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International Journal of Electronics

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Evaluating sustainable energy harvesting systemsfor human implantable sensors

Faris M. AL-Oqla, Amjad A. Omar & Osama Fares

To cite this article: Faris M. AL-Oqla, Amjad A. Omar & Osama Fares (2017): Evaluatingsustainable energy harvesting systems for human implantable sensors, International Journal ofElectronics, DOI: 10.1080/00207217.2017.1378377

To link to this article: http://dx.doi.org/10.1080/00207217.2017.1378377

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Publisher: Taylor & Francis

Journal: International Journal of Electronics

DOI: 10.1080/00207217.2017.1378377

Evaluating sustainable energy harvesting systems for human implantable sensors

Faris M. AL-Oqla1,*

, Amjad A. Omar2, Osama Fares

3

1Department of Mechanical Engineering, Faculty of Engineering, The Hashemite

University, Zarqa 13133, Jordan

2Department of Electrical, Electronics and Communications Engineering, American

University of Ras Al Khaimah, Ras Al Khaimah, UAE.

3Electrical Engineering Department, Isra University, Amman, Jordan.

*fmaloqla@hu.edu.jo

ABSTRACT

Achieving most appropriate energy harvesting technique for human implantable sensors is still

challenging for the industry where keen decisions have to be performed. Moreover, the available

polymeric based composite materials are offering plentiful renewable applications that can help

sustainable development as being useful for the energy harvesting systems such as photovoltaic,

piezoelectric, thermoelectric devices as well as other energy storage systems. This work presents

an expert-based model capable of better evaluating and examining various available renewable

energy harvesting techniques in urban surroundings subject to various technical and economic,

often conflicting, criteria. Wide evaluation criteria have been adopted in the proposed model

after examining their suitability as well as ensuring the expediency and reliability of the model

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by worldwide experts’ feedback. The model includes establishing an analytic hierarchy structure

with simultaneous twelve conflicting factors to establish a systematic road map for designers to

better assess such techniques for human implantable medical sensors. The energy harvesting

techniques considered were limited to Wireless, Thermoelectric, Infrared Radiator, Piezoelectric,

Magnetic Induction, and Electrostatic Energy Harvesters. Results have demonstrated that the

best decision was in favor of wireless harvesting technology for the medical sensors as it is

preferable by most of the considered evaluation criteria in the model. The precise obtained

judgments were justifiable and have narrow marginal inconsistency. This work also demonstrates

the robustness in the results thorough systematic sensitivity analysis.

Keywords: Energy harvesting; Sustainable materials; Renewable energy; Uncertainty;

Biosensors; wireless network; Sustainable healthcare applications.

1. Introduction

The rapid trend in the field of microelectronics integrated with new advanced sustainable

materials towards creating wearable miniaturized systems that require small amounts of energy

has stirred major research and development effort in the field of energy harvesting to power

these systems (AL-Oqla, Sapuan, Anwer, Jawaid, & Hoque, 2015; Lowy, Tender, Zeikus, Park,

& Lovley, 2006; Mateu & Moll, 2005; Nunes-Pereira, Sencadas, Correia, Rocha, & Lanceros-

Méndez, 2013). In general, energy harvesting involves deriving renewable energy from external

sources like kinetic, thermal and wind energies. Macromolecular structures of polymeric

materials on the other hand, as light-weight, flexible with low-cost types of materials are

promising for plentiful renewable applications that can help sustainable development in

developing countries including being useful for the energy harvesting systems such as

photovoltaic, piezoelectric, thermoelectric, triboelectric devices as well as other energy storage

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systems (AL-Oqla, 2017). The utilization of polymers in such devices as well as other

electromechanical actuators and electrochromic devices have dramatically enhanced their

potential for being used as sustainable electrically driving sensors, robotics, vehicles, artificial

muscles and implantable sensors (Pang, Lee, & Suh, 2013).

Energy harvesting process is normally applied to small autonomous robots, wearable electronic

devices, and wireless sensor networks (Hannan, Mutashar, Samad, & Hussain, 2014). Energy

harvesting from sustainable and renewable sources like that of solar-light, temperature

differences and vibrations have been utilized commercially as one alternative solution for solar

power calculators, human-power-driven devices like shake-driven flashlights (Hyland, Hunter,

Liu, Veety, & Vashaee, 2016), and thermal-powered wristwatches, among others. Therefore,

energy harvesting has been proven to be a viable replacement for batteries in modern electronic

devices.

