Measurement And Analysis Of Blood Glucose Concentration Biology Essay

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This paper describes the development of a embedded based parallel system to measure glucose concentration from the blood samples. The developed instrument works on the principle of absorbance transmittance photometry using ATmega32 microcontrollers. In order to handle more blood samples and reduce the response time of glucose analyzing process in large number of blood samples , the embarrassing parallel measurement operation is implemented.. The proposed system architecture and the co-design of hardware and software are discussed in detail. The system is evaluated using the parameters of Speedup Factor, Efficiency, Scalability and Throughput are studied. The result shows that system attained the Linear speedup in measurement of blood samples.

Keywords: Parallel Process, Embedded System, Glucose Concentration, microcontroller, Gang Scheduling, Clinical Blood Analyzer.


Diabetes has become a development issue and it threatens the health and economic prosperity of people in low and middle-income countries, the International Diabetic Federation (IDF) report said. It also predicted that diabetes would cost the world economy at least $376 billion in 2010. India leads the world in the number of people suffering from diabetes and by 2030, nearly 9 per cent of the country's population is likely to be affected from the disease. Unless serious action will be taken the epidemic of diabetes would increase from 7 million new cases a year in 2007 to 10 million new cases this year.

Diabetes is a common disease related to endocrine metabolism. At present there is no method which can cure diabetes totally. The main therapy is to prevent or alleviate the occurrence of complications through frequent monitoring and adjustment of glucose level. Physicians suggest that the glucose level should be tested at least four times per day. Nowadays the diabetes patients are taking treatment at a specialized centre. In which the number blood samples should be analyzed in effectively with stipulated period for the concerned doctor get the patients' blood analyzing report .

The methods of analyzing can be classified as Direct method and Indirect Method. For the analyzing of blood contents mostly indirect methods are followed. In indirect methods the techniques of spectro photometric, polarometric, amperometric, electrochemical, coulometric, polarography, radiochemical and cluorescence are available. The tool proposed in this paper is designed using the principle of absorbance transmittance photometry. It is a high-performance, multi-microcontroller-based, photometric biochemical analyzer to measure the glucose concentration. It also modifiable to measure various biochemical parameters such as blood sodium (Na), potassium , Urea and bilirubin.

In recent years, automation in clinical chemistry has progressed with a change from rigid to very flexible instruments. Automation of clinical instruments has brought about a revolution in the field of medical instrumentation. It has reduced the load on clinical laboratories largely by reducing the time taken in the test and minimizing the involvement of laboratory staff. The functioning instrument in clinical process can distinguished as serial and parallel system. By using serial system, at a time it can conduct only one test, but the parallel system with advantages of high throughput, minimum response time, precision and accuracy.

It is necessary , screening the people for their blood glucose level monitoring should be intensified and provide necessary precaution steps. To do the glucose monitoring in mass level of blood samples, the performance of serial clinical blood analyzer may not be enough and require an alternate. In order to meet the above requirement, the Parallel Clinical blood analyzer could be a solution. The potential users of parallel blood analyzing system are Specialized Diabetic Centers, Primary Health Centers, Community Health Centers and District Hospitals. This work focused about an alternate system in parallel environment to analyze the blood samples for glucose and other parameters. The proposed parallel measurement is designed using off-the-self microcontrollers The performance of the system is analyzed using more number of blood samples as workloads.


The instrument is designed using the principle of absorbance transmittance photometry. According to Lambert and Beer's law ,when monochromatic light is passed through colored solution, the intensity of the transmitted light de- creases exponentially with the increase in concentration of the absorbing substance. The value of absorption of light energy is dependent on the number of molecules present in absorbing material and the thickness of the medium. Thus, intensity of light energy leaving the absorbing substance is used as an indication of concentration of that particular sub- stance.


Figure 1: Schematic of Lambert and Beer's law.


Transmission %80






Figure 2: Relation between percent transmission and concentra- tion.

As shown in Figures 1 and 2, if I0 is the intensity of incident light in coloured solution and It is the transmitted light, then according to this law

It = I0 e−kct , (1)

and transmission

T= It/Io= e-Kct (2)


logeT= -kct (3)


Loge (1/T)=kct (4)

Where C is the concentration of absorbing sample, t thickness of the light path, and K absorption constant.

