Project_2_Group_C_5.pdf

Analyses of the X-Nucleo-IKS01A3 and Quanser based
sensors measurement characteristics
Bjørge Zagros Rysstad (203363) and Sigve Hamilton Aspelund (561868)
Department of Electrical Engineering and Computer Science, University of Stavanger, Norway
bz.rysstad@stud.uis.no, sh.aspelund@stud.uis.no
Abstract. The project consists of an analysis of various sensors by collecting data
from the sensors’ steady-state output, as well as their disturbed steady-state output.
The measured values are then plotted as histograms. The sensor characteristics
were then listed from each sensor by referring to their respective data sheets or
using the digital interface. Data was also collected from several activities for the
purposes of HAR analysis in a later project.
Keywords: Sensor characteristics · Random and systematic errors · Steady-state mea-
surements · HAR
1 Introduction
We were given the following sensors to catalog:
– LSM6DSO MEMS IMU Accelerometer
– LPS22HH MEMS Pressure
– LIS2MDL MEMS Magnetometer
– LIS2DW12 MEMS Accelerometer
– STTS751 Temperature
– HTS221 Capacitive Humidity Sensor
– Quanser Board sensors
Fig. 1: Stacked X-Nucleo Board. Fig. 2: Quanser Board.

2 Bjørge Zagros Rysstad (203363) and Sigve Hamilton Aspelund (561868)
We provided a variety of characteristics for the aforementioned sensors, taken in the
order they are listed the textbook used for the course. Some of the listed characteristics
were not mentioned in the data sheets we looked at, which are marked as ’not listed’.
We also measured the steady-state and disturbed steady-state of the sensors, and then
plotted them as histograms to compare the distribution with the expected normal distri-
bution curve. The results follow in the Experimental results chapter. Finally, we provided
measurements of several activities using the STM32-Nucleo board and Unicleo-GUI
for data collection for the purpose of Human Activity Recogntion (HAR). This data was
not to be analyzed or described in this project.
2 Data Collection
2.1 Steady-state method
Steady-state protocols were performed by leaving the sensors undisturbed while collect-
ing measurements for 60 seconds. For the STM32-Nucleo board, it was simply left on
the table facing upwards.
Fig. 3: The PC and sensor boards are left untouched at a distance during steady-state
data collection.

Sensors and Measurements (ELE230) Project 2 3
2.2 Disturbed steady-state method
This section of the data collection was perfomed by simulating various disturbances
during another 60 second measurement of data. For example, for the thermistor we
blowed on it with hot air, and the pressure sensor was intermittently pressed or lifted.
Fig. 4: Example screen capture from the data collection process of doing a disturbed
steady-state test for the LSM6DSO. Intermittent disturbances are introduced throughout
the steady-state process.

Fig. 5: Photo during the measurement process of disturbed steady-state.
2.3 Activities of Daily Life Human Activity Recognition method
The board is fastened to our arm according to the orientation specified in the project
manual, and we perform all listed activities for the specified amount of repetitions.
Fig. 6: Showing how the board is attached to the arm during data collection for HAR
activities.

2.4 Sensor Data
LIS2DW12 MEMS Accelerometer
– Active/Passive: Active (Supply voltage, 1.62V to 3.6V).
– Analog/Digital: Digital.
– Null-type/Deflection: Null-type.
– Accuracy: The zero-g level offset accuracy is ±20 mg.
– Precision: No mention of precision or reproducibility of measured values.
– Range: The operating temperature range is from -40 to +85°C. Measurement range
is ±2, ±4, ±8 and ±16 g.
– Threshold: Has a customizable range of thresholds for various functions, such as
from sleep-to-wakeup (wakes up when axis rotations exceeds a certain threshold),
as well as for tap detection (has a double-tap functionality that can be programmed
for user interfacing) and free-fall detection (also has a customizable threshold for
duration of detected free fall and a scale from ±2𝑔). The ”regular” 4D/6D detection
thresholds can be adjusted to span 80, 70, 60 or 50 degrees by adjusting the corre-
sponding 6D THS bits ranging from 00 to 11 (as per Table 60[2]) for operating at a
range of ±2𝑔.
– Resolution: Datasheet lists 16-bit resolution for the output data, however the oper-
ating modes work on 14-bit resolutions (and 12-bit for Low-Power Mode 1) (Table
10[2]).
– Linearity characteristic: No particular mention of the inherent linearity charac-
teristic, however it is simply stated in the datasheet description to be a linear
accelerometer.
– Smart/Non-smart: Smart; has data processing capabilities (i.e. wake-up detection,
double-tap and so forth).
– Indicating Instrument or Output Signal: Output Signal
– Sensitivity of Measurement: There is a lot of variance in the noise listed across
tables in the datasheet, so instead we will simply refer to the lowest listed RMS
noise, which is 1.3 mg/(RMS).
– Sensitivity of Disturbance: In figure 7 we can see a table over mechanical charac-
teristics listing sensitivity values that vary depending on the chosen operating mode.
Since 0 LSB represents 𝑇 = 25◦𝐶, each additional digit adds the listed values of
mg (where 𝑔 is gravity) to the measurement sensitivity.
Additionally, the sensitivity change vs. temperature is listed to be 0.01 %/𝐶, al-
though it is unclear whether this is simply a generalization of the above sensitivity
relating to amount of temperature digits, or an additional sensitivity component that
works in combination.
– Hysteresis/Dead space: Cannot find relevant information on hysteresis effects or
dead space.
– Sensor order: Sensor order (or time constant) is not found in the data sheet. Appears
by the measurements to be 0th order.
LSM6DSO MEMS IMU Accelerometer (gyroscope)
– Active/Passive: Active (requires a supply voltage of 1.71 V to 3.6 V).

