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Journal of Medical Systems(Manuscript No. MS:6699892415035920)

A Fatigue Measuring Protocol for Wireless Body

Area Sensor Networks

Sana Akram1 Nadeem Javaid2, Ashfaq Ahmad2 Zahoor Ali Khan3 Muhammad Imran4 Mohsen Guizani5 Amir Hayat2 Manzoor Ilahi2

Received: / Accepted: May 26, 2015

This manuscript should be cited as:

Sana Akram, Nadeem Javaid, Ashfaq Ahmad, Zahoor Ali Khan, Muhammad Imran, MohsenGuizani, Amir Hayat, Manzoor Ilahi, A Fatigue Measuring Protocol for Wireless Body AreaSensor Networks, Journal of Medical Systems (in press), 2015.

Abstract As players and soldiers preform strenuous exercises and do difficultand tiring duties, they are usually the common victims of muscular fatigue.Keeping this in mind, we propose FAtigue MEasurement (FAME) protocolfor soccer players and soldiers using in-vivo sensors for Wireless Body AreaSensor Networks (WBASNs). In FAME, we introduce a composite parameterfor fatigue measurement by setting a threshold level for each sensor. Whenever,any sensed data exceeds its threshold level, the players or soldiers are declaredto be in a state of fatigue. Moreover, we use a vibration pad for the relaxation of

fatigued muscles, and then utilize the vibrational energy by means of vibrationdetection circuit to recharge the in-vivo sensors. The induction circuit achievesabout 68% link efficiency. Simulation results show better performance of theproposed FAME protocol, in the chosen scenarios, as compared to an existingWireless Soccer Team Monitoring (WSTM) protocol in terms of the selectedmetrics.

Keywords Wireless body area sensor networks multiple sinks fatiguemeasurement routing link efficiency voltage gain vibration detection andvibration energy

1Institute of Space Technology, Islamabad 44000, Pakistan2COMSATS Institute of Information Technology, Islamabad 44000, Pakistan3CIS, Higher Colleges of Technology, Fujairah Campus, UAE4King Saud University, Saudi Arabia5Qatar University, QatarCorresponding author is from Department of Computer Science, [email protected], www.njavaid.com


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2 Sana Akram et al. A Fatigue Measuring Protocol for WBANs

1 Introduction

Wireless Body Area Sensor Networks (WBASNs) have attracted attentionsince 1995. The objective was to make communication at and near the human

body possible to measure different body attributes. The whole idea behinddeveloping these networks is to provide better health care services to patientsand elderly people. With modern medicine and better lifestyle, the averageexpectation of life has increased. Nowadays, these devices are more neededthan ever before since our society has a higher number of elderly people whoneed continuous monitoring and urgent medical aid. WBASN is excellent forthis purpose with very little human interventions [1].

Besides health monitoring of elderly people and critical patients, WBASNsare also used in many other fields where continuous and remote monitoring ofhealth is necessary. These fields include players in different sports, astronautsin space, soldiers in battle fields, etc.

For health monitoring of athletes and other players, parameters like res-piratory rate, heart rate, blood oxygen, blood glucose and body temperature

are important. Another important issue with players is what is called musclesfatigue. Commercially, many sensors are available for fatigue measurementslike temperature and heartbeat measuring sensors. However, the sensors mea-suring amount of lactic acid in muscles can provide more accurate results. Thisis due to the requirement of more oxygen; lethargic muscles, which is fulfilledat the production of lactic acid (as a by product) [2].

The idea of accurately measuring players fatigue levels can revolutionizethe sports industry across the globe. The sports that include more physical ex-ertion and include a team of players of different stamina, can benefit the most.One of the most popular games that fall under this category is soccer. Fansget very upset when their favorite player(s) show bad performance because offatigue.

Typically, in-vivo sensors are used for soccer players that are not onlysmall in size but are also easy to use [3]. Being too small, these sensors havelow battery power and thus their data transmission rates are very limited.Generation and transmission of redundant data is minimized by using differentthreshold levels for sensors. A data packet is only generated by the sensor nodewhen the sensed value is equal to or above the provided threshold level.