The quantity (-log T) or log(1/T) is termed as extinction E/O or the absorbance.

A= log (1/t)= log 1000/(%transmission),

A=2-log (% transmission).

Therefore A=Kct.

If t is constant, that A x c.

In this system, the basis requirement is to measure optical density/absorbance and then concentration of the test parameter under run accurately.


The parallel based glucose analyzing system is necessary when the large number of blood samples to be analyzed in a particular time span. Normally the blood glucose level of diabetic patients are measured before and after the breakfast. Therefore, the Laboratory of specialized hospital, which is exclusive for diabetic patients get hundreds of blood samples for analyzing glucose level during morning session. By the way, of introducing parallel measurement system, the turn around time of a blood sample will be minimized.

Parallel processing involves dividing a problem into parts in which separate processors of processes perform the computation of the parts. An ideal parallel computation is one that can be immediately divided into completely independent parts that can be executed simultaneously. This is picturesquely called embarrassingly parallel or perhaps more aptly called naturally parallel. Parallelizing these problems should be obvious and requires no special techniques or algorithm to obtain a working solution. Ideally, there would be no communication between the separate processes; that is, a completely disconnected computational graph, as shown in Figure-3. Each process requires different or the same type of job and produces results from its input data without any need for results from other processes. This situation will give the maximum possible speedup if all the available processors can be assigned process for the total duration of the computation. The only constructs required here are simply to distribute the task and to start the processes. Interesting, there are may significant real applications that are embarrassingly parallel, or at least nearly so.

In a practical embarrassingly parallel computation, tasks are distributed to the different processors and results collected and combined in some way. This suggests that initially, and finally a single processor must be operating alone. A common approach is the master-slave organization. The master processor is responsible to start and send initial data to all slave processors as well as collects the result from the slaves. The resulting structure is shown in figure-4. In this work, the task of the measurement of glucose for the blood samples using absorbance method is implemented as embarrassingly parallel environment.


The parallel glucose analyzer is a complete system that consist of one master node and three sensor nodes. The master node acts as co-ordinate node, which is able to send commands to sensor nodes and receive the data from the same. The sensor nodes are connected with master node using I2C bus and function as slave nodes. The proposed system is able to handle four blood samples concurrently instead of one sample. The embarrassingly parallelism scheme, as depicted in Figure-4 is implemented using Atmega32 microcontrollers, which is shown in Figure-5.

The Atmega32 has been found appropriate for this parallel blood measurement system. It is an 8-bit, CMOS, low power device composed of standard on-chip peripherals, The AVR core combines a rich powerful instruction set (131 instructions) with 32 general purpose working registers along with 8 bit CPU. This chip has 32k byes of in-system programmable flash memory, 1024 byte EEPROM, 2k byte SDRAM, Master/Slave SPI serial interface, 32 general purpose I/O lines, flexible timer and counter with compare modes, internal and external interrupts and a programmable watch dog timer with power saving mode.

In the Figure-5, the master node is interfaced with 30 characters x 8 lines alphanumeric LCD display through Port-B. The Port-C is assigned to connect a key board, from which the user can activate the analyzer either in Mode-1 or Mode-2 operation. In Mode-1 all sensor nodes are assigned to measure the glucose level in blood sample by issuing command from Master node. In Mode-2 each sensor node can be deployed by Master node, exclusively to measure either glucose or sodium or potassium or urea contents in blood sample. Therefore, mode-1 is used to measure the glucose in all blood samples, the mode-2 is preferred when the blood samples need to complete analysis of three more parameters. The LCD panel is used to display the interactive menu to choose the option, date and time from the RT-clock, Status of sensor nodes and its result. A 40 column thermal mini printer is interfaced through port-D, which is used for hard copy of the results, which are analyzed and sent by the sensor nodes.

The sensor node arrangement is shown in Figure-6 as block diagram. Block-A is referred as Light source, in which LED is used as source of light. In order to get required wave length of light to be passed , four different colors of LEDs have been mounted on a rotational disk. By choosing appropriate LED, the sensor node can be used to measure any one of the parameters along with suitable reagents. The rotation of disk is controlled by the stepper motor, which is connected through the port-B of micro controller. Pulses are generated according to required sequence to rotate the motor at required angle, which brings the selected LED in front of light-path and activate that.