Energy harvesting is used mainly in places where it is not convenient or healthy to power a

device using a battery or other power supply. An example is sustainable implantable medical

devices (Hannan et al., 2014) that are implanted inside the human body which makes it

inconvenient and rather costly to remove these devices from the body, charge them, and return

them back to their locations inside the body. Moreover, these devices may use wires to connect

them to the power source which may cause skin infection, discomfort, and can be hazardous to

health (Hannan et al., 2014). In order to minimize power consumption in biomedical

microsystems as well as increase their battery life, design styles that require low power

consumption have to be followed such as utilizing low power transducers, passive sensors and

low-power ICs. Moreover, the distinguished biomedical device characteristic of consuming low

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power has increased the potential of using the renewable energy harvesting schemes for

powering these devices and extending their lifetime.

There are different techniques for energy harvesting, but in this paper we will focus on some of

the most common ones; infrared radiator harvester (Goto, Nakagawa, Nakamura, & Kawata,

2001), wireless energy harvester (Challa, Prasad, Shi, & Fisher, 2008; Lemey, Declercq, &

Rogier, 2014; Ramos & Popovic, 2015), thermoelectric energy harvester (S. Yang, Lee, & Cong,

2010; Y. Yang et al., 2012), piezoelectric energy harvester (Khaligh, Zeng, & Zheng, 2010),

magnetic induction harvester (Cheng, Wang, & Arnold, 2007; Su, Zu, & Zhu, 2013), and

electrostatic energy harvester (Tashiro et al., 2000; Yen & Lang, 2006).

Although this paper will focus on the six main techniques; Wireless, Thermoelectric, Infrared

Radiator, Piezoelectric, Magnetic Induction, and Electrostatic Energy Harvesters, there are other

techniques which are not as common but can be used for energy harvesting (Sojan & Kulkarni,

2016). These include mechanical energy in the form of vibration from human motions,

structures, vehicles and ocean waves (Zuo & Tang, 2013), stress and strain, heat sources like

engines (Yildiz & Coogler, 2014), various activities that generate human energy (Sojan &

Kulkarni, 2016), sound energy (Kralov, Terzieva, & Ignatov, 2011), and wind energy (Weimer,

Paing, & Zane, 2006). A full comparison between all the energy harvesting techniques can be

found in (Sojan & Kulkarni, 2016). In what follows, we will briefly point out for each of the six

harvesting technique its advantages and disadvantages.

An infrared radiator harvester (Goto et al., 2001; Murakawa, Kobayashi, Nakamura, & Kawata,

1999) is used to capture energy from a high power nearby laser source to be used in applications

such as brain and cardiac pacemakers or other bio-implantable devices. This technique has

several weaknesses; including the large size, skin heating, low harvested energy, and high power

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source (Goto et al., 2001; Murakawa et al., 1999). This type of harvester may produce up to a

few milliwatts of power and its main element is a photodiode array.

A Wireless Energy Harvester (Challa et al., 2008; Lemey et al., 2014; Ramos & Popovic, 2015),

on the other hand, captures the ambient energy from the atmosphere that is in the form of

electromagnetic radiation and converts it into electric energy. This EM radiation may come from

WI-FI transmitters, TV masts, cell phone antennas, RF and microwave signals, etc. This

requires a wideband receiver to harvest energy from 500 MHz to 10 GHz. This technique,

although produces low power that can be in the microwatt or low milliwatt, can be used for

applications like wireless sensor networks, RFID tags, textiles (Lemey et al., 2014), etc. The

main element in this harvester is the wideband antenna and the receiver circuit.

A thermoelectric energy harvester (S. Yang et al., 2010; Y. Yang et al., 2012) exploits

temperature differences to produce energy. Although the power produced by this harvester is

small (up to tens of microwatts of power), it is sufficient for applications such as implantable

medical sensors used in implanted nerve and muscle stimulators, cochlear hearing replacements,

and wireless patient diagnostics (Hannan et al., 2014).

A thermoelectric generator usually has a lot of thermocouples thermally interconnected in

parallel and electrically interconnected in series to form a thermopile (Hannan et al., 2014).