Fig. 7: Sensitivity
– Accuracy: Found to be +-20 mg, from the ‘linear acceleration zero-g level offset
accuracy’ in the datasheet (from Table 2[3]).
– Precision: Found no specific information in regards to precision.
– Range: The range in which the sensor is guaranteed by the data sheet to function is
-40 to +85 degrees C.
– Threshold: Threshold for 4D/6D functionality has a default value of 80 degrees
(SIXD THS[1:0] = 00), but it is customizable to threshold values of 70, 60 and 50
degrees as well, by setting the correct bits (from Table 126 in the datasheet). (Did
not include “TAP threshold” information or “Threshold for wakeup”)
– Resolution: The Analog-to-Digital conversion process has a resolution of 16 bit,
with a 52 Hz refresh rate.
– Linearity characteristic: Could not find a specific reference to the percentage of
Full Scale Output (FSO) in relation to a Best Fit Straight Line (BFSL), but Table
2 in the datasheet gives a linear acceleration of +0.01 % per degree C for linear
acceleration sensitivity change vs. temperature, and angular rate sensitivity change
is given as 0.007 % per degree C.
– Smart/Non-smart:. No microprocessor or controller listed in its architecture from
the data sheet, meaning it should be non-smart.
– Sensitivity of Measurement: Similarly as for the linear characteristics, the sensitiv-
ity of disturbance is given for various tested ranges, in the form of mg/(RMS) (Root
Mean Square). This also varies depending on if the sensor is in normal/low-power
mode, or if it is in ultra-low power mode (more noise than normal).

Fig. 8: Taken from Table 2 in the datasheet[3].
– Sensitivity of Disturbance: The sensitivity of the measurements varies on the
specified range of the sensor. Since FSO has customizable ranges, these are given
separately in the data sheet[3], as shown below in Figure 9.
Fig. 9: Taken from Table 2 in the datasheet[3].
– Hysteresis/Dead space: Did not find any relevant information in regards to hystere-
sis or dead space effects.
– Sensor order: Appears to be 0th order.
HTS221 Capacitive Humidity Sensor
– Active/Passive: Active.
– Accuracy: ±3.5% rH for a range between 20% to 80% relative humidity, as well as
±0.5 °C for temperatures between 15 to 40°C.

-40 to +85 ◦𝐶.
– Threshold: 0.004%rH/LSB for humidity, and 0.016°C/LSB for temperature.
– Resolution: 16-bit humidity and temperature output data
– Linearity characteristic:
– Smart/Non-smart:. No microprocessor or controller listed in its architecture from
the data sheet, meaning it should be non-smart.
– Sensitivity of Measurement: RMS noise is listed at 0.007.
– Sensitivity of Disturbance: 0.05 ◦𝐶/year.
– Hysteresis/Dead space: ±1 % rH hysteresis effect for humidity.
– Sensor order: 1st order (has a listed typical time constant of 15s, and ’capacitive’
is also in the name, which has a differential circuit component)[4].
STTS751 Temperature Sensor
– Accuracy: ±0.5 °C (typical value)
-40 to +125 ◦𝐶.
– Threshold:
– Resolution: 4 different (customizable) resolutions are listed, ranging from 9-bit (0.5
°C/LSB) to 12-bit (0.0625 °C/LSB). 10-bit is the default resolution[5].
– Linearity characteristic: Not listed, but assumed to be linear.
– Smart/Non-smart:. Smart; it is programmable.
– Sensitivity of Measurement: No listing of RMS noise or sensitivity.
– Sensitivity of Disturbance: No listing of temperature drift or sensor drift.
– Hysteresis/Dead space: Did not find any relevant information in regards to hystere-
sis or dead space effects.
– Sensor order: Appears to be 0th order from the nature of displayed measurements.
LIS2MDL MEMS Magnetometer
– Active/Passive: Active (Analog supply voltage 1.71V-3.6V) 16-bit data output using
an analog-to-digital converter
– Analog/Digital: Digital (provides digital value as output).
– Accuracy: The LIS2MDL has a magnetic field dynamic range of ±50 gauss. This
means an uncertainty at ±50 gauss.
– Precision: No information regarding precision specifically is found, and so the
variability of measurements will be affected by the sensitivity of measurement.

