulse at different

locations of the body

Traditional Chinese

Medicine

Observed four characteristics of

the pulse: size, frequency,

strength and rhythm.

2.1. The Natural Constitution Features and Nadi-Patterns

In the Ayurvedic treatment ‘Prakrati Nidana’ is a term that describes the basic functional element of

physical, mental and emotional energy pattern called ‘Tridosha’ namely ‘Vatha’, ‘Pitta’ and ‘Kapha’. While

checking the pulse domain experts can reveal every aspect of the human organism, the Body, Mind etc.

While checking tridosha experts considering three parameters base on, size or volume of the pulse, number

or rapidity of the pulse and rhythm or regularity of the pulse

In the size and volume denotes the condition of blood vessel. Number and rhythm denotes the condition

and working of the heart. Kapha influenced more on size and volume of the blood vessel, Pitta influenced on

rapidity and Kapha influenced on regularity. However in the practical scenario identifying the Vada, Pitta

Kapha is a big deal to the new generation.

2.2. Pulse Diagnosis Technique

For the purpose of identifying the disease, the doctor with his hand should examine the pulse signal of

the patient from radial artery at wrist position. With a stable calm and concentrated mind, the doctor should

feel pulse with his fingertips. For female left hand give the accurate pulse readings, for men the right hand

gives the accurate pulse reading. Best period for pulse examination is in the first three hours (Prahara) of the

morning [2].

There are some particular restrictions and conditions while taking the pulse reading. Those who have just

taken bath, just taken food, just undergone snehana (oil massage or fat intake), having thirst and hunger and

who are asleep in these conditions specialists can’t be recognize pulse waves properly. While getting the

pulse reading experts need to consider some additional physical conditions of the patients and need to note

the general condition and habits of the patient facial expression, the reaction toward the climatic conditions,

appetite, strength, nature of sleep, breathing pattern history of ailments are need to confirm. According to

these factors experts can come to the conclusion of the diseases [1].

2.3. Previous Work

In the early researches researchers used various types of sensors to acquire the data. While testing and

going through with the literature reviews selecting sensors and designing the device is the most challenging

part in this research. Specific sensors are used and tested by many researchers. Such as: HMX-Pulse sensor,

Millivolt Output Pressure Transducer, Piezoelectric pulse diagnosis, Photoelectric sensor, piezoelectric

sensor and ultrasonic. HMX-Pulse sensor used by Ranjan and Mandeep (2013), in their research process,

they research on acquiring finger tips PPG signal. They mentioned in their work that, this system can

develop in future to acquire tridosha. Persis and Rani (2010) researched on modern pulsometer for capture

tridosha. This research was carried using Millivolt Output pressure sensor. Narayanan et.al (2015) and

A.E.Kalange et.al research on diagnosis Nadi using piezoelectric sensors and discovered piezoelectric

sensors are most suitable and sensible sensors to acquire Nadi [4].

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3. Methodology

In this section, we describe the methodology of designing the pulse detecting system. As shown in the

Fig. 1, pulse diagnosis and prediction system that we designed.

And the details of the experimental methods used for detecting the human pulse and the circuits used for

signal conditioning have been given. Initial part of this research carries out the identification of the sensor.

Fig. 1: Overall design of our system

3.1. Sensor Selection

This research consists of many research components. One of the main researchable areas is selecting the

suitable sensor for the research and validates the possibility of developing the device. Acquiring pulse wave

is a challenging task. In this section we are going to analysis prior work related pulse acquisition, used

sensors and device setup.

Pulse detected on the radial artery, which is mainly related to the blood flow and heart rate of human

body. Pressure sensor is the most suitable sensor type to recognize pulse wave. However there are many

types of pulse sensors available. Such as Piezoresistive strain gauge, capacitive sensors, Electromagnetic

sensors, piezoelectric sensors, optical sensors etc [2]. While analyzing the previous work Piezoresistive

strain gauge sensors, piezoelectric sensors and optical sensors were used to capture pulse signal. Pulse can

be detected at various places. Within those pulse points’ fingertips, wrist point, neck points are more

sensitive points. Fingertip palpitation is not an exact point to capture Nadi palpitation.