Piezoelectric energy harvester (Khaligh et al., 2010) on the other side, relies on the ability of

piezoelectric materials to generate electric power if subjected to deformation. The amount of

power generated can be in the microwatt or milliwatt depending on where the piezoelectric

material is embedded which could be either in the knee, heal, close to the heart, etc.

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A Magnetic induction harvester (Cheng et al., 2007; Su et al., 2013) relies on the energy induced

in a circuit due to the changing magnetic flux penetrating that circuit. This harvester can be

fixed on the ankle and will provide a few microwatts of electric power to diminutive biomedical

devices (Cheng et al., 2007; Hannan et al., 2014).

Finally, an electrostatic induction harvester produces electricity by means of electrostatic

induction by converting mechanical vibration into electrical energy. This is carried out by

moving part of a transducer versus an electrical field (Tashiro et al., 2000; Yen & Lang, 2006)

and produces power in tens of microwatt.

The capabilities, performance and design attributes of the current available sustainable energy

harvesting techniques are strongly influenced by and depend on simultaneous wide conflicting

variables, criteria and characteristics. These usually restrict expanding energy harvesting

applications for health monitoring and make the selection of a reliable energy harvesting type a

sophisticated and multi-criteria decision making (MCDM) problem (Al-Oqla & Omar, 2012; Al-

Oqla & Omar, 2015; F. M. AL-Oqla, M. S. Sapuan, M. R. Ishak, & A. A. Nuraini, 2015; AL-

Oqla & Sapuan, 2015; Al-Widyan & Al-Oqla, 2014; Sharafi & ELMekkawy, 2014; Testa, Di

Trapani, Foderà, Sgroi, & Tudisca, 2014). Examining such beneficial characteristics for energy

harvesters would dramatically enhance their overall performance as well as expand achieving

better low-cost sustainable design possibilities.

This work thus aims to establish a decision making selection model that is capable of better

evaluating various types of energy harvesters under wide range of simultaneous beneficial but

conflicting evaluation criteria to enable designers to select the most appropriate reliable energy

harvesting technology type for biomedical sensors applications. To our knowledge, this is the

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first work that aims to build an expert-based decision making model capable of better evaluating

and examining the capabilities of various available energy harvesting techniques considering

wide range of technical and economic conflicting criteria simultaneously. This work would

practically reduce human errors during the process of selecting the energy harvesting technique

as well as establish a systematic road map for better examining and evaluating these various

energy harvesting techniques.

2. Methodology

2.1 The analytical hierarchy process (AHP)

AHP is a reliable and commonly used MCDM tool considered to solve problems with multiple

conflicting criteria. The AHP technique is gaining popularity as it is understandable and easy to

apply. It was tested in several domains like biomedical, environmental, energy systems,

ecological, biological (F. M. AL-Oqla, S. Sapuan, M. Ishak, & A. Nuraini, 2015b; AL-Oqla,

Sapuan, Ishak, & Nuraini, 2016; Al-Widyan & Al-Oqla, 2011), as well as various electrical and

industrial applications (AL-Oqla & Hayajneh, 2007; Al-Oqla & Omar, 2012; Al-Oqla & Omar,

2015).

The analytical hierarchy process utilizes pair-wise comparisons via verbal judgments and

accurate ratio and scale priorities to capture both objective and subjective evaluations. It is also

capable of providing a systematic mechanism for examining the consistency of the evaluations to

reduce the decision bias (F. M. AL-Oqla et al., 2015; F. M. AL-Oqla, S. Sapuan, M. Ishak, & A.

Nuraini, 2015a; Dweiri & Al-Oqla, 2006). The first step for the AHP is to split the main

objective into sub-objectives, developing from the general to the specific. Thus, a simple AHP

model should contain a goal, criteria and alternatives. Criteria would then be further separated

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and recognized into a reasonable number. As the more criteria is involved, the less significant

each criteria would be. Relative scores in the hierarchy will yield an n x n matrix A, which

captures the judgments of experts regarding the pair-wise comparisons. Next, computations are

initiated for normalizing and achieving comparative weights in each distinct matrix. Such

normalized weight (Wi) of a distinguished factor can be gained via calculating the geometric

mean of the ith row, i ∈[1,…,n] as expressed in equation (1) and at that time normalizing all

rows of these geometric means in the decision matrix. Moreover, the Eigen column vector,

which denotes the relative normalized weight (W) can be achieved using equation (2).