As players and soldiers are the common victims of muscular fatigue dueto strenuous exercises and difficult duties. In FAME protocol, we introducea composite parameter for fatigue measurement by setting a threshold levelfor each sensor. Whenever, any sensed data exceeds its threshold level, theplayers or soldiers are declared to be in a state of fatigue. Moreover, we usetwo threshold values for players and three threshold values for soldiers fatiguemeasurement. For players fatigue measurement, we use a threshold for lacticacid level in the blood and a threshold for total distance traveled by the player.Whereas, for soldiers fatigue measurement, in addition to the aforementionedtwo parameters, we also use a temperature sensor. This is because soldiers donot have controlled or constant environmental parameters as players do. A


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Journal of Medical Systems, 2015 3

soldier while exercising, goes up hills, down hills, crosses bridges and barriers,etc. However, a player performs strenuous exercises for a limited period oftime within a controlled environment. Furthermore, we use a vibration pad forthe relaxation of fatigued muscles, and then utilize the vibrational energy by

means of vibration detection circuit to recharge the in-vivo sensors. Motivatedby our preliminary work in this area [4], our contribution in this paper is topropose a fatigue measuring protocol for soldiers and a mathematical modelfor recharging the implanted sensors.

Rest of the paper is organized as follows: section II presents the relatedwork, section III discusses experimental details for players as well as soldiers,section IV explains the basic fatigue measuring parameters and specifies thespeed profile of a soccer player and a soldier, section IV presents the FAMEprotocol for players, section V extends the FAME protocol for use by soldiers,section VI provides a technique to relax fatigued muscles, section VII includesa mathematical model for recharging implanted sensors, simulation results aswell as their discussions are included in section VIII, section IX concludes thepaper.

2 Related Work

Authors in [5] use wearable sensors to monitor humans daily life activities.As different people show different movements during various activities, so, itis challenging to correctly monitor all of their movements and actions. Forthis purpose, authors give a sparse representation theory for humans actionsmonitoring using inertial sensors. They validated the method by carefully rec-ognizing 9 different daily life activities by 14 subjects. Activity recognitiontheory, presented in this article, is almost 96.1% precise.

In [6], Kashif Kifayat et al. present design and implementation of a bodyarea sensor network and a gaming platform to dynamically adapt the physio-therapy treatments. The proposed framework uses three components side byside which includes a Wireless Sensor Network (WSN), a game and a dataacquisition manager. The WSN collects the information from the patients andforwards it to the data acquisition manager which then provides updates tothe game. As time passes by and the patient shows better mobility, the gamereaches its higher levels and becomes difficult. This is done to provide accuratetraining for patients with the passage of time.

Researchers in [7] introduce an Ambient Assisted Tool (AAT) for elderlypeople. This AAT is based on ambient intelligence paradigm. To provide as-sistance to people, one of the major challenges is to correctly recognize theiractivities. Routine activities are observed using different sensors. After thisstep, the used algorithm finds difference in patterns of the recently observeddata from the stored data. Then planning is performed accordingly to eachperson. This tool can be applied for continuous vital sign monitoring, locationdetection and fall detection of humans.


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M-ATTEMPT [8] is an energy efficient routing protocol for heterogeneoussensors attached with the human body for continuous patient monitoring.The sink is placed at the center of the body. M-ATTEMPT uses; single hopcommunication for the delivery of normal data, whereas, multi hop is used for

emergency data. It is a thermal aware protocol which when senses a hot-spoton the route, immediately changes its route by skipping that node until it isback to its normal temperature. This technique protects human tissues frombeing damaged.

Heet al.in [9] introduce a Personal Wireless Hub (PWH) to collect PatientHealth Information (PHI). Information is collected via biomedical sensors. Thesensed data is then routed towards the health care center. Researchers observethat only critical and important data should be routed to the PWH and thisdata must be secured. Authors present an encryption procedure based onpolynomial authentication scheme. The encryption scheme generates a key forevery sensors data and the key is updated on a regular basis. Authors, alsoreport the experimental results of the proposed scheme for sensors that areresource limited. These results show that the protocol produces manageable

overhead for these sensors.Loet al.in [10] present a new approach to determine the location of nodes

on the patients body. This is done by measuring and comparing instantaneousair pressures on each sensor node. Spatial information helps in identifying theexact location of sensors on the body. This technique reliably maps patientslimb positions, in absolute coordinates, longitudinally along the length of pa-tients body. This technique is very helpful for calculating mobility patternsand observing daily life activities of a patient.

Dhamdhere et al. provide a real time team monitoring for a soccer match[11]. They present different challenges for monitoring soccer players. They givetheir own design where they divide team members into different categorieson the basis of position from the sink, and then calculate the delay along

with resource consumption profile of the WBASN for each team member,respectively.

Nardis et al. in [12] use body area networks for soccer players to gener-ate a realistic mobility model. Each player has BASN which provides playersposition information throughout the game. The communication is done usinginter-BAN multi-hopping. They design a new mobility model called DynaMoand compared its delay and throughput with the Reference Point Group Mo-bility model (RPGM).

