Wireless Portable

Emergency Detection Device

Param Vora

Steven Garrett

Collin Liu

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As hikers, bikers, climbers and other outdoor enthusiasts push the limits of their bodies in remote locations, they need reassurance that they will be found in the event of an emergency. Current devices manufactured for these purposes require manual activation. Our device will activate automatically. This is the motivation for our design.

1. Introduction

1.1 Objectives

We propose to build a device that will sense a potential emergency involving a subject either becoming unconscious or entering a state where he/she is unable to take action for rescue. Our device will:

Monitor Blood Oxygen Saturation Monitor Heart Rate Detect falls and non-movement Make an emergency call when needed Send GPS coordinates to facilitate rescue

2. Design

2.1 Block Diagram

Figure 1 High Level Device Block Diagram

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2.2 Block Descriptions

Android® Mobile Phone

An application will be created utilizing the Google® open source development kit along with the Nexus One mobile phone. This application will accept the data in serial format from the design integrated Bluetooth module using the Bluetooth Serial Port Profile to display heart rate, oxygen saturation, and motion information for the athlete wishing to utilize the data. The phone will also take care of the necessary communication link in the event of an emergency, along with providing authorities with exact location coordinates from the integrated GPS.

Bluetooth Module

The Roving Networks RN-41 class I Bluetooth module will provide a serial link between the MSP430 and the Nexus One using the integrated Bluetooth serial port profile (SPP). As a class I module, the range of the device can be as far as 10m away. This way the user may comfortably distance themselves from their cell phone without worry of losing a connection to safety.

MSP430FG439 Microcontroller

As a microcontroller designed specifically for medical devices, this IC will provide the platform for our algorithm which will monitor blood oxygen saturation, movement and temperature. The IC is responsible for powering the LEDs in the SpO2 sensor, along with reading the output signal from the sensor photodiode.

As a processing unit, the IC will digitally filter the signal from both the accelerometer and the SpO2 sensor, calculate the values needed, determine a potential emergency, and pass any necessary data to the RN-41 for transmission to the Nexus One.

Pulse Oximeter (SpO2 Sensor)

The Nonin® 8000R reflective forehead sensor will house the LEDs and provide the signal to be analyzed by the microcontroller. The integrated LED and photodiode components are well tested by Nonin® and should provide a reliable signal for accurate measurement. The forehead also provides the most reliable place to measure oxygen saturation on an individual in motion [16].

LED Drive

The LED driver system is responsible for providing a constant current to the LEDs in the SpO2 sensor. This system consists of four transistors wired in an H-Bridge configuration. In this configuration, current can be controlled to flow through either the red LED or the infrared LED, but never both. This provides a

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hardware override which ensures that the signal from the SpO2 sensor never consists of components from both the IR and Red LEDs.

Utilizing NPN transistors with relatively lower base current gains (hfe), the current through the branches of the H-Bridge may easily be controlled by a voltage from the MSP430 DAC via the signal labeled “RED_DRIVE” or “IR_DRIVE” in figure () below. Figure 1.1 is a simulated graph of the current through the IR LED vs. IR_Drive Voltage (given that the PNP transistor for the appropriate current path is on). The trend demonstrates that the current through the LED may be linearly adjusted through recommended current values [1] using the entire range of the DAC (roughly from 1 to 3 volts).

Figure 2 Simulation Results of a DC Sweep on the IR_Drive Signal Using Multisim®

Analog Filter and Amplifier

Using the MSP430 built in operational amplifiers, the signal can be properly conditioned before being sampled by the internal ADC. In accordance with the method used in [2] the signal is first amplified by a transimpedance amplifier circuit. The output of this amplifier is sampled and filtered to obtain an offset voltage. This offset voltage is used in the second stage amplifier to remove the DC component in the signal. By amplifying only the AC signal in the second stage, the full range of the ADC can more adequately be used. This should ensure more accurate results following calculations of SpO2.