1

n

ni ijj

GM a=

= ∏ (1)

1 11

2 21

1

/

/

...

/

n

ii

n

ii

n

n n ii

W GM GM

W GM GMW

W GM GM

=

=

=

=

= = =

(2)

For the matrix to be consistent, then the following should be satisfied as in equation (3).

A .w = n w (3)

Where A, w , n are the pairwise comparison matrix, the Eigen vector and the size of the matrix,

respectively. An inconsistency test is required in the analytical hierarchy process to confirm the

expert judgment. Therefore, an inconsistency ratio of less than 0.1 must be attained for a

reasonably consistent judgment. If not, the subjective judgment should be revised (AL-Oqla et

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al., 2016; Saaty, 2003). A consistency measure or index is used as nonconformity of consistency.

A flow for the steps used in this work is shown in Figure 1.

2.2 The AHP model for this study

To explore the factors that affect the proper evaluation and the decision making process of

selecting the most suitable technique of energy harvesting for human implantable sensors, Expert

Choice™ software (Faris M AL-Oqla, SM Sapuan, et al., 2015b) was utilized. In this work,

numerous factors that have potential to affect the decision-making for the current problem have

been wisely proposed as illustrated in Table 2. The data collection for assessing the

comparability of the carefully chosen factors was attained via a questionnaire. Such data were

gathered from fourteen carefully selected experts worldwide in academic institutions as well as

professionals. In more detail, eleven experts returned their filled questionnaires, which were

considered- according to AHP method- suitable samples for our model (Al-Oqla & Omar, 2015;

Faris M AL-Oqla, SM Sapuan, et al., 2015b). It has to be mentioned here that 180 comparisons

among the current alternatives with all sub-factors are required to complete the comparisons

matrices for this model. To reduce the work needed from experts, they were sent a matrix

containing all sub-factors and alternatives to judge the weights of the alternative using Saaty’s

scale (from 1-9 according to AHP method) with respect to each sub-criterion. Authors then

converted these numerical values into appropriate weights to fill the judgment matrices.

The energy harvesting alternatives considered here were Wireless Energy Harvester (WEH),

Thermoelectric Energy Harvester (THE), Infrared Radiator Harvester (IRH), Piezoelectric

Energy Harvester (PEH), Magnetic Induction Harvester (MEH), and Electrostatic Energy

Harvester (EEH). The Expert ChoiceTM software package (Faris M AL-Oqla, SM Sapuan, et al.,

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2015b) was used to carry out the comparison, where expert’s judgments were established by

weighting pairs of major criteria according to the main goal. The process was performed several

times to compare all sub-factors from the main factors points of view and compare the

alternatives to the sub-factors.

3. Results and Discussion

3.1 Comparing Factors and Sub- Factors

An appropriate set of pair-wise comparison judgments was developed and captured for all levels

in the model in appropriate matrices. The judgments were developed using Saaty's 1–9 scale

(Faris M AL-Oqla, SM Sapuan, et al., 2015b). In developing such matrices, the expert starts by

relating pairs of main items with respect to the main goal point of view by assigning their

importance. Verbal assessment are usually utilized to facilitate as well as help the expert in

summarizing his knowledge efficiently.

The priority of each main factor in the model was calculated according to the AHP method and

interpreted in Figure 2, where the reliability of the power source (RPS) criterion was clearly the

most important item in the model as it has a total aggregate weight of 32.7%, followed by the

technical maturity (TM) criterion with a total aggregate weight of 32.4%. On the other hand, the

power supply cost (PSC) factor was shown to be the insignificant factor with a weight of 16.4%.

3.2 Alternatives Pair wise Comparison

In order to demonstrate the relative significance and evaluate the energy harvesting alternatives

(EHAs) regarding each main factor in the model, a series of aggregated priorities for the

alternatives with respect to each main criterion was calculated. The priorities of the energy

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harvesting alternatives with respect to the reliability of the power source criterion was calculated

and interpreted in Figure 3. Concerning the reliability of the power source criterion, it was found

that WEH alternative was the best choice with an aggregated weight of 18.0%, followed by the

MEH alternative with 17.6%, whereas the IEH alternative was the least important with a weight

of 15.3%.