Researchers in [13] observe the Hospital Information System (HIS) andintroduce an idea to embed different medical devices into the system. Thesedevices are; echographs machines and network digital cameras. Ubiquitousechograph is a common medical imaging device. If echograph is embeddedwith HIS network, then, it is quite easy for doctors to immediately diagnosepatients. This information saves precious time and lives. The embedded net-work digital camera also saves hospitals time and resources by assisting staffin recognizing the patients. The technique is to use a barcode against eachpicture of the patient.


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Authors in [14] present a ZigBee based routing protocol to monitor pa-tients. As, the existing protocols achieve reliable communication by using ei-ther broadcast or multi-cast transmissions, so, the end to end transmissiondelay and network traffic increase accordingly. Keeping this in mind, authors

present, any-cast routing protocol for patients vital signs monitoring. Theprotocol selects any receiver which is near the sensor, for low network latency.This wireless network performs fall detection, indoor positioning and ECGmonitoring for the patients that are using it. Whenever, a fall is detected, thenetwork is able to tell the exact location of the fall to the hospital crew.

Authors in [15] and [16] worked on a MAC layer of the OSI model, andpresent a medium access control protocol and a thorough survey on energyefficient protocols for WBANs, respectively.

Zulkifliet al.implement a heart rate monitoring algorithm for players, per-forming strenuous exercises [17]. Heart Rate Monitors (HRMs) are commonlyused as a training aid in sports. When HRMs are incorporated with WBAN,they can provide continuous and accurate monitoring of players during hardexercises.

Lerer et al. provide a personnel management system for collecting vitalhealth information for ice-hockey players [18]. Ice hockey is very strenuousand difficult game because of its high speed requirements. The paper focuseson using inexpensive sensors for college students playing ice hockey. It uses arespiratory rate monitoring system and audio processing on the collected datato find fatigue in players.

Garciaet al.in [2] propose a new remote monitoring mechanism for soccerplayers, called Wireless Soccer Team Monitoring (WSTM). They use multi-hopping topology for data routing in the network. Two sinks are placed behindthe goals and each player is equipped with a sub-BANs to calculate fatigue.

In [19], an inductive power system is proposed in which the implant receivespower from an external transmitter through an inductive link between anexternal power transmission coil and the implanted receiving coil. The resultsare plotted for the parameters of voltage gain and link efficiency.

In [20], an innovative modeling method for mutual inductance of two mag-netically coupled coils in an inductive link is proposed. The model ensuresefficient energy and data transmission in implanted electronic devices. In [21],an inductive link is designed for medical implants, using coupled coils. Voltagegain and link efficiency are used to verify reliability of the designed link.

Most of the existing literature seems to be focused on one of the specificaspects; increasing network lifetime, inductive link design, parameter moni-toring, treatment methodology, etc. Moreover, these protocols are also limitedto one specific application; players or soldiers or patients monitoring. Our ap-proach considers all of the mentioned parameters along with a diversified rangeof applications, i.e., experiments are conducted on players as well as soldiers.


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Fig. 1: Soccer players speed profile

3 Experimental Details for a Soccer Match and Battle Field

The standard size of a soccer ground vary from 100 yards to 120 yards inlength and 60 yards to 80 yards in width. We choose 106 68 square yardsfor the soccer field, where the total number of players from both teams is 22,

i.e., 11 players per team. Defense and attack are the two commonly used gamestrategies. The first one needs 4 to 5 players in their own half of the field.Whereas, the rest of the players try to be in the opponents half to try to scoreas many goals as possible in order to win the game.

In contrast to a soccer match, neither the boundaries nor the time periodof the war are specific. Strategies of a war are also very diversified. We can notpredict anything about a war before it actually happens. The speed and fatigueanalyses of players and soldiers are discussed in the following two subsections.

3.1 Speed analysis for soccer players

On average, the duration of a soccer match is from 90 to 100 minutes. Duringthis time, the total distance covered by a professional player is slightly morethan 11 km per match (nearly 7 miles). This distance is not only covered byrunning but, some sprinting is also performed [22] as shown in the statisticsprovided in fig. 1. The average running speed of a player can vary from 10.3km/hour. to 12.9 km/hour. Soccer players usually sprint during the possessionof the ball, and can attain 25 km/hour at maximum [23].