– Range: Magnetic dynamic range at ±49.152 gauss. Temperature operating range
from -40 to 85°C.
– Threshold: Only found threshold info regarding customizability of interrupt pins
(INT), which is an absolute value tied to the current gain. Crossing the threshold is
detected on both positive and negative sides.
– Resolution: This sensor has a Selectable power mode/resolution and a High-
resolution mode at 3 mgauss (RMS). The temperature sensor output change vs
temperature has a 12-bit resolution at 8 digits/°C.
– Linearity characteristic: Found no particular mention of the linearity characteris-
tic.
– Smart/Non-smart: Non-smart; dependent on an external microcontroller/applica-
tion processor to write MD[1:0] bits.
– Indicating Instrument or Output Signal: Output Signal.
– Sensitivity of Measurement: Figure 10 shows the power modes, RMS noise of
operation modes with the values of the high-resolution modes and low-power tables.
Fig. 10: Sensitivity of measurement from RMS noise[6].
It is also listed as ±7%, and so the given RMS noise are typical values.
– Sensitivity of Disturbance: The sensitivity change vs. ambient temperature is ±0.03
%/°C
dead space.
– Sensor order: Cannot find any sensor order data in the sheet, but from observing
the measurements it appears either as 0th order (a resistor on its own also has no
differential circuit components).

LPS22HH MEMS Pressure
– Active/Passive: Active (needs supply voltage 1.7- 3.6V)
– Analog/Digital: Digital (converts analog to 24 bit gas output)
– Null-type/Deflection: Deflection.
– Accuracy: Absolute pressure accuracy (0.5 hPa), meaning an uncertainty of +-0.5
hPa
– Precision: Low pressure sensor at 0.65 Pa, meaning it has a spread up 0.65 Pa error
in the measurements.
– Range: Range between 260 to 1260 hPa absolute pressure, and up to 24-bit pressure
data output.
– Threshold: (Not listed)
– Resolution: The resolution is 24 bit.
– Linearity characteristic: Could not be easily found in the data sheet.
– Smart/Non-smart: Non-smart (no microprocessor/controller).
– Indicating Instrument or Output Signal: Digital Output
– Sensitivity of Measurement: Listed at 0.0065 hPa RMS.
– Sensitivity to Disturbance: From the datasheet, the temperature coefficient offsets
is noted to be ±0.65 Pa/◦𝐶
dead space.
– Order of the instrument: Undetermined.
Quanser Magnetic sensor
– Active/Passive: Active (magnetic fields need moving electrons, which requires an
active energy supply)
– Analog/Digital: Digital (output shown on PC).
– Null-type/Deflection: Null-type
– Accuracy: (Not listed)
– Precision: (Not listed)
– Range: (Not listed)
– Resolution: (Not listed)
– Linearity characteristic: Output voltage is linearly proportional to the magnetic
field that is applied perpendicularly[1], but in practice it seems to vary slightly due
to the 1st order nature (as seen in Figure 11).
– Smart/Non-smart: Non-smart
– Indicating Instrument or Output Signal: Output Signal (emf induced voltage)
– Sensitivity of Measurement: (Not listed)
– Sensitivity of Disturbance: (Not listed)
– Hysteresis/Dead space: (Not listed)
– Sensor order: 1st order.

Fig. 11: Screencapture from PC while utilizing the Quanser board while using the
Magnetic field sensor. The right-side shows the characteristic and also indicates the
order of the instrument.
Quanser Infrared sensor
– Accuracy: (Not listed)
– Range: Measurement range of 10 to 150 cm. Temperature operating range of 5 to
40 ◦𝐶.
– Linearity characteristic: Linear (outputs a voltage corresponding to the distance
to the target[1]).
– Smart/Non-smart: Non-smart.
– Sensor order: 1st order.

