ASSESSMENT OF FORCE SENSING RESISTORS: CONTROL AND DESIGN FOR E-BRAILLE DEVICE

FINAL PROPOSAL

Timothy D. CarambatAdvisor: Dr. Mohammad Saadeh

Dr. Cris KoutsougerasET 493 Senior Design Project I

Fall 2016

ABSTRACT

The objective of this proposal is to continue the design and control of the E-braille device that

has been under research in previous semesters. In particular, this proposal will deal less with the

research and theory of the E-braille device-as it has been performed, and will instead focus on

the finalization and fruition of the E-braille device and control systems. The E-braille device is

an assistive technology for the visually impaired, allowing them to simulate tactile sensation in

the form of the Braille language. The E-braille device will be worn on the dorsal portion of the

user’s finger. Currently, the manner in which tactility is simulated is via an electronic tactile

display. The pressure in which the finger is subject to is controlled by a selected force sensing

resistor (FSR). The feedback from this sensor drives a miniature DC motor which in turn

controls the vertical movement of the top of the device via rack and pinion gears. The sensor

allows real time reactive force of the device on the finger pad to maintain a custom comfortable

pressure for the user. This proposal focuses on identifying several FSRs for use in the E-Braille

device in terms of the clamping mechanism. Also, it aims at investigating control methods, the

tactile display currently in use, and other design facets that improve device efficiency and user

experience.

INTRODUCTION

The main goal of the E-braille device is to provide a suitable, low-cost and low-maintenance

assistive technology for the aid of visually impaired persons. The E-braille is novel in its nature

as its technology is not expensive, an unfavorable characteristic that other devices have yet to

overcome, as well as providing a platform that accommodates a large percentage of users. The E-

braille device, in final form will be compact in nature, cheap to produce, accessible by the largest

available population of the target market, and powered externally using normally available

power sources. These physical traits in conjunction with necessary criteria for use by an impaired

user do present obstacles in design and control.

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The E-braille device, in full functionality, will provide a novel way for visually impaired users to

have information read to them without the need for audio or voice synthetization of their reading

material. Providing a more natural and private method of information exchange. The device

hopes to fulfill a personal and market need for such an assistive technology.

Additionally the underlying technology that I will be researching in terms of the FSR can be

easily utilized in other applications aside from this device. In its most simplistic operation the

FSR should operate as an accurate load cell. This technological advancement would be critical

in applications where load detection is required but due to space or cost restraints a typical load

cell is not feasible.

COMPONENTS AND CONSTRUCTION

Currently, as the device has been research and worked on previously, progress has been made in

regards to the physical manifestation of the device. The E-braille device is composed of several

necessary components working in harmony to provide the ideal user experience. The E-braille

device is primarily composed of a controller with necessary coding and software, the physical

clasping device with rack and pinion, a driving motor and gear to adjust clasp, tactile display

board, and force sensing resistor (FSR). Currently the prototype appears as follows:

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Figure 1-Prototype Figure 2- Prototype with hand

Figure 3-Assembly

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The prototype currently features larger than final-product componets for ease of data-acquisition

and continuing adjustments. The prototpye currently is controlled via a programmable

microcontroller, called an Arduino Uno, seen below:

Figure 4-Arduinio Uno

The Arduino Uno is a microcontroller board based on the ATmega328. It has 14 digital

input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz ceramic

resonator, a USB connection, a power jack, an ICSP header, and a reset button. It contains

everything needed to support the microcontroller; simply connect it to a computer with a USB

cable or power it with an AC-to-DC adapter or battery to get started.

Paired to this Arduino is an accessory for the controller, commonly called a “shield”. This shield

gives us the ability to communicate with the E-braille controlling motor easily, as well as

granting us the ability to utilize forward and reverse motion.

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Figure 5-MegaMoto Shield

Next, the critical feedback device, the force sensing resistor. FSR's a resistor that changes its

resistive value (in ohms Ω) depending on how much it has been compressed. The greater the

force the lower the resistance. These sensors are fairly low cost, and easy to use but they're

rarely accurate.