The stages of the conditioning circuit inside the MSP430 are outlined in more detail below.

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Transimpedance Amplifier (1st Stage Amplifier)

Figure 3 Transimpedance Amplifier [3]

(1)

(2)

(3)

The transimpedance amplifier in this configuration is designed so that it acts as a current to voltage converter along with a low pass filter. Signal picked up by the photodiode provides a small current that is then converted to voltage waveform by the transimpedance amplifier. Equation (1) shows that the frequency response of the amplifier is due to the feedback network of the amplifier. The DC gain is set solely by the feedback resistor RF [1].

The design of this stage of the filter was determined as follows

Rf was chosen by experiment based on the gain needed to bring the current signal into the range of the ADC.

Diode capacitance was estimated based on typical photodiode capacitance values to be about 50pF.

Op-amp capacitance and GBW product is pre-determined by the manufacturer and in this case is about 50pF and 2.2 MHz respectively.

Based on the values above, Cf was calculated using equations (2) and (3) to be approximately 1pF. Note that equation (2) was described in [1] and was determined to minimize noise created by the op-amp.

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The values found using this process correspond to the values C1 and R2 in figure 6. Also, the oscilloscope waveform in figure 4 below demonstrates a preliminary extraction of the signal from the first stage amplifier. Each peak represents a pulse.

Figure 4 First Stage Amplifier Output of the SpO2 Signal

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Differential Amplifier (2nd Stage Amplifier)

Figure 5 Defense AmplifierThe main purpose of the second stage amplifier is to remove the DC offset signal picked up by the sensor due to the normal flow of blood and other background noises. The gain for this amplifier is entirely determined by resistance values internal to the MSP430 located on the resistor ladder shown in Figure 6. The control signal “OAFBRx” is used to determine how much resistance becomes R1 and how much becomes R2. As an example, the voltage Vout is determined below using the case where OAFBRx = 111.(4)

(5)

(6)

(7)

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Figure 6 MSP-430 Internal Op-Amp Circuit [4]

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Accelerometer

The MMA7260Q Accelerometer from Freescale Semiconductor will provide motion sensing data used to determine a fall. This integrated circuit module will detect movement and tilt and output a voltage proportional to the magnitude of acceleration. By processing the signal (one for each axis) with the MSP430, the motion of the user will be monitored for potential falls or impacts.

Each output signal from the accelerometer is individually filtered with a low pass filter to ensure that there is no aliasing of high frequency components after being sampled by the MSP430. The filter cutoff frequency is found below using the equation [5]

(8)

Finally, using the accelerometer control signals GS1 and GS2, the sensitivity of the device can be altered based on the magnitude of movements detected.

Digital Temperature Sensor

The MSP430 integrated temperature sensor will provide temperature data for calibration of the SpO2 data to varying ambient and bodily temperatures. The temperature information could also prove vital for paramedics in the event of an emergency.

Buzzer/ Button

This combination of components will be vital to minimize the event of a false alarm. Upon detection of an emergency situation, the buzzer, powered by a PWM signal from the MSP430, will emit a loud alarm. If the victim is conscious and alert, he/she will press the button to disarm the alarm and avert transmission of an emergency call. Otherwise, if there is no response from the user via an external button within a 45 second period, an emergency call will be made.

Power Circuit

The power for the device is supplied using a Texas Instruments® TPS61010 boost converter. This >95 efficient converter will increase the voltage of a standard AAA battery to 3.3 volts, thus providing the necessary voltage for normal operation. Using the built in low power threshold capability, the device will detect a drop in battery voltage and send a signal to the MSP430 indicating a low battery. With the device integrated buzzer, the user will be notified of a low battery long before the device fails.