Fig. 12: Screencapture from PC while utilizing the Quanser board while using the
Infrared sensor. The right-side shows the characteristic and also indicates the order of
the instrument.
Quanser Temperature sensor
– Active/Passive: Active (needs a current through it to register voltage difference with
temperature change).
Fig. 13: Circuit diagram from the Quanser datasheet[1] for the thermistor.

– Analog/Digital: Digital (output shown on PC, even though pure sensor functionality
is analog).
– Null-type/Deflection: Null-type
– Accuracy: Uncertainty of the 47 𝑘Ω thermistor at 25◦𝐶 listed to be ±5%.
– Range: 5 to 40◦𝐶
– Linearity characteristic: Resistance is a linear circuit element.
– Smart/Non-smart: Non-smart
– Indicating Instrument or Output Signal: Provides a voltage output signal.
– Sensor order: 0th order.
Fig. 14: Screencapture from PC while utilizing the Quanser board. The right-side shows
the characteristic and also indicates the order of the instrument.
Quanser Pressure sensor
– Accuracy: ±2 kPa
– Range: Measurement range from 60 to 165 kPa, and operating range of 5 to 40 ◦𝐶.
– Linearity characteristic: Linear.

– Sensitivity of Measurement: No other value relating to RMS noise other than the
listed ”accuracy” given above.
Fig. 15: Screencapture from the PC while utilizing the Quanser board and Pressure sen-
sor. The right-side shows the characteristic and also indicates the order of the instrument.
Quanser Ultrasonic sensor
– Active/Passive: Acitve.
– Accuracy: +2.54 cm
– Range: Measuring range of 15.24 to 645.16 cm. Sensor temperature operating range
of 5 to 40 ◦𝐶.
– Resolution: ±2.54 cm
– Linearity characteristic: Linear (outputs voltage that correlates to the distance to
the target, i.e. proportional).
– Sensitivity of Measurement: ±2.54 cm

Fig. 16: Screencapture from the PC while utilizing the Quanser board and Ultrasonic
sensor. The right-side shows the characteristic and also indicates the order of the instru-
ment.
3 Experimental results
3.1 Example code used for plotting histograms
%% Reading interference data from CSV and plotting histograms ,
normal plot and performing chi-square test.
%% Data loading and pre-processing
% Specify file format with quanser=1 or 0, depending on if it
was STM board
% or quanser data.
quanser=1;
filenumber=10;
% Stripping data and cleaning
if quanser==0
folder=’/STM_filer/’;
file_list={"stm32-magnetic -disturb.csv",’stm-magnetometer -
steady-state.csv’,’stm-temp-disturbed -steady -state.csv’,’stm
-temp-steady-state.csv’,’stm-tilt-test.csv’,’A0S230R1’};

file=folder + string(file_list(filenumber));
DataTable=rmmissing(readtable(file),2);
else
folder=’/Quanser_filer/’;
file_list={’Prosjekt2_Steady_state_Infrared.csv’, ’
Prosjekt2_Steady_State_thermistor_maling.csv’, ’
Prosjekt2_Steady_state_magnetic.csv’, ’
Prosjekt2_Steady_state_ultrasonic.csv’,’
Prosjekt2_Steady_state_Pressure.csv’,’
Prosjekt2_Disturbed_steady_state_Infrared.csv’, ’
Prosjekt2_Disturbed_Steady_State_thermistor_maing.csv’, ’
Prosjekt2_Disturbed_steady_state_magnetic.csv’,’
Prosjekt2_Disturbed_steady_state_ultrasonic.csv’,’
Prosjekt2_Disturbed_Steady_state_Pressure.csv’};
file=folder + string(file_list(filenumber));
DataTable=rmmissing(readtable(file,DecimalSeparator=","),2);
end
%% Data processing and plotting
for i = 2:width(DataTable)
VariableName=DataTable.Properties.VariableNames{i};
k=1;
measurant_data=DataTable{:,i};
figure;
subplot(1,2,k)
% Plotting normal density function as subplot
normplot(DataTable{:,i}); title([’Normal Probability plot
for ’ VariableName], ’Interpreter’, ’none’)
k=k+1;
subplot(1,2,k)
maxdata=max(measurant_data); mindata=min(measurant_data);
% Calculating bins using Sturges ’ rule
n_bin = round(log2(length(measurant_data)+1));
width_bin = (maxdata - mindata) / n_bin;
bin_edge = linspace(mindata , maxdata , (n_bin+1));
hist_count = histcounts(measurant_data , bin_edge);
% Calculating fitted normal distribution
pd=fitdist(measurant_data(:), ’Normal’);
xd=linspace(min(bin_edge),max(bin_edge),100);
yd=max(hist_count)*exp(-(((xd-pd.mean).ˆ2)/(2*pd.sigma.ˆ2)))
;
% Plotting histogram with overlapping normal distribution
hist=histogram(measurant_data ,’BinWidth’,width_bin); hold on
;
plot(xd,yd,’-r’,’LineWidth’,3)