Electrical and computational knowledge is essential to complete the product successfully. There are

various sensors available in the market related to medical usage. Such as Piezo-electric sensors, Infrared

Sensors , PVDF sensors, Acoustic sensors, Liquid sensors, Doppler sensors , Photo-electric sensors, Laser

and image sensors etc. These sensors are using for various usage. Combination of these sensors to produce a

maximum adoptable system will produce a positive result to the data capturing part. Here would like to

specify some main sensors and specialty of those sensors. Such as: Infrared Sensors (Accurate), PVDF

sensors (smaller but affected by temperature), Acoustic sensors (easily influenced by vibration) and Doppler

sensors (less accuracy).

Fig. 2: Grove piezoelectric vibration sensor dimension in mm

According to the domain analysis and literature review, we have identified ‘What we really need to

capture through the sensors’. The factor we need to consider while checking the patient is artery pulse

pressure. Since many pressure sensors available in the market, piezoelectric type sensor is the most suitable

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for the ‘Nadi’ pressure identification. Piezoelectric sensors are used to capture the dynamic pressure changes

which can convert mechanical energy into the electronic energy form.

Grove-Piezo vibration sensor is the selected piezo electric sensor type for our research after analyzing

and testing various sensor types. Grove piezo vibration sensor’s special features are flexibility, vibration,

impact and touch sensitive type of sensor with 0.001Hz~1000MHz dimension, 0°C ~ 85°C Operating

Temperature, 0.625" dimension and 28 µm thickness. Fig. 2 shows the selected grove piezoelectric vibration

sensor’s dimension details. Reason for this sensor selection is Grove piezoelectric vibration sensors can be

used with wide frequency response, wide temperature range, wide dynamic range, adjustable sensitivity and

high receptivity for strong impact.

3.2. Design of the Device

According to the previous literature review and domain experts’ advice, we designed our device as a

wearable band. Flexible electronics is a new technology where the electronic circuit can mount and can use

as a flexible device, which also can work as printed circuit board. The problem we faced while setup using

flexible circuit board is to mount three sensors across the artery pulse. And hard identify the position and fix

in the correct place.

The wristband is the best suitable set up to fix the sensors and which can design as the standard product.

However, we couldn’t able to afford the cost of the wristband because of it found for an alternative

inexpensive solution.

Fig. 3: our system design

Circuit appended glove setup is another way to design the device. In glove setup, sensors can be fixed in

the three positions and, can use by a doctor to check the patient. However, the problem with the soft circuited

glove is doctors or tester’s pulse vibration, hand movement, and cells movement will affect the patient’s

pulse readings. This will cause inaccuracy in the reading. For the device design setup we selected Velcro

tape to fix the sensor and to acquire accurate readings. Fig. 3 shows the device we designed for the pulse

acquisition.

3.3. Data Acquisition

The details of the device experimental and setup methods used for detecting human Nadi readings and

the circuits used for signal conditioning are given below. In this system design, we can categorize the set up

into two different types.

1. Detecting the ‘Nadi’ position

2. Signal processing

Identifying the accurate Nadi position is one of the challenging tasks. Nadi positioning will deviate

person to person. For some people ‘Nadi’ position will be in the upper layer of the skin level and for some

people will in the deep level of the skin. In this research, we select the people with normal ‘Nadi’ positioning

to reduce the complexity and avoid the complexity in processing.