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2.3_Schematics

Figure 7 MSP430FG439 and Accelerometer

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Figure 7 LED Drive Circuit

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Figure 8 Power Circuit and Bluetooth Module

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2.4 Software Implementation

2.4.1 Software Flow

The logic will have a basic flow that starts by monitoring movement using the accelerometer. We will be implementing a fall detection algorithm to identify when the user has fallen and is in need of assistance. If the fall is followed by sixty seconds of no movement, this will trigger a signal to turn on the pulse oximeter and monitor heart rate and blood oxygen percentage. If the heart rate is rapidly decreasing or is already below a threshold, and/or the blood oxygen level is under a specified threshold, the unit will sound a local alarm. If this alarm is not shut off within 60 seconds, an emergency call will be made.

Figure 9 Software Flow Diagram

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The fall detection algorithm works by calculating the energy expenditure for every 800ms [6] using the equation (9) below.

(9)

This value will be compared to a specific threshold obtained by experimentation. Once the fall is detected, it will monitor the accelerometer with a lower sensitivity. If no motion is detected to indicate that the user is back up and running, it will turn on the pulse oximeter to monitor the heart rate and blood oxygen levels. If the heart rate or blood oxygen level is falling to indicate that the user might be going to a state of unconsciousness, it will automatically ring a local alarm. If this local alarm is not shut off by the user to signal a false alarm, an emergency call will be made. Simultaneously, if the blood oxygen level is decreasing or at a relatively low level, it will further confirm an emergency and will sound the local alarm if it is below a certain threshold. The Android platform will provide the functionality to create an application that will display heart rate, Sp02 percentage, an emergency indicator, and a Reset for the user to turn off emergencies.

Figure 10 Android Nexus One

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Heart Rate

Sp02 Percentage

Reset

2.4.2 LED Drive Algorithm

The Objective:

Ensure only one LED is on at once Each LED is ON only for sufficient time to collect the necessary signal.

Otherwise power is wasted Utilize a period where both LEDs are in an OFF state, so that ambient

noise can be subtracted from the signal. Compute SpO2

Timing of the LEDs [2]:The LEDs will have 6 modes that they will cycle through each period. The period of the following sequence will be roughly 1ms.IR LEDInitialize Mode- Turn IR LED on and allow enough time for the analog signal to become

stable before sampling- Read the RED LED information stored in ADC register- The RED LED conversion data is also processed to get only the AC

component of the signal, track the DC component associated with the signal and update the DAC register to determine voltage offset, and finally ensure the LED intensity is within the correct range for the next sequence.

Clear DAC Mode- Turn off IR LED- Clear DACPower Saving Mode- Both LEDs are off and CPU is in low power mode until the timer

interrupts the CPU to initiate the RED LED operationRED LEDInitialize Mode - Turn RED LED on after allowing enough time to pass for the analog signal

to become stable before sampling- Read the IR LED information stored in ADC register- The IR LED conversion data is also processed to get only the AC

component of the signal, track the DC component associated with the signal and update the DAC register to determine voltage offset, and finally ensure the LED intensity is within the correct range for the next sequence.

Clear DAC Mode- Turn off RED LED- Clear DACPower Saving Mode- Both LEDs are off and CPU is in low power mode until the timer

interrupts the CPU to initiate the IR LED operation

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2.4.3 SpO2 Algorithm

(8)

The general algorithm for oxygen saturation calculation is derived from the Beer Lambert Law using the ratio shown as equation (8). This derivation is outlined in great detail in many texts such as [1]. However, the Beer-Lambert law fails to give accurate results thanks to substances other than hemoglobin present in human blood. Regardless, an important trend exists where the ratio R, shown in equation (9), is proportional to SpO2 such that a lookup table can be used to determine accurate values from the calculation of R.

(9)

The table that relates the ratio to percent oxygen saturation is generated empirically so that each ratio value has an oxygen saturation value corresponding to it.

2.5 Performance Requirement

Due to the motion artifact limitation of pulse oximetry, while the subject is in motion the LED/Sensor module is powered off. This will also help to save battery life by minimizing the time that the LEDs are on. However, once the subject becomes stationary, the pulse oximeter will resume its normal activity to monitor the subject’s heart rate and oxygen saturation within a ±3% accuracy range.