Moreover, the priorities of the EHAs with respect to the technical maturity criterion are

demonstrated in Figure 4. Here Thermoelectric Energy Harvester type was the best regarding

this criterion whereas the Infrared Radiator Harvester option was the worst scenario based on the

technical maturity criterion.

In further investigations about the appropriateness of the energy harvesting techniques with

respect to the criteria considered in table 1, WEH option is found to be the best regarding the

power source cost criterion as shown in Figure 5, and is also the best based on the power source

operation criterion, as shown in Figure 6.

Moreover, the normalized priorities of the alternatives from the main goal stand point is

demonstrated in Figure 7, showing the highest value to be for the Wireless Energy Harvester

type with a total weight of 29.2%, followed by Thermoelectric Energy Harvester type with a

weight of 17.7%, whereas the least priority was for the Infrared Radiator Harvester type with a

weight of 14.4%. The best energy harvester type choice in this case was the Wireless Energy

Harvester. It can be noticed here that the alternatives’ weights (energy harvester types) are close,

without dominant alternative for the model. This clearly demonstrates the difficulty of evaluating

the available energy harvesting techniques for all such evaluation criteria (12 evaluation criteria)

simultaneously without using such a decision making approach, and thus the difficulty of

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reaching an unbiased judgment for selecting the best harvester type without a systematic

selection model like the one used in this study.

On the other hand, to examine and demonstrate the relative importance and evaluate the energy

harvesting alternatives regarding each sub-factor in the model, a series of aggregated priorities

for the alternatives with respect to each individual criterion was calculated for such purpose. For

instance, the normalized priorities of the EHAs with respect to providing adequate constant

electric power sub-criterion was calculated and demonstrated in Figure 8, where both WEH and

MEH were the best choices regarding this sub-criterion, whereas the THE, IHE and PHE were

the worst options according to this particular sub-factor.

As well, the normalized weights or priorities for the rest of alternatives from each particular

criterion standpoint in the model can be found leading to a systematics unbiased decision for the

best alternative of the energy harvesting types for the human implantable sensors. The

summarized results of these priorities are shown in Table 2.

Exploring results shown in Table 2 demonstrates that the best decision was in favor of WEH, as

it was preferable from most of the considered criteria in the model and its preferences was not

the least in any. Other types however, were found not being the best regarding any criterion and

having sometimes the least preferences regarding several evaluation criteria, like that of IEH

type. Other types were found to be neither the worst in all evaluation criteria nor the best except

for one evaluation criterion like the EEH, PEH and MEH types.

3.3 Model Sensitivity Analysis

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As the built model should be reliable to achieve appropriate decisions, it is crucial to examine the

robustness of the reached results. Thus, detailed sensitivity analysis is used to examine the

sensitivity of the model and its response to any disturbance in the weights of the used criteria.

One way of performing such sensitivity examination is by altering the weights of all main factors

in the model to be dominant (having weights of more than 50%) and to observe the dominance of

any alternative in the model, and to examine the rank of the best alternative before increasing

the main factors’ weights. That is; then results are said to be robust if the new ranking does not

dramatically change. The results of the sensitivity performance of the built model are illustrated

in Figure 9. In part (a) of figure 9, the main factor “Reliability of the power source” was altered

to be dominant (having a weight of 52.5% of the whole weight in the model), and the response of

the model to this change was illustrated to its right side. It can be noticed that the best alternative

type (Wireless energy harvester) is still the best alternative type and no dramatic change in the

rank of alternatives occurred. Moreover, no dominance of any alternative was noticed after such

exaggerated change in the weight of this particular main factor in the model. Similarly,

exaggerated weights of the other main factors of the model were assigned and the sensitivity of

the model was examined in parts (b), (c) and (d) of figure 9. This in order gives an evident

indicator of the models’ reliability and the robustness of the gained results.

The overall importance of the utilized factors and their items in this study are made known in

Table 3. It can be realized that the major global contributor was the reliability of the power

source main criterion with a score of 32.7%, and that of the ease of fabrication sub-factor with a

weight of 13.5%, which is in consistence with the trend and the awareness from experts that if

the fabrication process of the power supply harvester for the human implantable sensors is very

complicated, then the practical reliability of the harvester will not be assured which might

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negatively affect the human being. On the other hand, the least globally contributive criterion

was the variety of application with a score of only 2.3%, which was logical as the concern of the

model was the human implantable sensors rather than any other application.