title([’Histogram plot for ’ VariableName], ’Interpreter’, ’
none’); xlabel(’Frequency of measurement’);
ylabel(’Measured value’);
%% Chi-square test for comparison with normal distribution
at various alpha levels
mu=mean(measurant_data(:));
sigma=std(measurant_data(:));
% Checking that sample size is considered sufficiently ’
Large’ (=>100) for application of
% chi-squared
alpha=[0.01 0.05 0.10 0.25];
if length(measurant_data)>100
warning(’off’,’stats:chi2gof:LowCounts’)
compare_dist=makedist(’Normal’,’mu’,mu,’sigma’,sigma);
for m=1:length(alpha)
[h(m,(i)),p(m,(i))]=chi2gof(measurant_data ,’cdf’,
compare_dist , ’Alpha’,alpha(m));
% Null Hypothesis is that the Data is Gaussian
if h(m,(i)) % Distribution comes from a Normal
Distribution
fprintf([’Null Hypothesis was rejected for ’
VariableName ’ at an alpha of: <’ num2str(alpha(m)) ’> n’],
’Interpreter’, ’none’)
else % Distribution does not fit a Normal Distribution
fprintf([’We fail to reject the Null Hypothesis for
’ VariableName ’ at an alpha of: <’ num2str(alpha(m)) ’> n’
], ’Interpreter’, ’none’)
end
end
else % If measurements are too low, kstest is performed
instead
for m=1:length(alpha)
[h(m),p(m)]=kstest(measurant_data ,’cdf’,compare_dist , ’
Alpha’,alpha(m));
if h(m)
fprintf([’Null Hypothesis was rejected for ’
VariableName ’ at an alpha of: <’ num2str(alpha(m)) ’> n’],
’Interpreter’, ’none’)
else
fprintf([’We fail to reject the Null Hypothesis for
’ VariableName ’ at an alpha of: <’ num2str(alpha(m)) ’> n’
], ’Interpreter’, ’none’)
end
end
end
warning(’on’,’stats:chi2gof:LowCounts’)

end
Listing 1.1: MATLAB code used for reading data, splitting it, calculating bins using
Sturges’ rule and then plotting the measurand data as histograms with normal distribution
plots fitted for visual clarity. A normal probability plot is also listed, where a constant
gradient line implies a Gaussian relationship. Finally there is code to perform a Chi
squared test from the measured samples fitting them to a Gaussian distribution.
3.2 Tests for Chi-Square (𝝌2): Types, Formula, and Examples.
For categorical data, a statistical test called the Pearson’s chi-square test is used. It’s
employed to ascertain whether our data deviate appreciably from your expectations. The
Pearson’s chi-square tests come in two varieties:
To determine if the frequency distribution of a categorical variable deviates from
your expectations, we apply the chi-square goodness of fit test.
If two category variables are not connected to one another, they may be tested using
the chi-square test of independence.
Chi-square is pronounced “kai-square” (rhymes with “eye-square”) and is sometimes
written as 𝜒2. Another name for it is chi-squared.
Chi-Square Fit Test: Formula, Instructions, and Examples A variation of Pearson’s
chi-square test is the chi-square (𝜒2) goodness of fit test. It may be used to determine if the
observed distribution of a categorical variable deviates from what you had anticipated.
Chi-square goodness of fit test, for instance: A dog food manufacturer hires us to
assist in the testing of three new dog food tastes. 75 dogs are chosen at random, and
bowls are placed in front of each dog so they can choose from the three tastes. We
anticipate that each of the tastes will be selected by around 25 dogs, demonstrating the
dogs’ equal popularity.
After obtaining the results of your experiment, you want to determine whether the
distribution of the dogs’ taste preferences deviates considerably from what you had
anticipated using a chi-square goodness of fit test.
The degree to which a statistical model fits a collection of observations is shown by
the chi-square goodness of fit test. Genetic cross analysis is one of its common uses.
What is a goodness of fit test using chi-squares? A goodness of fit test for a categorical
variable is called a chi-square (𝜒2) goodness of fit test. How well a statistical model fits
a collection of observations is measured by its goodness of fit.
The values predicted by the model are in close proximity to the observed values
when the goodness of fit is high. The values predicted by the model are far from the
actual values when the quality of fit is poor. Distributions are the statistical models
that chi-square goodness of fit tests examine. A probability distribution with several
parameters can be as complex as a simple one with equal probability for every group.