Block-B contains, sample holder, flow cell, peristaltic pump. The flow cell is used to mix the blood sample and reagents. The light beam of particular wave length is penetrated through the flow cell and come out from a narrow hole of opposite side. The port-B ( ) of sensor node is interfaced with a stepper motor, that drives the roller type peristaltic pump. This pump is used in the system is aspirating the required volume of sample and reagents, washing the flow cell. While conducing the test, the contents of flow cell must be kept at required temperature. The Temperature Sensor LM335 is used to measure the inside temperature of Flow cell , which is connected through Port-B ( ) of sensor node. The peltier device is used to maintain the temperature of sample in required level, it works in both directions for cooling and heating. The Port-B ( ) has been assigned for the device.

Block-C contains photodiode sensor, which observes the light from the flow cell as input and produces the current with proportional to the light intensity. The output of the photodiode is given to one of the inputs of 4051 multiplexer, which is in Block-D. The one more input of multiplexer is temperature sensor's output. The output of multiplexer is connected to the built in 10-bit ADC through port-A of Atmega32. The Block-E has a four row 16 characters alphanumeric display, which is used to display the mode of operation, measuring parameters and its value, report of result sent to master node. The communication interface unit is in Block-F, the port-D (PD0 and PD1) is used to communicate with master node. Atmega32 SPI feature is used to configure the microcontrollers in maser/slave mode.


The proposed parallel measurement is implemented as open loop system to measure the glucose concentration in blood samples. The software required for this system has been developed using ' C ' cross compiler for Atmega32 in modular form.. The software can be focused around two sides, i.e. one is master side and other one is slave sides. After the development of software, the program is burned into the micro controllers EEPROM. The layout of the steps followed in the development has been provided in the flow chart shown in Figures 8 and 9, as roll of the master node and sensor nodes respectively. The Talker-Listener principle is followed mutually while exchanging data among the microcontrollers. When the sensor nodes are communicate with master without collision by using 'Newhall-type loop' based method is adopted in coding level.


On a single processor, the scheduling of blood samples for measurement is one-dimensional. On a parallel system, the scheduling is two-dimensional. The scheduling method has to decide from the group of blood samples, which sample to analyze and which sensor node to analyze it on.

The proposed parallel system has been designed to function in two Modes (1 & 2) in order to get the increased throughput and reduced response time respectively. The method of sample handling is differed in each mode. In Mode-1 'Gang' scheduling method is followed and in Mode-2 'Distributed Gang' scheduling is adopted.

In Mode-1, the function of all sensor nodes is set as to measure the glucose level of blood samples, which are loaded in the system. Here, all nodes do the identical process of glucose measurement, so the Gang scheduling is followed to bring out better performance, the Gang scheduling method has 3 parts.

Groups of sample formed as unit or a gang.

All members of gang measured simultaneously on different sensor nodes.

All gang members start and end their time slices together.

The Figure-9 shows the method of Gang scheduling applied in blood samples. Considered that the parallel blood analyzer with 4 sensor node will get number of blood samples to analyze is 16, marked as A0,A1,A2……….A15. During time slot 0, samples A0,A1,A2 and A3 are scheduled to admit in to the system for measurement of Glucose. During time slot-1 samples A4 through A7 are scheduled to measure. Then the cycle repeats till the sample group A12 to A15 is measured.

The Mode-2 is implemented, when a group of blood samples need to complete analysis. Upon this mode, Sensor Node-1 is assigned to measure parameter of Glucose, Sensor Node-2,3 and 4 is allotted to measure Sodium, Potassium and Urea respectively. The single sample volume is distributed among the four sensor nodes and measure the corresponding parameters. The Distributed Gang Scheduling is mapped in this mode of operation, which is shown in the Figure-10. The gang is formed by grouping a blood sample as its sub groups. Blood Sample A0 has sub-grouped as like A01, A02, A03,A04. The sample group A01 is used for glucose, A02 for sodium, A03 for potassium and A04 for Urea.


Generally the roll of parallel system in measurement process is either increased the throughput or reduce the response time . In this work of parallel processing in blood analyzer is supports both of the throughput as well as earlier response time, by way of implementing two modes of operation. The Mode-1 is increased the throughput of the system and the Mode-2 is giving speedup of the complete analysis process and submits the earlier response of results.