A soccer player attempts approximately 100 sprints per match lastingabout 2-5 seconds. On average, for a single match, the minimum work torest ratio for a soccer player is 1:2 [24]. Soccer players speed, direction andball possession can all be compromised if he cannot recover from the fatigue.If a player continues to run instead of taking rest then, he can be seriouslyinjured leading to a possible permanent damage of muscular tissues. So, toavoid this, a threshold for fatigue level must be defined beyond which playersmay face serious health issues.


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Fig. 2: Lactic acid production process in muscles

3.2 Speed analysis for soldiers

As mentioned earlier that the parameters for war, battle or even a small mili-tary mission, are very difficult to predict. In harsh environments and difficultconditions, a soldier mostly walks. Otherwise most of the time, soldiers run.The number and duration of sprints for a soldier are also comparatively higherthan that of a player. Typically, the duties and exercises performed by a soldierare very strenuous.

3.3 Muscular fatigue measurement for players and soldiers

Muscular fatigue is commonly explained as the inability of muscles to gener-

ate any force. When humans perform any vigorous exercise or do sprinting orswimming, they begin to inhale faster to send more oxygen to the workingmuscles. A human body generates most of the energy using aerobic methods.However, under some circumstances, where the required amount of oxygen(O2) is more as compared to the normally needed level, the body uses anaer-obic methods such as glycolysis which uses glucose for energy production. Inglycolysis, glucose is metabolized into pyruvate which is further broken downin the presence of enough O2 to generate energy. However, if the O2 supply isnot enough then the pyruvate changes into lactate which is then transformedinto energy as shown in fig. 2.

If muscular cells do the above mentioned practice for more than 3 minutes,lactic acid will start to accumulate in the muscles. This shows the applica-bility of lactic acid as a good parameter to measure fatigue [25]. The sensorused to measure lactic acid level, is an in-vivo sensor which includes a needle,pricking the muscle to draw blood. Normal results show a value between 4.5to 19.8 mg/dL (0.5-2.2 mmol/L). Where, mg/dL = milligrams per deciliterand mmol/L = millimoles per liter [26].


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Fig. 3: Soccer player

4 The FAME Proposed Technique for Players

In any sport, one of the major concerns is the fitness of a player. Whereas, in

team sports like soccer or hockey, fitness of each player is not only importantbut is also considered a match winning criteria. The problem with these sportsis that the fatigue level of a player continuously changes during a game as itincludes running, sprinting and many other tiring activities. So, there shouldbe a mechanism to constantly monitor each player in the team. Whenever,any critical condition occurs, coaches and health officials immediately takeprecautionary measures to handle the situation which reduces the chancesof any further injury. To cater for the aforementioned issues, we propose atechnique; FAME, for fatigue measurement of soccer players. FAME works onthe network layer of the standard OSI model. For experimentation purpose, weconsider two teams of 11 players each. Each player has an implanted sensor toregularly collect the lactic acid level as shown in fig. 3. In FAME, each players

fatigue information is transmitted, whenever, the maximum threshold level forlactic acid in blood or distance covered during the match, are reached. Thisinformation is routed towards the sink using only direct transmission due toits usefulness in decreasing the propagation delay.

To support direct transmission mechanisms, we use 6 sinks fixed at theboundaries of the ground (refer fig. 4) whose locations are given in table I.Typically, WBAN applications require minimum propagation delay as well asminimum energy consumption. Thereby, we have deployed multiple sinks tominimize the communication distance which leads to minimization of prop-agation delay and minimization of nodes energy consumption (i.e., multiplesinks in the field let the sensors consume less energy, while minimizing thepropagation delay, during the transmission of fatigue information).

As RPGM is one of the commonly used mobility models for soccer players,thereby we adopt this model in FAME. The assumed maximum speed is 25km/hour, whereas, the normal running speed is assumed to be 10-12 km/hour.

In FAME, we use direct transmission methods in the network topologysupported by using multiple sinks at the boarder of the ground. To show a


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Table 1: Location of sinks at the soccer ground

Sink Axis

N umber X (yards) Y (yards)1 0 34

2 17 03 51 04 106 345 17 1066 51 106

Fig. 4: Fatigue measuring process during the game

comparison between energy requirements of direct and multi-hop communica-tion, the equations for both transmission methods are stated as:

EMHtransmit (k, d) =N (Ecircuitry + Eamp) k d2 (1)

EMHreceive(k) = (N 1) (Ecircuitry+ Eamp) k (2)

EMHTotal =EMHtransmit + EMHreceive (3)