Testing of hypotheses A hypothesis test is the chi-square goodness of fit test. It enables
you to infer from a sample what the population’s distribution is like. You may determine
whether the goodness of fit is ”good enough” to draw the conclusion that the population
follows the distribution by using the chi-square goodness of fit test.
A Poisson distribution of floods per year?
A normal distribution of bread prices?
Poisson distribution A discrete probability distribution is called a Poisson distribution.
It provides the likelihood that an event will occur a specific number of times (k) over a
specified period of time or area.
The Poisson distribution contains a single parameter, the mean number of occur-
rences 𝜆. Examples of Poisson distributions with various values of 𝜆 are displayed in
the figure 17
Fig. 17: Poisson-distribution
How the test statistic is calculated (formula) Pearson’s chi-square is the test statistic
for the chi-square (𝜒2) goodness of fit test.

Fig. 18: Poisson-formula
Normal Distribution Data has no skew and is symmetrically distributed when it
follows a normal distribution. The data has a bell-shaped distribution when shown on
a graph, with the majority of values gathering about the center and falling off as they
go out from it. Because of their structure, normal distributions are sometimes known as
Gaussian distributions or bell curves. See the figure 19
Fig. 19: Normal distribution-formula
The figure 20 shows a normal distribution.

Fig. 20: Normal distribution
3.3 Steady-state results
In general, the chi squared goodness of fit output was unsatisfying, as it rejected the
Null hypothesis even when the data has a larger sample and appears to fall within the
Normal distribution both on the histogram, as well as having a fairly constant gradient
on the Normal Distribution plot. The terminal output for the chi code has been copied
in at various points where the output varied - but even then it’s not always the relevant
data that’s being determined as being Gaussian (for the magnetometer steady-state, only
the gyroscope outputs rejected the Null hypothesis).
STM32 NUCLEO-F103RB MEMS sensors

Fig. 21: Steady-state for the LIS2MDL MEMS Magnetometer sensor.

Magnetometer (Figure: 23) We measured values at home. The bins are decided by
Sturgis’s rule. That means that we cannot increase the bins to get steady-state measure-
ments. In the steady-state measurements, we expected that most of the data would be in
one bin. In this example, that is not true. We have two bins where most of the data is. The
major bin with frequency (-2000 to -750) contains a major part of the data. This bin has
circa 13000 measurements. The second-largest bin has ca 10750 measurements, which
is quite large compared to the largest bin. This is a systematic error in the sensor. This
comes from the measurements, where the sensor was not stable. It was quite difficult
to get steady measurements. There are also random errors in the two smallest bins.
These are so few that we almost neglect them. Getting steady-state measurements with
the magnetometer sensor was impossible. The explanation of the random errors may
be measurement errors in the sensor or some errors in the way the measurements were
taken.
Figure 25 shows the measured steady-state interference data for the LSM6DSO
accelerometer/tilt test. The x-axis shows the Frequency of measurement and the y-axis
shows the Measured value.

Fig. 24: STM32 temperature steady-state.
Fig. 25: LSM6DSO MEMS IMU Accelerometer steady-state test, first repetition

Accelerometer, figure 25, shows a typical bell-shaped histogram. This means a his-
togram high around the expectation value frequency 67.5, and lower values further away
from the expectation value. We wanted a steady-state measurement with all measure-
ments in one bin, but that is not possible for this accelerometer. The 4 highest bins we
see as systematic errors and the lower measurement bins as random errors because they
are located scattered from the expectation value. The explanation of the random errors
may be measurement errors in the sensor or some errors in the way the measurements
were taken.
Fig. 26: Chi-squared terminal printout for disturbed steady-state. Rejecting the Null
hypothesis means the data does not conform to a Normal distribution.
accelerometer steady-state test. The x-axis shows the Frequency of measurement and
the y-axis shows the Measured value.