Figure 6-FSR Diagram and FSR

Finally, the motor to actuate the upper portion of the E-braille device is of low-cost and high

power. With a cross section measuring only 10×12 mm (0.39″×0.47″), these small brushed DC

gear motors have a gear ratios—from 5:1 up to 1000:1—and offer a choice between three

different motors: high-power (HP), medium-power (MP), and standard. (Figure 6).

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Figure 7-DC motor

Other componets currently are used as needed. Such as Date Aquisiton devices and software,

strain guages, amplifiers, filters, and other circuity devices. These items are used as needed for

analysis or testing purposes and may not be present in the final version of the E-braille device.

CURRENT STATUS:

The current status of the E-braille device is “semi-functional” in the manner that physical

componets preform their desired actions, but the control of such motions and actions do not

preform as desired from inputs. Currently, system control is not written and executed through the

Arduino development environment. This is due to its lack of interface and data-aquisiton in real

time. Using Arduino as a “slave”, all commands and code are run through computer desktop

software know as LabView. LabView is currently used for real-time data input, real-time data

acquisition and its ease of use and powerful analtyical libiraries while the device is running.

Actions that cannot be preformed with other softwares.

Previous reseach and devlopment of this deivce largrly hinged on the selection of the FSR. The

FSR is a system-critical componet as it is the feedback mechanism for the clamping action of the

device on the user’s phalange. It is important to note that an inherent property of all FSR’s is that

when compressed the output voltage and resitivity of an FSR is not linear, especially under light

compression.

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Figure 8-FSR linearity

Seeing as though the E-braille device will not be operating under high compressive force it can

be seen that a model will need to be used to measure and predict the output voltage and what

force that corresponds to, so that adjusts can be made if needed.

In order to attempt to linearize the FSR it would need to have some basis of measurement. To

accomplish this during testing a traditonal load cell was used. A load cell was used for their

predicitabilty and accuracy, normally. The load cell used in past testing was extremely senstive

and would have to be continually calibrated after powering off, which caused small measurement

errors during testing. Overcoming the load cell error and sensetivities extended testing time

considerably. Pictured below is the old Omega LC201 load cell as well as the new FUTEK

lrf400 load cell.

Figure 9-(Left) Omega LC201 and FUTEK lrf400 (Right)

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-1 0 1 2 3 4 510

1

102

103

104

105

Force (N)

Res

ista

nce

(koh

m)

Testing of the FSR:

Given that due to human error it is impossible and impractical to provide a continous and

accurate force; a cam actuating mechansim was developed and implemetned into testing.

Pictured below is the first model of the cam actuator. This machine would be used to simulate

sinusodial, triangular, square, and other traditonal signals.

Figure 10 &11- Cam Mechanism

Modeling, Prediction, and PID control:

In addition to the testing of the FSR, this data was collected and implemented into a

mathematical model called the Hammerstein-Wiener model. The model actually represents an

amalgamation of two non-linear system identification models, Hammerstein and Wiener. For

more information on the workings on the model and identification process used, the reader is

deferred to (Saadeh and Trabia, 2012).

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An example of system-identification would be shown as below.

Figure 12-Hammer-Wiener System Identification

It can be noted that the Hammer-Weiner model is fairly accurate in the identification of the

system as compared to each model independently and linear models. For future testing it may be

of better value to use the individual Hammer or Wiener models. This will be determined from

data. Above is simply a single case example and does not mean this particular system type will

be the best overall for all models of FSR’s

The reasoning for use of this model was to identify the FSR signal and then feed this data into

the Proportional, Integral, Derivative (PID) controller to control the motor and clamping force.

The PID takes the error from actual measurement and the system-prediction model and uses this

difference to adjust the PID controller to bring the motor on target more quickly without

oscillation or overshoot of the set force. The tuning of this control as well as effectively pushing

data into the model and making adjusts in real-time without delay to the system is where

progress was halted due to time constraints.

ADVANCEMENTS MADE

Setup new load cell and configure the device.