EDTtransmit (k, d) = (Ecircuitry+ Eamp)

k

d

2

(4)EDTTotal =EDTtransmit (5)

where, Ecircuitry is the electrical energy consumed by the circuit, k is thepacket size and d is the distance from a sensor node to the sink. N in the


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Fig. 5: Flowchart of FAME for players

first three equations, represents the number of hops needed to reach the sink.Equations (4) and (5) clearly show that the total energy is N times less for adirect transmission scenario as compared to multi-hopping, given in equations(1), (2) and (3). Here, one can argue about the distance factor present in allthe equations, as, these distances are very small for multi-hopping. But thisproblem can be catered by using multiple sinks along the field. Use of multiplesinks decreases the distance from sensor to sink. To lower the overall energyconsumption of the sensor network, multi-hop communications take place withreduced transmit power. However, the drawback of this approach is latency ascompared to direct transmission. So, there exists a tradeoff between latencyand energy efficiency.

The whole process of measuring fatigue during a live soccer match and thedeployment of sinks at the boundary of the ground is depicted in fig. 4. Theflow chart of the proposed scheme is given in fig. 5.


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Table 2: Thresholds for different sensors

Sensor type T hreshold value

Temperature 40 0CLactic acid level in blood 20 mg/dl

Distance covered Vary according to mobility model (km)

5 The FAME for Soldiers: Extending FAME Protocol

The physical condition of a soldier is one of the top priorities for military forces.Fatigue measurement in soldiers is very useful, because it continuously updatesthe military unit about physical states of the soldiers. This will also help sol-diers to instantly make or alter their fighting plans. So, we extend FAMEfor soldiers in three exercising modes; Soldier Walking Model (SWM), SoldierSlow Running Model (SSRM), and Soldier Fast Running Model (SFRM) withthe speeds of 5 km/hour, 15 km/hour, and 24 km/hour, respectively [27]. Thespeed values used in our work are not apriori fixed values. Instead, these are

the upper bounds for the three exercising modes. Subject to each bound, wehave used uniform distribution to generate speed values. Soldiers are also verycommon victims for chronic pains and muscular fatigue. They run, performlong duties and do strenuous exercises on daily basis. For the soldiers fatiguemeasurement, we use a composite parameter consisting of body temperature,lactic acid level in blood and the distance covered by a soldier (walking orrunning in a spell). Fatigue measurement could also be carried with a sin-gle parameter or two parameters, however, for more accurate results we usethree parameters because the chosen scenarios need more accuracy rather thanthe computational overhead. Moreover, as all the three parameters are con-tributing to fatigue in an additive-linear manner thereby we add the threeparameters to calculate the composite fatigue cost as follows.

Fatigue = Body temp. + Lactic acid level+ Dist. covered

For experimentation purposes, we consider a running track for soldiers inside afield of 100m100mas shown in fig. 6. As the soldier moves on the track (mo-tion in the exercising modes), we measure the fatigue. Three sensors, checkingthe fatigue of a soldier, are placed on the abdominal region of the body. Outof the three sensors, the one measuring fatigue is in-vivo whereas the othertwo are on the body. The threshold levels that the sensors are using are listedin table 2.

As mentioned above, fatigue measurements are performed using three mo-bility models; SWM, SSRM and SFRM. When any sensor has a value thatis equal to or above the specified threshold, it sends information to the sinkwhich is a Personal Digital Assistant (PDA; a soldier wears it on his belt).This data is then transmitted to the main office which is monitoring the entireunit. The whole scenario is depicted with the help of flowchart in fig. 7. Thevalues used in the radio model are given in table 3.


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Fig. 6: Fatigue measuring track for soldiers

Table 3: Radio model parameters

P arameter V alue

Transmission energy 16.72 nJReception energy 36.2 nJ

Data aggregation energy 0.3064 nJMulti-path energy 5 nJ

6 Relaxing Muscles using Vibrational Therapy

In this section, we examine our technique for relaxing fatigued muscles, inwhich a player being exhausted during play or a soldier being tired during

exercise, use a vibrational pad, attached on the fatigued muscle of the body.The player/soldier is benefiting from this pad in two ways.

Firstly, the generated vibrations, aid the player/soldier to get the musclesrelaxed by providing a short-term therapy session.


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Fig. 7: Flowcahrt of FAME for soldiers

Secondly, these vibrations are helpful in producing electrical signals, withan aim to re-charge the implanted sensors, measuring the level of fatigue.This is merely done to maximize the battery lifetime of the implantedsensors. Mathematical models and description of the techniques are givenin the following section. Fig. 8 depicts the whole process.