Evaluating the performance of a system is meaningful only in the context of a workload, that is, what the system is being asked to do. The parallel system is evaluated by using a group blood samples both in mode-1 and mode-2. The performance of parallel system is compared with against the single processor system with same group of blood samples.

Mode-1 Performance:

The performance of the microcontroller based parallel system is evaluated using the time required to find the glucose level from the workload of 16 blood samples. The Table-1 described the execution time required to measure the glucose level in the form of module names, which is executed on the single processor system. The measurement time of blood samples in a sensor node is sum of (a+b+c) calculated as ------------------ microseconds.

The measurement time of 16 samples using single node with against the four parallel sensor nodes is shown in the Figure-3 as time space diagram. These two implementations must be comparable so that fair conclusion can be drawn from the measurement results. From the Table-2, the time consumed to predict the glucose concentration for 16 blood samples using single sensing node (Ts) is ------------------ micro seconds, the execution time using 4 sensor nodes (T/P) is --------- micro seconds. The communication between Master and and Slave nodes establish in two incidents, first phase is distribution of absorbance value of blank and standard from the master to all the slave processors and the second phase is measured glucose level by slaves nodes to the master nodes. Let the time required for the first phase is ……… and the time consumed in the second phase is calculated as ---------micro seconds, which is maximum time required to send results by four sensor nodes. The speedup factor (TS/(Tp+TC)) of distributed system is ………. And the Efficiency is Ts/Tp*4)*100 is ………%. The computation and communication time are calculated using the pin-pong method.


The embarrassingly parallel process based blood analyzer is designed using ATmega32 microcontrollers as a loosely coupled multiprocessor system, in order to reduce the blood analyzing time in group of blood samples. The performance of the system is studied and obtained the speedup, efficiency and throughput values. The performance achieved by this multiprocessor system can be replaced by a single faster processor, when the faster runs, the more heat it generates and it is to get rid of this heat. But, the proposed parallel system is constructed by using multiple off-the-self components of microcontrollers, which are runs at normal speed and produce minimum heat, but which collectively have far more processing power that a single faster processor. In this way, this parallel blood analyzer can be viewed as an eco-friendly system.

ware, the communication topology and the structure of nodes. Denham (1977, [3]) discussed three methods of The concept of distributed process has been proved an effective way of improving a system operation, which can be extended to processor that are some what more complex. The embedded based distributed process is a viable alternative for many mechanical applications including automobiles, industrial robots, production machines, rock drilling machines, safety related tasks, such as vehicle dynamics control and engine control. With a distributed process approach, hardware and different level of process are spatially distributed to actuators and sensors in the mechanical system. Benefits by a distributed approach include modularity, improved functionality and performance [1,2]. For distributed applications different hardware structures can be identified with respect to the spatial distribution of the harddecomposition on the basis of spatial, organizational and dynamic characteristics of the application. The first is a clear relation to I/O bounds, as followed in this work. The second and third methods are relation to hierarchical arrangements and dynamic multivariable systems respectively.

This paper explains a distributed measurement process, which is implemented on microcontroller based clinical blood analyzer. This work can be viewed as the decomposition of centralized blood analysis system into decentralized ones. The Locality-based Distribution (LOC-D) [4] model is formed using four nodes of microcontrollers by applying the decentralization methodology on the integrated blood analyzing unit. The distributed analysis of three parameters on a blood sample made this system as a level three in degree of freedom (DOF) . The nodes are functioning with a local clock, process of each node statically allocated, nodes are connected by a serial bus with communication scheduling policy with upper bounded access delay. This system is formed as a test bed for distributed embedded system for studying the properties of distributed process.

In this analysis machine the concurrent and overlapped measurement pattern is followed in order to handle more than one blood samples instead of single sample, by which analyzing time is minimized. The loading of blood samples to detect Sodium, Potassium and Calcium ions are followed by the linear pipeline fashion. The arrangement of mechanical movement of blood sample shelf is outside the scope of this paper. This Blood Analyzer is more ultimate for the multi specialty hospital's laboratory, which can produce the blood analysis result with minimum delay for a number of patients.