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We got the domain experts help to capture the correct ‘Nadi’ position to proceed further data collection

part. Below image will describe the positions of the ‘Nadi’ respectively ‘Vada’, ‘Pitta’ and ‘Kapa’. The

Grove - Piezo Vibration Sensor have the advantage to detect the physical pressure change and convert into

the mechanical measurement when the pressure pressed in the wrist. The sensor is placed on the skin surface

over a palpable pulse. The ‘Nadi’ signals obtained from the Grove - Piezo Vibration Sensors are passed to

the signal processing circuitry. Since the sensor reading will be converted into the voltage reading using the

Arduino (Arduino reading between 0V to 5V). Fig. 4 will show the signal processing and ‘nadi’

identification system’s setup block diagram.

The physical signal of the ‘Vada’, ‘Pitta’, and ‘Kapha’ will captured using circuited three pressure

sensors. The reading will be converted using Arduino Uno where the input voltage is around 7V to 12V. The

sensor will fix into 50mA DC current for 3.3V pin.

Fig. 4: Block diagram of the Nadi identification

Arduino will provide the reading between (0- 1023) which need to be converted into the voltage.

Mathematically this can be shown as:

1

1023 5

Ardiuno reading (1)

5

1022Float voltage sensor Value X (2)

Converted reading will be pre-processed using Matlab. The output of the Arduino is connected to the

filter to filter out unwanted readings presented in the pulse wave. A low-pass filter is designed at the cut-off

frequency of 100Hz. The cut-off frequency is calculated by the following equation:

1

2f

RC (3)

Voltage to frequency conversion part can be design using hardware solution and software solution.

While analysing the hardware solution there are many voltage to frequency convertors available in the

market. (Crystal oscilloscope and other transducer) However in here we selected software solution to solve

this. Frequency conversion part is held using Fourier Transformation. In the readings around 50Hz of the

frequency was environmental noises. According to the literature analysis and the domain experts’ knowledge,

‘Nadi’ frequency ranges were provided. Vada (1.35Hz to 1.58 Hz), Pitta (1.16Hz to 1.33 Hz) and Kapha

(0.833Hz to 1.2 Hz) [4].

3.4. Pre-Processing

Pre-processing of the acquired signal is important as the received pulse signal may contain noises due to

the interaction of the pulse signal with the muscles, environmental noises during the time of signal acquiring.

Consequently, selecting and designing of the suitable filter to noise removal is an important step of pre-

processing module. In signal processing filters can be used to clear the waves and to obtain a de-noised and

smoothened wave. That may remove the unwanted components or features from a signal. For that we should

have an prior idea of what sort of noised and disturbances we meet in our scenario and what are the

frequencies does our signals belong to and then we can move towards a better filtration mechanism. The

Physical signal

from radial

artery

Capture using

three sensors

Convert the

reading into

voltage using Arduino

Filtering using

software filter

Convert into

the frequency

range

30

frequency ranges can be categorized in to several band-forms which consider the frequency bands the filters

should pass and which frequencies it should reject.

In this research, we are following software filtering techniques to remove noises. The software filter

designed is ‘Savitzky-Golay filter’, which can smooth the waves. Savitzky-Golay smoothing filters perform

much better than standard averaging FIR filters, which tend to filter out a significant portion of the signal's

high-frequency content along with the noise.

The following equation defines the general filter equation according to Savitzky-Golay. The particular

behaviour of the filter can be influenced by choosing appropriate filter coefficients.

1

2

1

2

1np

t i t inp

i

y a Xn

(4)

Savitzky-Golay filter is kind of low-pass filter, where k - degree of polynomial and f - frame size

parameters are considering while smoothing. Below graphic will describe how the smoothing and noise

removal function will occurring using Savitzky-Golay filter.

3.5. Feature Extraction and Pulse Detection

Acquired signal are exported in MATLAB using load command. Time and frequency domain features

are extracted using digital signal processing techniques. In here we used Fourier analysis technique to

convert the time and frequency domain. Nadi frequency is measured using the power spectrum of the signal.