Make adjustments to cam mechanism to accommodate new load cell.

Identify FSRs using new load cell to calibrate them using cam mechanism.

Signal mapping to Arduino around arbitrary set point from user.

Data Acquisition without slowing system in use.

BREAKDOWN OF ADVANCEMENTS MADE:

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Setup new load cell:

The new FUTEK LRF400 load cell arrived in my possession mostly disassembled. The

physical load unit, was however, a solid component that came pre-calibrated from the factory.

The LRF400 load cell came with peripheral devices for data acquisition such as an

amplifier/filter, a serial connection assembly kit, and a cable to allow communication of the

LRF400 with a data acquisition device if needed. A photo below shows the LRF400 that is

hooked to an OMEGA data acquisition device. It should be noted that in this setup I have both

the old Omega load cell and the new Futek cell being analyzed by the DAQ device so that I may

compare the steady state signal of both load cells.

Figure 14-DAQ setup of Futek and Omega Load Cell

A sample of data can be seen below. The noise frequency can be seen by each load cell in

the graph, the new Futek cell in red and the Omega in yellow. Both cells are unloaded in this

experiment as well as zeroed. It was noted that after some time the noise of the Omega load cell

was excessive in comparison to the Futek cell. This test, as well as a hysteresis experiment,

proved the Futek cell to be far superior to the Omega load cell for uses of fine data acquisition.

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Figure 15- Comparison of Futek Cell (red) and Omega Cell(yellow) unloaded noise

Make adjustments to cam mechanism to accommodate new load cell:

It is evident that the two load cells for our analysis have dramatically different

dimensions. Given that the Futek cell appears to be a more stable platform for testing; the cam

mechanism initially used to test the Omega load cell will have to modified extensively so that it

may support the new Futek cell without difficulty or testing error. Modeling of the Futek cell in

the old cam mechanism is seen below.

Figure 16-Full cam assembly with Futek Cell structure accommodations.

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Figure 17-Futek LRF400 Cradle

Figure 18-Cam mechanism riser to adjust for height of Futek Cell

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Identify FSRs using new load cell to calibrate them using cam mechanism:

When undergoing the retrofitting of the new cam mechanism it was evident that the new

load cell would necessitate the need for revisions to the cam mechanism that was designed prior

to the spring 2016 semester. These parts were designed with the intention of allowing the new

FUTEK load cell to be centered beneath the cam. The parts design can be seen in Figures 17-18.

These parts would simply allow the FUTEK cell to be center-aligned to the cam mechanism and

would rise the entire cam mechanism by the difference in height of the Omega and FUTEK load

cells. Functionally, the old system and new system would be identical.

Prior to the actual printing of these new components, testing was performed using the sinusoidal

cam. Immediately, there were issues with testing. The returning spring force on the cam seemed

to prevent the motor from rotating the cam. For example, at the bottom of eccentricity of the

cam, where the returning spring would be compressed, the return force of the spring would lock

the motor-voiding all data in the test. This was remedied by modifying the returning spring.

After adjusting the spring, it was discovered that during revolutions-right after the bottom of the

eccentricity- the cam would accelerate and spin faster than the motor speed. Additionally,

sometimes the cam would not compress the spring and would lock. Usage with other springs also

displayed that the cam follower was making off-center contact with the cam, making contact

difficult in continuous rotation.

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Figure 19-Bad contact of cam with follower. Using older designed for Omega load cell

It became evident that the motor was under-powered for our required torque needs. This was

simply a real-world testing error. Replacement of the motor was imperative, this would prove to

be an issue as the existing cam structure was 3D-printed and will not be easily modifiable and

revisions cannot be undone once preformed. After advisement, it was decided that constructing a

similarly functioning system would need to be done, but this new system would need to be

modular and have revisions be able to be done easily with minimum modification to the system.

It was decided to use an assembly kit. The kit would need to be modeled first and custom

components designed afterwards so that the assembly could come together.