The distributed blood analyzer is a complete system that consists of three sensor nodes and one coordinate node. The first sensor node is used for measurement of Na, second and third sensor nodes are used for measurements of K and Cl ions respectively. The sensor node consists of a ISE sensor, instrumentation amplifier, analog to digital controller, microcontroller, stepper motor, pump motor, and LCD display panel. The coordinate node is used to activate the sensor nodes, to collect the measured values from sensor nodes, to store in EPROM based database, to display the measured values in global LCD panel, to generate blood analyzed report on printer, and to send the result to a remote PC for further reference. The Nodes are communicated using RS-232 interface.

The block diagram of distributed blood analyzer is shown in Figure-1. The blocks A, B and C indicate the sensor nodes. Each sensor node consists of five sub-blocks. The Coordinate node is marked as block D. The sub-blocks A1, B1, and C1 contains Na, K and Cl ISE sensors, which will output the voltage corresponding to the concentration of Na, K and Cl ions in Blood sample. Since the output of sensors is low, it is amplified by an operational amplifier, which is kept in the sub-blocks of A2, B2, and C2. The sub-blocks A3, B3 and C3 indicate the A/D converters. The Output of an operational amplifier is given to input of an A/D converter, which will convert the analog voltage of concentration value into its corresponding digital voltage. The microcontrollers are kept in the sub-blocks of A4, B4 and C4, which are used to process the signals from A/D converters. The microcontrollers also will carry the work of sensor activation, conditioning, calibration feeding, measurement calculation, display the result value and send to the coordinate node. The pump motors, stepper motors and LCD panels are interfaced with microcontrollers, which are shown as A5, B5 and C5. The pump motor is used to circulate the calibration-1 solution in ISE sensor. The stepper motor is used to dip the ISE sensor for measurement into the standard calibration-1 solution, calibration-2 solution and blood sample.

The proposed apparatus is able to handle 3 samples simultaneously. The blood samples are kept in a linear shelf and motor arrangement will move the sample from one sensor node to another which is controlled by Coordinate node. As per Loc-D model all nodes work independently without central control, the analyzed outputs only send to the coordinate controller, so that the behavior of apparatus is referred as distributed process.


The design of clinical pipe line based distributed blood analyzer is mapped as circuit diagram, which is shown in Figure-2. The Circuit design consists of four stages, which are corresponding to four blocks (A, B, C and D) of Figure-1. The Stage-I is the Na sensor node, Stage-II and Stage-III are K and Cl sensor nodes respectively. The stage-IV is the coordinate controller. The design of stages II and III are replica of stage-I except the corresponding ISE sensor. The description of circuit design of Stage-I is as follows. The place of an ISE sensor of Sodium is indicated by number 1. The amplification is carried out by the 714 based operational amplifier, which is denoted by no.2. The amplified signal is connected to the pin number 2 of the MCP3201 based 12 bit A/D converter, which is marked as no.3. The serial output and clock of A/D is connected to port 1.1 and 1.0 of microcontroller. The Microcontroller P89C668 has been found appropriate for the control unit on the basics of advantageous features. The position of microcontroller is designated as number 4. The pump motor is interfaced to microcontroller in port 1.7 through the buffer 74S07 and BDX33 transistor, which is indicated by No.5. The L293 motor controller chip is used to control the stepper motor, which is connected to microcontroller using port 1.4 to 1.6 and marked by No.6. A 12 MHz crystal is used by connecting pin no.14 and 15 of microcontroller. The reset circuit is provided at pin no. 4. The Port 2.2 to port 2.7 is reserved to connect the local LCD display to know the measured values in each sensor node. The coordinate microcontroller and sensor node microcontrollers are interconnected using Tx and Rx lines. The Port1 and Port 2 of coordinate microcontroller in stage-IV are interfaced with Keyboard and LCD display interface, the port 0 is used to connect the printer.


In order to increase the throughput of blood analyzer and decrease the analyzing time of more samples, the decentralization is adopted in this tool, which can be realized by applying different methods of input in feeding of blood samples to sensor nodes. The pipeline based sample feeding method is implemented which is shown in the algorithm of Figure-3, in which the sensor nodes and its sample holding are indicated. The index value 'I' is updated on the basis of timing. According to the pipeline processing, the sensor nodes are treated as pipeline stages, in which there is no common clock to synchronized the measurement process in all sensor nodes. The sensor nodes carry its corresponding work in asynchronous pattern because of time varying measurements. To ensure the smooth flow of blood samples without bottle neck problem in the nodes, the self synchronization is adopted in the sensor nodes. Each node is assumed to have been given the same time to complete the measurement task. The uniform time period is referred as BTU (Basic Time Unit), this can be treated as value of pipeline cycle.