The peak value of the power spectrum is considered as the frequency of that Nadi signal. Through the feature

extraction ‘Vada’, ‘Pitta’, ‘Kapha’ Nadi patterns will be extracted separately and validate the nadi patterns

using the predefined nadi pattern styles and frequency range.

4. Result

Several sensor based experiments were conducted by us to determine the best suitable sensor and to

acquire accurate wave’s pattern. Different types of sensors were selected and checked with various samples

and developed signal conditioning circuit. The result obtained from the sensors were analyzed and discussed

with the domain experts. To implement this pulse detecting system selecting the sensor is the difficult and

we tested out different types of sensors. While using piezoelectric sensor, it was observed that the pulse

waves with full of noisy and the pulse range and frequency ranges were deviated from the standard range.

Fig. 5: Pulse waves using various vibration sensors.

Secondly we tried Piezosense Minisense 100, which obtain the better result than piezo electric sensor.

Piezoelectric Minisense 100 works as the cantilever beams accelerometer. In Cantilever beam design, beam

carries the load to support the force against mounted and shear stress. In the design of the sensor, beam

mounted horizontally and the vertically fixed plate creates the bending in the beam, due to the inertia of the

beam. Strain in the beam will create the piezo elective as responsive to create the voltage output across the

(b) Piezosense Minisense 100

(a) Piezo electric sensor pulse reading

(c) Grove-piezo vibration sensor

(d) Pulse sensor

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electrodes of the sensor. Piezo electric Minisense 100 is high responsive to the outside vibrations. The

sensitivity of the sensor and the responsive to the frequency change will effect higher in the result. In the real

environment, while checking with the pulse waves, environment factors influencing very higher in the result

because ‘Nadi’ frequency range is between 0.833Hz to 1.58 Hz [piezoelectric sensor for human pulse

detection]. Environment frequency range is above 50 Hz.

Pulse sensor is the market available sensor which cost higher. Pulse sensor’s reading and the out is

systematic and clear output. However price rate very higher than Grove piezo electric vibration sensor.

Grove piezoelectric vibration sensor is high sensitive and flexible to capture the pulse reading throughout the

research. Main Advantage is the design of the sensor. Grove piezoelectric sensor designed as the flexible

component comprising a 28 µm thick piezoelectric PVDF polymer film with screen-printed Ag-ink

electrodes laminated to a 0.125 mm polyester substrate and fitted with two crimped contacts. Hence, the

design pattern helped to be flexible to get a proper reading which data acquiring. In addition to that it is less

affected by the environmental effects when compared to Minisense 100. The main reason for this deviation is

Minisense 100 is more sensitivity than Grove piezoelectric sensor. Fig. 5 shows the obtained results using

various sensor type used for research.

Through the analysis finally identified that Grove-piezo vibration sensor is most suitable for developing

the device. Fig. 6 will provide the data collected samples using the Grove-piezo vibration sensor for Vada

Nadi pulse pattern.

Fig. 6: Result of the grove piezoelectric vibration sensor

5. Discussion

Our system designed to obtain the Ayurvedic traditional medicine waves patterns respectively Vada,

Pitta and Kapha. In this research paper we will discuss on designing the device with suitable sensor and

obtain the accurate wave pattern. This research contain the component of selecting best sensor, selecting

best design type to get optimum accurate result and best suitable mechanism to capture the waves pattern.

5.1. Comparison with Earlier Systems’ Sensor Selection

When comparing these sensors type and devices with our system, HMX-Pulse sensor used for finger tips

PPG signal. While using HMX-Pulse sensor for fingertip palpitation signal detection, they used

Photoplethysmogram transducer to operate with PPG100C amplifiers to record blood volume pulse waves.

In here mainly they used optical pressure sensors TSD200 PPG. The high-level functionality of these

processes is TSD200 transducer consist infrared emitter and photodiode detector which transmit the changes

in infrared reflection according to changing blood flow. HMX pulse sensor was used in this system, which

works as strain cantilever beam transducer.