By operating each device within its temperature range and current rating, the Bluetooth wireless feature should be able to operate within a 33 feet range, sufficient enough for the signal to be transmitted from upper to lower body region with high accuracy. The accelerometer sensitivity is temperature based on roughly 0.6%/C.

With an AAA battery powering the device, the run time is expected to exceed 24 hours. Should the user find him or herself needing additional time, the battery was chosen to be one which is easily available and replaceable.

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NameOperating Temperature

Min Max

Texas Instruments MSP430 DSP

-40C 85C

BlueSMiRF Bluetooth Modem

-40C 70C

Triple Axis Accelerometer

-20C 85C

Nonin® Reusable Reflective Sensor

0C 40C

The current ratings are as follows:

NameVoltage Range Current Usage

Sleep/Standby Active

Texas Instruments MSP430 DSP

1.8V - 3.6V 1.1uA 500uA

BlueSMiRF Bluetooth Modem

3.3V – 6.0V <10mA 70mA

Triple Axis Accelerometer 2.2V – 3.6V 3uA 500uA

Nonin® Reusable Reflective Sensor

+3.3VDCTypical Operation

8.78mA

+5.0VDC 9mA

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3. Verification

3.1 Testing Procedures

Android Cell Phone Communication with Bluetooth

The communication between the Bluetooth modules in the manufactured device must be thoroughly tested with the android phone. It must be ensured that a reliable connection between the two devices is made in a variety of situations. Different situations will be tested, such as having the phone in a backpack or having the phone in a pocket.

Accelerometer

The accelerometer output voltages will be monitored on lab-view while the device is dropped, vibrated and generally worn around. Knowing what types of signals are present in a variety of different situations will help in developing a threshold for the algorithm described above. Following this analysis, the accelerometer must be interfaced with the MSP430 and the appropriate algorithms must be programmed onto the microprocessor.

Pulse Oximeter Testing

The pulse oximeter signal outputs must be analyzed with lab-view in a variety of situations to determine how and when to get an accurate reading of blood oxygen saturation. Often times motion will impact the accuracy of the pulse oximeter reading. Using the accelerometer, the motion artifacts in the signal may be filtered out to provide an all around accurate reading. Also, a threshold must be determined for dangerously low blood oxygen and heart rate levels. This can be tested by having the user hold their breath or enter a period of rest to slow their heart rate.

The accuracy of the Sp02 reading from the oximeter must be calibrated to empirical data determined in clinical studies. This data must be found and utilized to allow calibration of our pulse oximeter.

Local Alarm Check

The local alarm will be tested to ensure it is audible by the user in a variety of situations and noise levels. This test will also allow testing of a variety of disarming methods. The method of disarming of the alarm must be tested to ensure that the alarm is never accidentally disarmed by the user.

Emergency Calling CheckThe emergency calling capability will be tested by dialing a personal cell phone

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or texting the emergency message. The final design will simply involve replacing the personal phone number with that of emergency services.

Overall Device Setup

The finalized device must be tested thoroughly with a variety of activity levels to determine that the device minimizes false alarms and correctly determines an emergency situation. Also, the finished device should be tested for battery life. This way the run-time for the device can be well known by the user to ensure that the device does not turn off before an emergency is encountered.

3.2 Tolerance Analysis

Some of the possible conditions affecting the accuracy of pulse oximeter include:

3.2.1 Motion Artifact

Any transient motion of the sensor relative to the skin could distort the output of the sensor by “mimicking” a heartbeat.

o According to the data sheet for Nonin® 8000R reusable reflectance SpO2 sensor, the accuracy of oxygen saturation measurement is expected to fall within +/-3% range under normal (hospital) operation.

Motion artifacts are resolved by taking in account of the output of accelerometer. If significant motion is detected by the accelerometer, then the pulse oximetry measurement is turned off.