7 Recharging Implanted Sensors of Players/Soldiers with a

Vibration Detection Circuit

Implanted sensors can be recharged using sunlight, infra red light, magneticinduction, etc. We use a method, in which vibration pads are used as musclerelaxants. This vibrational energy is changed into electrical energy using apiezoelectric sensor. First, we define the basic induction circuit.

7.1 Basic induction model

An inductive link consists of two coils, forming a transformer. The primarycircuitry generates flux which induces power in the secondary side (mutualinductance), implanted inside the human body as shown in fig. 9. The skinacts as an interface between the two coils. A parameter known as, couplingco-efficient (k) is the degree of coupling between the two circuits. For WBANs,k should be less than a value of 0.45 for the safety of body tissues. Voltage gainand link efficiency are the two parameters upon which efficiency of inductive


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Fig. 8: Vibration pads for relaxing muscles

Fig. 9: Basic induction model

link is being measured, in this study. The above mentioned parameters arehighly dependent upon the factor, k.

7.2 Vibration detection induction model

We use a vibration detection circuit for our technique. In the circuit, a piezo-electric transducer gives a high DC output impedance. That is why, we model


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Fig. 10: Vibration detection circuit with induction link

it as a voltage source and filter network. The voltage sourceVs, is directly pro-portional to the applied force, pressure, or strain. The output is then related

to the available mechanical force.Fig. 10 shows the circuit diagram of a piezoelectric inductive link, where,inductance Lm is because of the seismic mass and inertia of the sensor. Ce isinversely proportional to the mechanical elasticity of the sensor. Co representsthe static capacitance of the sensor. Ri is the insulation leakage resistance ofthe transducer. The values of these parameters are shown below in table 4.The equations for primary side are as follows:

Table 4: Inductive link parameters

Parameters Values

Operating frequency f=13.56 MHZPrimary coil L1 = 5.48 H

Secondary coil L2 = 1 HParasitic resistance of the transmitter coil RL1 2.12 Parasitic resistance of the receiver coil RL2 1.63 Load resistance Rload = 320

Z1 is the impendence between the primary inductive coil L1and insulationleakage resistance Ri.

Z1 = Ri(jL1+ RL1)

Ri+ jL1+ RL1(6)

Z2 is the parallel impendence between Ri and Co.

Z2=

Z1

jCoZ1+ 1 (7)

The source voltage Vs is given as:

Vs= I1(A) jM I2 (8)


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where,

A = 1jCe

+jLm+ Z2

For the primary side of the circuit, value of current is given as;

I1 =Vs+ jM

VloadZload

A (9)

Now, the equations for the secondary side are as follows:By solving for Zload, which is the impendence between the parallel capacitor

C2p and the resistive load Rload, we get;

Zload = Rload

1 +jC2pRload(10)

By further solving for Zload we get;

Zload = Rload

1 + 2C22pR

2

load

jR

2

loadC2p1 + 2C2

2pR2

load

(11)

As, we know that,

Zload = R2+ jX2

So, from equation (11),

R2= Rload

1 + 2C22pR

2

load

(12)

X2 =

jR2loadC2p

1 + 2C22pR

2

load(13)

The output voltage, Vload is given as:

Vload = jMI1 I2(jL2+ RL2) (14)

Finally, the voltage gain is given as;

Vload

Vs=

jMZload

2M2 + A(Zload+ RL2+ jL2) (15)

Now, for the link efficiency:

= VloadI2

Vs

I1(16)

Using the value of the voltage gain, we finally find :

= [ 2M R2

2M2 + Re[A](R2+ RL2)]2

Re[A]

R2(17)


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1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

2

4

6

8

10

12

14

16

18

20

22

No. of rounds (r)

No.ofdead

nodes

WSTM

The Fame

Fig. 11: Total number of dead nodes in the network

The parameter uses to find fatigue in a players muscles, is the amount oflactic acid accumulation in his/her muscle. WSTM does not mention anyparameter.

In FAME, we use direct transmission with multiple sinks to avoid quickdepletion of energy. Fig. 11 shows that nodes live longer and need not to bechanged frequently. However, in WSTM sensor nodes quickly die as comparedto our proposed protocol. The difference in results is because, WSTM usesmulti-hop scheme to route data towards the sink, whereas, our protocol usesa direct transmission method with multiple sinks in the field.

The stability period is the amount of time that elapses before the firstsensor node in the network dies. FAME shows that the first node dies nearlyat 5100th round, as compared to WSTM protocol in which the first node diesafter 2700 rounds. Fig. 11 clearly reveals that our proposed protocol is 22%more stable than WSTM.