Due to the Haemoglobin (Hg) in the blood, infrared light wavelength highly reflects. When the

transducer fixed near the capillaries, emitter and detector will change according to the capillary blood change.

Above mentioned method is one of the techniques used to capture the pulse waves. However in that research,

researcher captured only the pulse waves and analysed the pulse changes in the finger tips. In this research,

researchers considering acquiring the pulse waves from the finger tips are the best place. But in our research

we considering wrist as the best suitable place to acquire the pulse waves to obtain the Vada, Pitta and Kapha

waves. According to this research they designed a data acquisition system called “MP150 Kit” integrated

with PPG sensor and MP150 Biopac system. ‘MP150 kit’ introduced by Biopac System, Inc. Which can use

(a) Pulse wave pattern of sample 1

(b) Pulse wave pattern of sample 2

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for Ethernet-ready data acquisition and analysis, record multiple channels with differing sample rates and

record at speeds up to 400 kHz.

Problem with this system is couldn’t acquire tridosha separately. Millivolt Output pressure is another

sensor to capture signal. Millivolt output pressure sensors have no amplifiers, filters, transistors or any other

active component which works under highest frequency response and lowest power consumption. In this

research researcher used three Millivolt output pressure sensors and mounted in the wrist and used NI USB-

6210 data acquisition card. Millivolt output pressure sensor is kind of strain gauge transducer.

Using piezoelectric transducer pulse signal can be obtained and researchers researched on the usability of

it as mentioned in previous section. Piezoelectric transducer is place on the skin and skin passed through a

signal processing. Position of the pulse is identified and adjusts to obtain appropriate pulse waves. Fig. 7

describes the pulse detection system using digital storage oscilloscope in their research.

Fig. 7: Block diagram of pulse detection system using Digital storage oscilloscope

According to these sensors used by different researchers, some of the sensors are working under the old

technologies. However we identified piezoelectric sensors is the best suit for these kinds of researches while

other sensors are good with some other specialties. Combining of these different types of sensors and

produce a multi model sensor is the efficient and accurate to obtain proper result.

5.2. Comparison with Device Designing

Design of the device is also has a very high impact on this study. There we have to consider about how

the sensors contact wrist to derive radial pulse data while reducing noise, how sensors are attached to the

device and the ease of use. In almost all the studies they have placed the pulse sensors side-by-side without

contacting one another over the radial artery in the position of the fingers Inch opening of wrist. According

to the literature the most appropriate touching area of the pulse sensor should be 30 mm for the trade-off in

repeatability and sensitivity [5].

In the context of the methods that attaches the pulse sensor to the wrist we found few options that have

been considered by other researchers. Most of the researchers have used the belt, as it is easy to fix and has

less deviation. And few researches have been conducted using a glove that can worn by the clinicians to feel

the patients pulse with three sensors attached to three fingers. But in here pulse waves acquired can be easily

influenced by the unstable finger contact pressure and noise and the pulse of the clinician. Other two

methods are rack-pinion and Electrodes [5]. Compared to other methods they are difficult to fix,

uncomfortable and has more deviation. To obtain correct pulse patterns stable and even pressure should be

provided on the sensors which placed on correct positions of wrist radial artery. In above designs they have

used few methods to apply pressure. In rack method those researchers have used copper disks incrementally

up to 20g. In glove clinician apply the pressure but that is unstable and can’t guarantee the same pressure on

all three sensors [5]. Appling pressure by using small size sphygmomanometer cuff [3], Inflated by the air

sent through an elastic tube is a better method. However, the high pressure of the cuff would produce

negative consequences such as the blood flow in the vein might congest. So according to the literature using

mechanical screw or Velcro tape [6] on belt and increase the pressure gradually is more successful. Then to

digitize the electrical signal obtained proportional to the pulse waveform signal in ‘Nadi Tarangini’ they

have used a 16-bit multifunction data acquisition card (NI USB-6210) [7]. In another study conducted in

India used an Arduino Uno for this task [8].