Fig. 27: LSM6DSO MEMS IMU Accelerometer test, second repetition
Accelerometer, second repetition in figure 27 shows an almost perfect bell-shaped
histogram. We see that the expectation value is at frequency 67.5 with 15000 measured
values with declining values when distancing from the expectation value. A steady state
measurement is all data in one bin, but that is impossible with the accelerometer sensor.
These are random errors in the sensor that always occur in this type of sensor. The
explanation of the random errors may be measurement errors in the sensor or some
errors in the way the measurements were taken.

steady-state test. The x-axis shows the Frequency of measurement and the y-axis shows
the Measured value.
Fig. 28: LSM6DSO MEMS IMU Accelerometer steady-state test
The third accelerometer in figure 28 steady state third repetition show the same as the
former tests: A bell-shaped histogram. It cannot give all the measurements in one bin.
There is scattered data around the expectation value at frequency 67.5. The maximum
value is 1350 measurements. Around the expectation value, there are values for random
errors. The explanation of the random errors may be measurement errors in the sensor
or some errors in the way the measurements were taken.

Fig. 29: Acceleration output (x-axis), disturbed steady state.
Fig. 30: Acceleration output (y-axis), disturbed steady state.

Fig. 31: Acceleration output (z-axis), disturbed steady state.

Quanser Board - Infrared Sensor
Figure 32 shows Infrared steady state interference data measured at the laboratory.
The x-axis shows the Frequency of measurement and the y-axis shows the Measured
value.
Fig. 32: Infrared steady state
The infrared sensor measurements in figure 32 show no central expected value but
two large bins measurements around ca 2600 and 2800 measurements at frequencies
0.61 and 0.63 but only a few measurements. Why it these two frequencies and not at
frequency 0.62 that is the largest can be seen as random errors and the measurements
could be redone to see if we had gotten the same result. We doubt that we would see
the same result. The explanation of the random errors may be measurement errors in
the sensor or some errors in the way the measurements were taken. Since there are no
measurements at frequency 0.62 it is probably random errors in the sensor that should
be calibrated or replaced. This is probably not a human random error.
Quanser Board - Magnetic Sensor
Figure 33 shows Magnetic steady state interference data measured at the laboratory.
value.

Fig. 33: Magnetic steady state
Magnetic steady-state in the histogram in figure 33 shows an almost perfect bell-
shaped curve. The expectation value is at frequency 2.2165 and has ca 2100 measure-
ments. This is a steady-state measurement where we expect values in one bin. This is
not so, and we conclude with random errors in the measurements. The explanation of
the random errors may be measurement errors in the sensor or some errors in the way
the measurements were taken. The sensor could be calibrated. The sensor should not be
replaced as the random errors most probably are measurement errors.

Fig. 34: Chi-squared terminal printout for Quanser magnetic steady-state. Rejecting the
Null hypothesis means the data does not conform to a Normal distribution. Unexpectedly,
it is only the gyroscope data here that conforms to a Normal distribution for all alpha
levels.
Quanser Board - Pressure Sensor
Figure 35 shows pressure steady state interference data measured at the laboratory.
value.

Fig. 35: Pressure steady state
The pressure steady-state histogram in the figure 35 shows scattered frequencies with
one higher with ca 265 measurements at frequency 1.9018 and ca 240 measurements
at 1.9016 with scattered measurements around the expectation value. The pressure
sensor has random errors that we can see in the histogram. The frequencies with the
lower number of measurements are not continuous but scattered. The explanation of
the random errors may be measurement errors in the sensor or some errors in the way
the measurements were taken. The syringe has not been pressured evenly or there are
random errors in the sensor. The sensor may be replaced or calibrated.
Quanser Board - Thermistor Sensor
Figure 36 shows thermistor steady state interference data measured at the laboratory.
value.

Fig. 36: Thermistor steady state
The thermistor steady state histogram in figure 36 shows a left-skewed histogram.
This is not even like a bell-shaped curve. Neither is the measurements in one bin. At
frequency -0.25 the highest measurement is at ca 320. The rest of the date is from 10 to
130 measurements. What we see is random errors in the measurement. The explanation
of the random errors may be measurement errors in the sensor or some errors in the way
the measurements were taken.
Quanser Board - Ultrasonic Sensor
Figure 37 shows ultrasonic steady-state interference data measured at the laboratory.
value.