The total sum of priority of the considered main factors regarding each alternative type in this

work is illustrated in Figure 10. It can be noted that the main criterion “reliability of the power

source” was ranked the highest in all EH alternatives except of that of thermoelectric energy

harvester type. On the other hand, the power source cost was the least importance with respect to

all alternatives except Electrostatic Energy Harvester and Infrared Radiator Harvester.

4. Conclusions

The growing interest in renewable energy harvesting techniques paves the way for unlimited

number of green applications and offers wide scenarios for the sustainable future. A compromise

between size, capabilities and permanence has dramatically influenced the decision for adopting

such technologies for human implantable sensors. Here, a model capable of examining various

characteristics and capabilities of renewable energy harvesting techniques was successfully built.

It was demonstrated that the wireless harvesting technique was the best for medical implantable

sensor application (in urban surroundings) regarding the reliability of the power source criterion,

the power source cost criterion and the power source operation one. The best decision was in

favor of WEH, as it was preferable from most of the considered evaluation criteria in the model

and its preferences were not the least in any. The proposed model is capable of enhancing the

reliability of the energy harvesting selection process under uncertain and conflicting environment

as well as expanding their implementations for further applications.

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Acknowledgements

The Deanship of Scientific Research at The Hashemite University is appreciated for supporting

this work.

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Table 1: Factors and their subfactors affecting the evaluation and selection model of the problem

addressed in this work.

Main Factor Sub factor

Reliability of the power source (RPS) • Providing adequate constant electric

power (PACEP)

• Need of storage battery (NSB)

• Operation period before implant

replacement (OPBIR)

Technical maturity (TM) • Technical know-how (TK)

• The ease of design (TED)

• The ease of fabrication (TEF)

Power supply cost (PSC) • Cost of design (CD)

• Cost of manufacturing and Market

maturity (CMMM)

Power source operation (PSO) • Compatibility with chemical

composition inside human body

(CCCIHB)

• Size (ability to be implanted inside

the body) (Size)

• Need of accessories like wires etc.

(NA)

• Variety of applications (VOA)

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Table 2: Summary of the normalized priorities for the alternatives with respect to the main criteria and their subs.

Reliability Technical Maturity

NSB OPBIR TK TED TEF WEH 0.91 1 1 0.87 0.81 THE 1 0.91 0.89 1 1 IEH 0.91 0.83 0.8 0.77 0.52 PEH 1 0.83 0.8 0.77 0.85 MEH 1 0.83 0.91 0.77 0.85 EEH 0.91 0.91 0.8 0.69 0.58 Cost Operation

CD CMMM CCCIHB Size NA VOA WEH 1 1 WEH 0.99 1 1 1 THE 1 0.8 THE 0.99 0.85 0.77 0.74 IEH 0.79 0.8 IEH 0.71 0.74 0.45 0.61 PEH 0.79 0.78 PEH 1 0.92 0.79 0.61MEH 0.89 0.8 MEH 0.85 0.87 0.66 0.62 EEH 0.89 0.94 EEH 0.85 0.87 0.79 0.49

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Table 3. Local (L) and global (G) influences of the developed selection model items and the

corresponding alternative priorities.

Main Factors Sub-factors Alternatives Total Priority

Reliability of the power source (L: .327 G: .327)

Providing adequate constant electric power (L: .297 G: .097)

WEH 0.019 THE 0.013 IEH 0.013 PEH 0.013 MEH 0.019 EEH 0.017

Need of storage battery (L: .371 G: .121)

WEH 0.022 THE 0.024 IEH 0.022 PEH 0.024 MEH 0.024 EEH 0.022

Operation period before implant replacement (L: .332 G: .109)

WEH 0.021 THE 0.019 IEH 0.018 PEH 0.018 MEH 0.018 EEH 0.019

Technical maturity (L: .324 G: .324)

Technical know-how (L: .341 G: .111)

WEH 0.022 THE 0.019 IEH 0.017 PEH 0.017 MEH 0.02 EEH 0.017

The ease of design (L: .243 G: .079)

WEH 0.013 THE 0.015 IEH 0.012 PEH 0.012 MEH 0.012 EEH 0.011

The ease of fabrication (L: .415 G: .135)