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Figure 20-Isometric view of new cam assembly

Figure 21-Front view of new cam structure

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Figure 22-Exploded view of new cam structure

Figure 23-Solidworks model of DC cradle

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Figure 24-Solidworks model of FUTEK cradle

Figure 25-New printed parts for new cam structure

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Figure 26-Futek Cell in cradle

Figure 27-High Torque DC motor in cradle

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Figure 28-New assembly pre-construction

As it can be seen the new the system is easily pieced together and the motor can be replaced with the mounting bracket to accommodate it. Otherwise, the system is functionally the same as the old mechanism, but should be able to allow easy modification for use of new motors as to obtain our speed and torque requirements.

The motor currently used on the old cam mechanism was, as discussed, not capable of torque requirements to spin the cam while force was applied on the cam. We have decided to move to a multitude of motors that will spin much slower (typically around 2Hz, or 120rpm). Currently supplied are two geared DC motors and three geared stepper motors. The DC motors seem to spin at a usable rpm with great torque output. Currently, a DC motor was assumed to be utilized with the new cam structure. The stepper motors do not seem to reach an operable rpm, this seems to be a gearing issue, as the stepper motor cannot rotate the main shaft quickly enough with the gearbox attached. From testing it appears to be limited to approximately 1HZ, which will be used in lower frequency testing.

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Figure 29- Available Testing motors. DC left and steppers right

Signal mapping to Arduino around arbitrary set point from user:

Using the LabVIEW software that is currently being utilized to analyze the FSR input of

the E-braille device, this problem has been solved. When I began working on this issue it was

evident that the motor controller had issues discerning which direction to drive the rack and how

slowly it should do so depending on both the input of the FSR and the user’s custom set point of

comfortability. It should be noted that the LabVIEW software does not have a map function for

this specific utilization. I was able to accomplish this by utilizing the Arduino map function

mathematics and transferring this into MATLAB code. I was then able to make a MATLAB

function called “MAPV” that took exactly the same parameters as the function would in

Arduino. Using a continuous loop function in LabVIEW I was able to continuously input the

FSR value and user set point into the system that would then rotate the E-braille motor in the

right direction and magnitude to make adjustments on the load experienced by the FSR. Upon

solving this I then found the motor was constantly making adjustments, causing excessive heat.

This was solved by simply making a range of acceptable values around the user set point.

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Allowing the motor to rest and cool while also maintaining the correct pressure on the user’s

finger.

Figure 30-LabView Interface and Code. Lower blue window is MATLAB script that performs MAPV function.

Data Acquisition without slowing system in use:

In order to identify the FSR signal and correlate this into a usable force via the

Hammerstein-Wiener model, we must first collect the FSR data to determine this signal. Initially

the system had issues during data collection that while attempting to save the data and record it

simultaneously the system would run extremely slow, running any data being collected. I was

able to get around the system performance lag during collection by locally saving the data into a

simple array when “Save” was enabled. This data was stored locally in temporary memory and

allowed collection to continue uninhibited, where each millisecond of runtime correlated into a

sample of data. After ending the testing the data is then saved in a folder on the testing computer

as a formatted Excel spreadsheet. Allowing the data to be reviewed as well as read back into a

LabVIEW code for analysis later by the Hammerstein-Wiener model. Currently the issue with

this prediction model seems to be the coefficients. These coefficients and input of this data real

time will be the next obstacle.

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Figure 31- LabVIEW display and code that solves data acquisition performance issues.

Design/tune a proportional-integral-derivative (PID) control system to drive a DC-motor:

This objective remains unfinished at the writing of this report. The PID system will be

integral to the use of the E-Braille device motor actuation. The PID system is used after the

Hammerstein-Weiner model prediction. The input of a value from the model into the PID will

likely be easy to configure, but the coefficients of the PID controller may have to be fine-tuned

later in this projects development. The coefficients will likely have to be determine empirically

through testing, which should be easily achieved through LabVIEW.

CURRENT OBJECTIVES (DELIVERABLES):

Finish new cam construction and begin testing.

Identify best system model for each model FSR.

Identify FSR from selection for best use in E-Braille application.

Have model work with real time system.