The measured values from the sensor nodes are collected by coordinate controller using round robin based polling technique. The polling process starts from sensor node 1 and proceeds [8]. The collection of result from the three nodes will be completed with in one BTU time unit. The communication pattern between coordinate node and sensor node is shown in the algorithm Figure 4. The barrier variable is used to collect and ensure the arrival of data from all sensor nodes for each blood sample.

In this work the empirical value of BTU is found according to the execution time required for measurement process and the time of sample movements between sensor nodes. To maintain the error free BTU the homogeneous based nodes are constructed. With reference to table-1, the time taken to analyze the blood contents in each stage is denoted by 'ti'. Let 'tl' be the time delay for the sample movement from one stage to other. From the table the maximum value of 'ti' is added with 'tl' [max (ti) + tl], which provided 50 seconds as value of BTU. The snapshot of blood analyzing is illustrated as space-time diagram in Figure-4. From the diagram it is observed that the staircase effect at the beginning. After the staircase effect, one blood sample is completely analyzed in each BTU.


The pipeline based distributed blood analyzing system is constructed to measure the Na, K and Cl ions and evaluated using the parameters of speedup, efficiency and throughput [9,10]. This maximum accommodation of 12 blood sample is treated as highest load value of the system. The Table-2 provides the attained parameter values and the charts are given, to compare the result values with other standard values.


The speedup can be defined as, a pipeline with k stages can process n tasks in Tk= k + (n-1) clock periods. K cycles are used to fill up the pipeline or to complete the execution of first task and n-1 cycles are needed to complete the remaining n-1 tasks. From the snap shot of Figure-5, the defined speed up is found as follows. The blood analyzer has 3 stages of sensor nodes and it could be loaded by 12 samples, the time taken to fill up the pipeline or time taken to complete the analysis of first sample is 150 seconds. The remaining 11 samples are analyzed in the delay of 50 seconds per sample is 550 seconds. The total time consumed by 12 samples in pipeline fashion Tk is 700 seconds.

The same number of samples is analyzed in a centralized processor Ts is consumed n.k time delay. The time required to examine a blood sample completely is calculated using ping-pong method is 140 seconds. The time required to measure 12 samples is calculated as 1680 seconds.

The speedup of a k-stage distributed processor over a centralized processor as Sk = Ts/Tk. The speedup value 2.1 is obtained as against the maximum speedup value of 3 when Sk->k. In Figure 5, the actual speed up is compared with ideal value, lower-bound (log2 n) and upper-bound (n/ln n) values.


The efficiency of the pipeline is measured by the percentage of busy time-space spans over the total time-space span, which equals the sum of all busy and idle time-space spans. Let n, k, and t be the number of tasks, the number of pipeline stages, and clock period of a linear pipeline respectively. The efficiency is formulated as n/(k+(n-1), which provided 0.857. Another view of efficiency measurement based on Minsk's conjecture is the ratio between the actual speedup and the ideal speedup, according to this view the value 0.7 is obtained. The Figure-6 shows the experimental efficiency and compared with the ideal efficiency of 1 and Minsky value.


This rate reflects the measurement power of blood analyzer, which can be defined as the number of results that can be completed by a machine per unit time. In terms of efficiency and clock period, the formula obtained to define the throughput is (n/k t + (n-1) t). Where n equals the total number of tasks being processed during an observation period of k t + (n-1)t. The value of observation period is (3x50 + 11x 50) 700 seconds. The obtained throughput value is 0.017 as against the ideal value [1/t] of 0.02, which is shown in Figure-8.


The ATmegar32 microcontroller based distributed blood analyzer is constructed, in order to reduce the delay of blood analyzing time. This tool is ideal for the multi specialty hospital with higher floating of patients and more number of operation theaters to conduct concurrent surgeries. In which the requirement of multiple blood samples should be analyzed simultaneously. The mapping of centralized measurement process of I1/O3 is decentralized into three I1/O1 processes and reached the 4 degree of freedom. The design of this system is followed the real-time cooperation constraints (RTCC) of synchronous sampling time, bounded jitter and constant measurement delays.. This tool can be extended by adding additional sensor nodes to measure for other bio-chemical parameters.