Testing Procedure Expected output of photodiode to be low when accelerometer detects

motion. Check the LEDs on the Nonin® 8000r to be sure they are

turned off when the device is moved Ensure that when the user needs or desires their oxygen

saturation level, it is available.

3.2.2 Ambient Light Interference

Photodiodes may pick up ambient light signals, thus decreasing the signal to noise ratio of our sensor.

o Photodiode may be saturated due to strong intensity of light detected, impeding the ability of the device to detect the light that pass through pulsating arterial vessel

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Minimize ambient light interference by placing opaque material around the photodiode.

By using differential amplifiers, ambient light errors, which appear in both primary operational amplifiers, can be subtracted, eliminating the error [1].

Testing Procedure By observing output of pulse oximeter sensor, pulsating signal should

be distinct with low background noise. If the sensor output appears saturated, it may be due to the sensor being exposed to ambient light.

3.2.3 Increased Body Heat

An increase in body heat may cause the wavelength of LED to shift, causing a false reading.

o An increase in temperature from 0 – 50 degrees can create a 5.5nm increase in wavelength in a given 660nm LED, and a shift of 7.8nm in a 950nm LED [1].

First, the signal amplitude from each LED has to be normalized A DC transfer characteristic of the LED drive circuit is generated for

both IR, Red LED. Operate the Drive signals between 1V and 3V for a current at roughly

50mA.

Testing Procedure Ensure that the proper amount of current flows through the LEDs to

cause the DC offset of the signal from both LEDs to be equal.

4. Ethical ConcernsBefore this device is ever allowed to be manufactured and sold, it should be thoroughly tested in a multitude of conditions. The primary purpose of the device is to save human lives; however, the device could also potentially give an individual a false sense of confidence. If the device is relied upon to make a call in the event of an emergency and the call fails to be made in such an

emergency, a life could be lost for what would be a silly reason.

5. Cost and Schedule

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5.1 Cost Analysis

5.1.1 Labor

Average starting salary for Electrical Engineering major - $60,125 [7]

Average starting hourly wage (assuming 40 hr work weeks) - $28.90/hour

$28.90 x 2.5 x 100 hours = $7,225.00

5.1.2 Parts

Part Part # Description Quantity Price

U1 MMA7260Q3 Axis

Accelerometer1 8.72

M1 MSP430FG439Low Power

Microcontroller1 6.6

S1 Omron B3FTactile Switch, Momentary On

1 0.23

N1 CEM-1203 Buzzer 1 0.72

Q1 ECX-31B32.768kHz

Crystal1 1.26

Q2 2SC5939G0LPanasonic NPN

Switching Transistor

1 0.48

Q3,Q4 MMDT4401Diodes Inc.Dual NPN Amplifier

1 0.5

Q5,Q6 MMDT4403Diodes Inc.Dual PNP Amplifier

1 0.5

C1 C0402C109C8GACTU 1pF 25% 1 0.06C2,C3,C4 LMK212SD104JG-T 0.1μF 3 0.49

C5,C6 TDK C3216X5R0J106 10μF 2 0.22C7 C0603C100K8GACTU 10pF 1 0.03C8 C0603C103J8RACTU 10nF 1 0.059

R1,R3,R4,R12 ERA-S15J103V 10kΩ 5% 4 0.55R4,R5,R6 ERJ-3EKF1001V 1kΩ 1% 3 0.073

R7,R8,R9,R13 SM-8 5KOHMS 1% 5kΩ 1% 4 0.0365R10 RNF 1/8 T1 10 1% R 10Ω 5% 1 0.009R2 MBA02040C4704FRP00 4.7MΩ 1% 1 0.075

R16 RNF 1/8 T1 100K 1% R 100kΩ 1% 1 0.009R15 RNF 1/8 T1 1M 1% R 1MΩ 1% 1 0.0095

R14,R12 HHV-25JR-52-500K 500kΩ 1% 2 0.062L1 SLF12575T-100M5R4-H 10μHΩ 20% 1 0.96

BAT1 - AAA Battery 1 0.7

B1 RN-41Roving Networks Bluetooth Module

1 24.95

X1 D-SUB 9-pin Serial Connector 1 1.5D1 BAS17 Diode 1 0.19P1 TPS61010 Boost Converter 1 1.2