WEH 0.021 THE 0.026 IEH 0.014 PEH 0.022 MEH 0.022 EEH 0.015

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Power source cost (L: .164 G: .164)

Cost of design (L: .313 G: .051)

WEH 0.01 THE 0.01IEH 0.008 PEH 0.008MEH 0.009 EEH 0.009

Cost of manufacturing and Market maturity (L: .688 G: .113)

WEH 0.022 THE 0.018IEH 0.018 PEH 0.017MEH 0.018 EEH 0.021

Power source operation (L: .184 G: .184)

Compatibility with chemical composition inside human body (L: .481 G: .089)

WEH 0.017 THE 0.017IEH 0.012 PEH 0.017MEH 0.015 EEH 0.015

Size (ability to be implanted inside the body) (L: .220 G: .041)

WEH 0.008 THE 0.007IEH 0.006 PEH 0.007MEH 0.007 EEH 0.007

Need of accessories like wires etc. (L: .173 G: .032)

WEH 0.006 THE 0.005IEH 0.003 PEH 0.005MEH 0.004 EEH 0.005

Variety of applications (L: .126 G: .023)

WEH 0.005 THE 0.003IEH 0.003 PEH 0.003MEH 0.003 EEH 0.002

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Figure 1. A flow chart for building the model.

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Figure 2: Priorities of the main factors in the model with respect to the main goal.

0.327 0.324

0.164

0.186

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

RPS TM PSC PSO

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Figure 3: Priorities of the energy harvesting alternatives with respect to the reliability of the power source criterion

0.135

0.14

0.145

0.15

0.155

0.16

0.165

0.17

0.175

0.18

0.185

WEH THE IEH PEH MEH EEH

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Figure 4: Priorities of the EHAs with respect to the technical maturity criterion

0

0.05

0.1

0.15

0.2

0.25

WEH THE IEH PEH MEH EEH

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Figure 5: Priorities of the EHAs with respect to the power source cost criterion

0

0.05

0.1

0.15

0.2

0.25

WEH THE IEH PEH MEH EEH

Pri

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Figure 6: Priorities of the EHAs with respect to the power source operation criterion

0

0.05

0.1

0.15

0.2

0.25

WEH THE IEH PEH MEH EEH

Pri

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Figure 7

0.0

0.0

0.0

0.0

0

0.

0.

0.

0.

0

Pri

ori

ty

7: Priorities

0

02

04

06

08

0.1

12

14

16

18

0.2

WEH

of the EHA

THE

A with respe

IEH

28

ect to the ma

PEH

ain goal

MEH EEH

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Figure 8power s

0

0.2

0.4

0.6

0.8

1.2

Norm

ali

zed

pri

ori

ties

8: Normalizsub-criterion

0

2

4

6

8

1

2

WEH

zed prioritiesn

THE

s of the EHA

IEH

29

A with resp

PEH

ect to provi

MEH

ding adequa

EEH

ate constantt electric

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Figure 9: Sensitivity analysis of the built model by making each main factor dominant with a

weight of 52.5%.

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Figure 10: The total sum of priority of the considered main factors regarding the considered

alternatives.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Electrostatic E Infrared Radiat Magnetic Induct Piezoelectric E Thermoelectric Wireless Energy

Su

m o

f P

riori

tiy

Power source cost (L: .164 G: .164) Power source operation (L: .184 G: .184)

Reliability of the power source (L: .327 G: .327) Technical maturity (L: .324 G: .324)

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Figure Captions

Figure 1. A flow chart of the steps used in building this work.

Figure 2: Priorities of the main factors in the model with respect to the main goal.

Figure 3: Priorities of the energy harvesting alternatives with respect to the reliability of the power source criterion

Figure 4: Priorities of the EHAs with respect to the technical maturity criterion

Figure 5: Priorities of the EHAs with respect to the power source cost criterion

Figure 6: Priorities of the EHAs with respect to the power source operation criterion

Figure 7: Priorities of the EHA with respect to the main goal

Figure 8: Normalized priorities of the EHA with respect to providing adequate constant electric power sub-criterion

Figure 9: Sensitivity analysis of the built model by making each main factor dominant with a weight of 52.5%.

Figure 10: The total sum of priority of the considered main factors regarding the considered alternatives.