As explained before, sensor nodes in the FAME protocol transmit data onlywhen the threshold level is reached for a particular player. In [2], node directlysends data to the BS if it is at its minimum distance to it, like in the caseof a goal keeper, otherwise it uses other players as relay nodes to route datatowards the BS. On the other hand, the proposed protocol only uses directtransmission method to send data to the BS. Hence, there are more packetsgenerated in WSTM as shown in fig. 12.

In wireless networks, there is always some loss of data because of multi-patheffects in the communication medium like refraction, reflection and absorption,etc. Keeping this in mind, we assume a noisy channel for simulation purposeinstead of an ideal one, having a packet drop probability of approximately30%. Fig. 13 shows the amount of packets dropped in the network before


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0 2000 4000 6000 8000 100000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

4

No. of rounds (r)

No.ofpacketssenttosi

nks

WSTM

The Fame

Fig. 12: Total number of packets sent to sinks

0 2000 4000 6000 8000 100000

2000

4000

6000

8000

10000

12000

No. of rounds (r)

N

o.ofpacketsdropped

WSTM

The Fame

Fig. 13: Total number of dropped packets

reaching any sink. Packets received successfully at the sink are roughly about70%. The amount of packets received at all the sinks without any error isanother important parameter to look for in the network. This parameter showsthe accuracy of the protocol and network topology. This parameter, for bothprotocols, is presented in fig. 14. The lifetime of nodes, shown in fig. 15,depends on their residual energy. In WSTM, as the nodes consume more energyduring the communication process because of multi-hop scheme, so the energy


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0 2000 4000 6000 8000 100000

0.5

1

1.5

2

2.5

3x 10

4

No. of rounds (r)

No.ofpacketssuccessfullyreceived

atsinks

WSTM

The Fame

Fig. 14: Total number of received packets at sinks

0 2000 4000 6000 8000 100000.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

No. of rounds (r)

Resid

ualenergyofthenetwork(J)

WSTM

The Fame

Fig. 15: Residual energy of the network

can deplete very quickly. Whereas, in FAME protocol the nodes live longerdespite of the fact that the nodes have very low initial energy.

Fig. 16 compares the throughput of 2 schemes, FAME and WSTM. The

throughput of a network is given by the following equation:

T hroughput(%) = PacketsReceived

T otalP acketsTransmitted 100 (18)


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WSTM The Fame0

10

20

30

40

50

60

70

80

90

100

Throughput%

WSTM

The Fame

Fig. 16: Throughput

0 2000 4000 6000 8000 100000

0.5

1

1.5

2

2.5

3

3.5x 10

3

No. of rounds (r)

P

ropagationDelay(sec)

WSTM

The Fame

Fig. 17: Propagation delay of data messages

From fig. 16, we are confident to say that the packet delivery ratio is 8 %better for our proposed scheme as compared to WSTM. It is obvious from fig.17 that FAME achieves less propagation delay than WSTM because it usesa direct transmission method to send information to the sink unlike WSTM,which uses multi-hop and generates more delay. The propagation delay is animportant factor to be handled in scenarios where, we want to quickly sendcritical data. Our proposed protocol only transmits data, when a threshold for


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0 0.5 1 1.5 2

x 104

0

1

2

3

No. of rounds (r)

No.ofdead

nodes

SWM

SSRM

SFRM

Fig. 18: Total number of dead nodes in the network

muscle fatigue is reached. In this respect, FAME should be preferred becauseit achieves minimum propagation delay.

8.2 The FAME for soldiers

Simulations are performed to measure the fatigue of a single soldier. Threesensors are used in this technique measuring the temperature, the lactic acidlevel in blood and the distance covered as the soldier moves along the track.Fig. 18 shows the death time for each implanted sensor node incase of a soldier.

Where, we consider only three nodes for fatigue measurement. Fig. 18 showsthat in case of SWM, nodes live longer. This is because the speed of a soldieris very slow and he is less likely to be in a state of fatigue. Whereas, in SSRMand SFRM, there is more chance for a soldier to be fatigued earlier.

From fig. 19, it is clear that in SFRM, soldiers are more fatigued and morepackets are generated by the sensors, sensing the fatigue parameters.

Fig. 20 shows the number of packets dropped in the network for each model.Packet drop probability for these simulations is selected to be 30%. The morethe number of packets generated by the sensors, the more the probability tobe dropped.

Fig. 21 shows that the running models; SSRM and SFRM, generate moredata packets as the soldier is more fatigued in these scenarios. Therefore, theprobability of successful reception of data packets is more for these scenariosas compared to SWM.