Integrate PID control to adjust user comfort via force input from FSR.

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Finish new cam construction and begin testing:

As discussed from previous efforts, the cam mechanism originally designed had to be initially modified to accommodate the newer FUTEK load cell. It was then later discovered that testing with the older cam mechanism was not reliable enough to produce usable data for identification of the FSR’s. This unreliability lead to the need for an entire re-design of the cam system. The new system was going to take a modular approach so that adjustments could be made easily or new parts could be integrated into the system.

During the interim semester work was done to complete the cam mechanism. Structurally the system was rigid and was promising to be a good platform in which to start obtaining FSR’s signals.

Figure 32- Newly Designed Cam Mechanism Setup

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For the cams used in these experiments they had to be outfitted to fit with the new motors that would be used for testing. It was decided that for low-frequency tests (~1Hz) a geared stepper motor would be used. For higher frequency (~10Hz) tests a geared DC motor would be used. For each FSR, cam tests would be performed with a sinusoidal and triangular cam profile.

Figure 33- Cam Attached to Stepper and Hub Configurations for mounting. DC motor is circular hub.

Currently, there are 5 FSR models that will be tested. They are all functionally similar but through testing, they appear to output various voltages at varying applied forces.

Figure 34- All FSR’s. A-E from left to right

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For each FSR the following tests were run, usually multiple times so that consistent results could be determined.

Sinusoidal Chirp Signal (.5Hz to 10Hz) Sinusoidal Signal (1Hz Constant) Triangular Signal (.5Hz) Triangular Signal (1Hz) Triangular Signal (2Hz)

This objective remains current as until testing and all FSR models are recognized testing is done on a case-by-case basis if results seem to be inconclusive or the data is not usable for analysis.

Identify best system model for each model FSR:

This is the most system-critical component of not only the purpose of this project, but also the key functionality of the E-Braille device. The system model used for each FSR will be the key component that allows it to function as a load cell. If this functionality is not achieved this technology will have no more use that it does now in force-sensitive applications, let alone application in the E-Braille device as the feedback for the clamping mechanism.

It should be clarified that from the possible systems (linear, Hammer, Weiner, Hammer-Weiner) that system should already be as optimized as possible in its configuration so that it performs as accurately as possible under all conditions of use.

Identify FSR from selection for best use in E-Braille application:

After identify the system models for each FSR it must then be determined with FSR to integrate into the E-Braille assembly. This is imperative because from past testing and results so far, each FSR is sensitive at different regions of force application. Being that the E-Braille device will not be clamping the user’s finger under high forces, the FSR should be most sensitive under light loads up to approximately 2N.

Have model work with real time system:

After integration of the FSR into the E-Braille device it must then utilize the design system. Under regular operation the FSR in the E-Braille device should input a voltage into the system and return a force. With the force value other operations can be done. The system should do this operation easily and without notable lag. If the system does not perform the process quickly enough enhancements will need to be made to either the computations or the system.

Integrate PID control to adjust user comfort via force input from FSR:

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Lastly the entire system should work under PID control so that motor adjustments are not made suddenly and that the user’s comfort level is obtained quickly, but without overshoot and oscillation around the set point. Competition of this step would then have a fully functional system where the clamping action of the E-Braille device is a direct result from the technology enhanced from the identification of the FSR behavior-a practical application of this new development in FSR technology.

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References

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3. Florez, J. and Velasquez, A., 2010, “Calibration of Force Sensing Resistors (FSR) For Static and Dynamic Applications,” 2010 IEEE ANDESCON, pp.1-6.

4. FUTEK Advanced Sensor Technology. (n.d.). Retrieved February 28, 2016, from

http://www.futek.com/product.aspx?stock=FSH00264

http://www.futek.com/product.aspx?t=instrument&m=csg110

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http://www.omega.com/pptst/OMB-DAQ-2408.html

http://www.omega.com/pptst/LC201.html

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9. Saadeh, M. & Trabia, M. (2012). "Identification of a Force Sensing Resistor

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Structures, JIMSS, 24(7): 813-827.

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