Comparing all these devices most of the devices are designed as wearable device and some of them are

designed as doctor wearable device. Wearable device designed by University of Peradeniya Electronic 33

Engineering students. In there the device was designed as doctor wearable device. The problem with that

device is less accuracy and the system obtains doctor’s pulse vibration too. With these analyses we designed

a device of patient wearable. In the device to reduce noises and increase interfere between skin and sensor,

used Velcro tape wearable bands shown in Figure.3.

6. Conclusion

Hence it can be concluded that “Nadi pareeksha” gives a new direction for the doctors for the detection

of diseases in early stages. If it is properly applied for the detection of various diseases like diabetic and

cancer, detection accuracy improves and as a result most of the people who are suffering from these diseases

can be cured in early stages.

But there are only very few researches done in traditional medical field due to lack of experts, domain

knowledge, and generation gap. Within those limited research up to now there are no workable commercial

products available in the world market. Still all systems are in the research level.

So in here we have tried to design a suitable sensor based pulse detection system which works on the

principle of Traditional Ayurveda Medicine. The diagnosis of a disease depends on certain specific

parameters like blood viscosity, blood volume, etc. So according to our study these parameters can be

analyzed better by a specific kind of sensor rather than any sensor. Up to now we have done sensor selection

and came up to the level of identification of pulse patterns. In future we expect to extend this research to

identify diseases using pre-identified wave patterns.

7. Future Work

Currently using our device can decompose the pulse into three components Vada, Pitta and Kappa

according to the Ayurvedic principles. We are working on to automate the diagnose diseases by identifying

the pulse patterns in the human body using Ayurvedic Nadi Pareeksha principles. And identify Diabetics

type 2 according to the different patterns and combinations of the Nadi: Vada, Pita and Kappa as our future

work.

8. References

[1] "Nadi Pariksha," in Ayurveda Amrutanam, Ayurveda Amrutanam. [Online]. Available:

http://ayurveda4all.weebly.com/nadi-pariksha.html. Accessed: Aug. 12, 2016.

[2] “WHAT IS NADI PARIKSHA (Ayurvedic Pulse Diagnosis),” AYURVEDANTAYOGA by NADI VAIDYA

RAVISHANKAR KRISHNAMURTHY, 2009. [Online]. Available:

https://ayurvedantayoga.wordpress.com/2009/03/15/what-is-nadi-pariksha/. [Accessed: 13-Aug-2016].

[3] A. E. Kalange and S. A. Gangal, "Piezoelectric sensor for human pulse detection," Defence Science Journal, vol.

57, no. 1, pp. 109–114, Jan. 2007.

[4] “Pressure Sensors,” EngineersGarage, 2012. [Online]. Available:

http://www.engineersgarage.com/articles/pressure-sensors-types-working. [Accessed: 13-Jul-2016].

[5] L. Xu, M. Q. Meng, C. Shi, K. Wang, and N. Li, "Quantitative analyses of pulse images in traditional Chinese

medicine," Medical Acupuncture, vol. 20, no. 3, pp. 175–189, Aug. 2008.

[6] D. Gaddam et al., "A Survey on Nadi Pareeksha for Early Detection of Several Diseases & Computational Models

using Nadi Patterns," International Journal of Computer Science and Information Technologies, vol. 6, no. 4, pp.

3424–3425, 2015.

[7] A. Joshi, A. Kulkarni, S. Chandran, J. V. K, and K. B. D, "Nadi Tarangini: A Pulse Based Diagnostic System,"

Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC), vol. 29, pp.

2207–2210, Aug. 2007.

[8] R. N, J. M. Shivaram, and Shridhar, "Design development of a system for Nadi Pariksha," International Journal of

Engineering Research and, vol. 4, no. 06, Jun. 2015.

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