From fig. 22, it is clear that the energy consumption for the nodes is lessin case of a walking model. The reason behind this is very obvious, that thesensors have to send less data to the sink because the soldier is less likely to


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0 0.5 1 1.5 2

x 104

0

1

2

3

4

5

6x 10

4

No. of rounds (r)

No.ofpacketssenttos

ink

SWM

SSRM

SFRM

Fig. 19: Total number of packets sent to sink

0 0.5 1 1.5 2

x 104

0

2000

4000

6000

8000

10000

12000

14000

16000

No. of rounds (r)

No.ofpacketsdropped

SWM

SSRM

SFRM

Fig. 20: Total number of dropped packets

be fatigued while walking. For the running models, the energy consumption isrelatively high.

The propagation delay depends on the position of the sensor from the sink.However, more data packets generated by the system shows a higher totalpropagation delay of the network. This is very obvious from fig. 23. For bothrunning models, the propagation delay is high due to more packet generationin the network.


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0 0.5 1 1.5 2

x 104

0

0.5

1

1.5

2

2.5

3

3.5

4x 10

4

No. of rounds (r)

No.ofpacketssuccessfullyrece

ived

atsink

SWM

SSRM

SFRM

Fig. 21: Total number of received packets at sink

0 0.5 1 1.5 2

x 104

0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

No. of rounds (r)

Residualenergyofthenetwork(J)

SWM

SSRM

SFRM

Fig. 22: Residual energy of the network

8.3 Vibration detection circuit

Two parameters namely, voltage gain and link efficiency have been investi-gated. The explanation for the results is given in the following two subsections.


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0 0.5 1 1.5 2

x 104

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1x 10

4

No. of rounds (r)

Propagation

Delay(sec)

SWM

SSRM

SFRM

Fig. 23: Propagation delay of data packets

8.3.1 Voltage gain

As shown in fig. 24, when the value of k increases, the voltage gain also in-creases. FromRload = 100, the graph has almost a linear trend. After whichit increases with a little higher slope. This shows that voltage gain is depen-dent, both on k and Rload. Taking into consideration, the safety parametersfor body tissues, at Rload = 320 and at k = 0.4, the voltage gain is about2.2 i.e., the induced output voltage is increased by a factor of 2 in relative tothe input voltage.

0 50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5

3

3.5

Rload (Ohms)

VoltageGain(Vload/Vs)

k = 0.2

k = 0.4

k = 0.6

k = 0.8

Fig. 24: Voltage gain


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100 150 200 250 300 350 400 450 5000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Rload (Ohms)

LinkEfficiency(P2

/P1)

k = 0.2

k = 0.4

k = 0.6

k = 0.8

k = 1

Fig. 25: Link efficiency (%)

8.3.2 Link efficiency

The graph presented in fig. 25 is for Rload = 100 , as the circuit operatesfor higher resistive loads. From the figure, it can be seen that till Rload =200 , the graph has a constant behavior, i.e., the link efficiency remains thesame. After this value, the efficiency increases and as the value of k increases,there is a steep increase in the efficiency. At Rload = 320 and at k = 0.4,the efficiency is about 68% meaning that more than half of the input poweris efficiently transferred at the secondary side, thereby, helping in driving theimplanted circuit inside the human body to a greater extent.

9 Conclusion

Due to strenuous exercises and tiring/difficult duties, players and soldiers arecommonly the victims of fatigue. Subject to declaration and therapy of fa-tigued muscles, this paper enlists three major contributions; fatigue measure-ment via in-vivo sensors in soccer players and soldiers activities, use of vi-bration therapy for relaxing fatigued muscles, and utilization of the therapyvibrations for recharging the in-vivo sensors. To achieve minimum delay, directtransmissions from sensors to sinks is used. However, the problem with directtransmissions is the high energy consumption of sensor nodes which is solvedat the cost of multiple sinks along the boarders of the playing field. Fatiguemeasurement, in soccer players, is carried out with a composite parameterwhich consists of thresholds for lactic acid level and distance covered, whereasfor soldiers the composite parameter is added with the temperature thresh-old. The use of this composite parameter makes fatigue measurement moreaccurate. Results provide a proof of the better performance of the proposedtechniques in terms of the selected metrics as compared to the existing work.


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10 Acknowledgement

The authors would like to extend their sincere appreciation to the Deanship ofScientific Research at King Saud University for funding this research through

Research Group Project NO.(RG#1435-051).

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