Nonin Sensor 8000R Nonin Reflective 1 160

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Forehead Sensor

5.1.3 Total

Total Parts: $213.36

Total Labor: $7225 x 3 = $21,675

Total: $21,888.36

5.2 Schedule

Week Team Member: Tasks

15-Feb

Steven - Continue Research, Program the MSP430/ Android PhoneParam - Continue Research, Program the MSP430/ Android PhoneCollin – Develop LED drive circuitAll - Order Parts and Develop Circuitry for Design Review

22-Feb

Steven – Test Accelerometer and fall detection algorithmParam – Work with processing algorithm for SP02 measurementCollin - Develop Sensor powering circuitAll - Work on Design review, Prepare to Present.

1-Mar

Steven – Implement algorithms for heart rate detection in MSP430Param – Implement algorithm for oxygen saturation computation in MSP430Collin – Determine Temp. and Motion effects on the OximeterAll - Integrate Parts with the MSP430

8-Mar

Steven - Interface Fall algorithm in MSP 430Param - Interface Fall algorithm in MSP 430/ Finalize Android app.Collin - Calibrate Sp02 Sensor to Temperature EffectsAll – Test Throurougly following Testing Procedures

15-Mar

Steven – Examine tolerance of circuit/ TestParam – Examine tolerance of circuit/ TestCollin – Compile components and begin ordering PCB boardsAll - Work on Android Development/ Ind. Progress Reports

22-Mar Spring Break

29-MarAll – Compile and assembly parts onto PCB board and mount sensor on demonstration helmet

5-Apr All - Mock-up Demo, Sign up for Mock-up Presentation12-Apr All - Mock-up Presentations19-Apr All - Test and Prepare for Presentations26-Apr All - Demo3-May All - Final Paper Due, Checkout

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References

[1] J.G. Webster, Design of Pulse Oximeters, New York: Taylor and Francis Group, LLC, 1997.

[2] A Single-Chip Pulse oximeter Design Using the MSP430

[3] Compensate Transimpedance Amplifier Intuitively, Texas Instruments., Dallas, Texas 2000

[4] MSP430x4xx Family: User’s Guide, Texas Instruments., Dallas, Texas, 2010

[5] A.S. Sedra, K.C. Smith, Microelectronic Circuits 5th Edition, Oxford University Press, 2007

[6] Human Fall Detection Using 3-Axis Accelerometer, Freescale Semiconductor., Chandler, Arizona, 2005

[7] Source: CNN Money, “Most lucrative college degrees”, 7/24/2009

[8] Diagnostic, Patient Monitoring and Therapy, Texas Instruments., Dallas, Texas, 2009

[9] Dual NPN Small Signal Surface Mount Transistors, Diodes, Inc., Dallas, Texas.

[10] Dual PNP Small Signal Surface Mount Transistors, Diodes, Inc., Dallas, Texas.

[11] Magnetic Buzzer, CUI, Inc., Beaverton, OR, 2006.

[12] OEM III Module Specification and Technical Information, Nonin® Medical, Inc., Plymouth, Minnesota, 2007.

[13] Three Axis Low-g Micromachined Accelerometer, Freescale Semiconductor., Chandler, Arizona, 2005.

[14] Class 1 Bluetooth® Module, Roving Networks. Los Gatos, CA, 2009.

[15] High-Efficiency 1-Cell and 2-Cell Boost Converters, Texas Instruments. Dallas, Texas, 2005.

[16] Y. Mendelson and C. Comtois, “A Wearable Reflectance Pulse Oximeter For Remote Physiological Monitoring,” Proc. Of the 28th annual International IEEE/EMBS Conference, New York city, 2006.

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