Fig. 37: Ultrasonic steady state
The ultrasonic steady-state histogram in figure 37 shows a perfect steady-state mea-
surement with all the measured values in one bin. This is the result that we wanted to
see. There are no random or systematic errors.
Fig. 38: Chi-squared terminal printout for Quanser ultrasonic steady-state. Rejecting the
Null hypothesis means the data does not conform to a Normal distribution, meaning the
data here is perfectly Gaussian.
3.4 Disturbed steady-state results
Similarly as for the steady-state outputs, the chi-squared test did not return good re-
sponses and generally rejected that most of the data was normally distributed. A screen
capture of the terminal output has been included for where the output differed for at least
one of the examples.
STM32 NUCLEO-F103RB MEMS sensors

Fig. 39: Disturbed steady-state for the STTS751 Temperature sensor.
The temperature sensor in figure39 shows a histogram with 5 bins with measured
data. There is one bin with 4700 measurements at a frequency of little less than 21.1, a
bin with ca 3900 measurements with a frequency of a bit more than 21.1, and two bins
with around 2300 measurements at a frequency 21 and a bit less than 21.3. The last bin
is negligible. We see a scattered plot that is not a typical bell-shaped curve. This gives
random error measurements. The explanation of the random errors may be measurement
errors in the sensor or some errors in the way the measurements were taken.

Fig. 40: Screen capture from the Unicleo-GUI showing the real-time measurement
during the disturbed steady-state test for the LIS2MDL MEMS Magnetometer.
The magnetometer sensor diagram in figure 40 show one big spike at the frequency
ca -100 with ca 20000 measurements. The histogram is right-skewed with two bins to
the left at ca 3500 and 2000 at frequencies -1800 and -200. We say that the two bins to
the left are random errors. The explanation of the random errors may be measurement
errors in the sensor or some errors in the way the measurements were taken.

Fig. 41: Disturbed steady state for the LSM6DSO MEMS IMU Accelerometer sensor.

The disturbed steady-state accelerometer histogram sensor in figure 43 is nearly a
perfect bell-shaped histogram. The largest measurement is at frequency 50 with 7200
values. The second largest bin is at frequency 150 with ca 3200 measurements. There
are a few measurements in the bins to the left and right of the expectation value. The data
around maximum measurement are random errors. The explanation of the random errors
may be measurement errors in the sensor or some errors in the way the measurements
were taken.

Fig. 44: Disturbed steady-state for the STTS751 Temperature sensor.
The temperature sensor disturbed steady-state diagram in figure 44 is right-skewed
the expectation measurements at 2700 at frequency 27.5. We can see the random errors
from frequency 21.5 to 27 with measurements from 200 to 1000. The explanation of the
random errors may be measurement errors in the sensor or some errors in the way the
measurements were taken. We held our thumbs at the sensor to measure the temperature
from the thumbs. The random error may be that the thumbs were not in the exact position
to cover the sensor perfectly. It may also be random errors from the sensor. The sensor
could be calibrated or changed to give steady-state measurements.
Quanser Board - Infrared Sensor
Figure 45 shows Infrared interference data measured at the laboratory. The x-axis
shows the Frequency of measurement and the y-axis shows the Measured value.

Fig. 45: Disturbed Infrared interference
The disturbed steady-state infrared histogram in figure 45 is left-skewed with the
expectation frequency at 0.5 with ca 3700 measurements. To the right, we see the
random errors from frequency ca 0.75 to 4.25 with measurements from ca 100 to 500.
The explanation of the random errors may be measurement errors in the sensor or some
errors in the way the measurements were taken.
Quanser Board - Magnetic Sensor
Figure 46 shows disturbed magnetic data measured at the laboratory. The x-axis

Fig. 46: Disturbed magnetic interference
The disturbed magnetic steady state sensor in figure 46 is a slightly left-skewed
histogram. The expectation frequency is at ca 2.215 with measurements at ca 2750. We
see the random errors at both the left and right side of the expectation frequency. The
explanation of the random errors may be measurement errors in the sensor or some
errors in the way the measurements were taken. We held a battery close to the sensor to
get values to the histogram. The random errors can be explained by uncertainty in the
sensor. We had some problems getting good measurements with the battery. We used a
1.5-volt battery. We had to hold the battery very still to get good measurements. This is
the cause of the random errors measured. It may also be the sensor that did not respond
well to the battery. We had done other measurements with a larger battery that gave us
more data more easily. It may be a used battery that gave us poor data or it may be the
sensor that has to be changed or calibrated.
Quanser Board - Pressure Sensor
Figure 47 shows disturbed magnetic data measured at the laboratory. The x-axis

Fig. 47: Disturbed steady state pressure interference
The disturbed steady-state pressure sensor histogram in figure 47 shows an uneven
histogram. This is far from a bell-shaped histogram. The highest number of measure-
ments is ca 170 at a frequency of ca 1.6. The rest of the data is scattered and is typical of
random errors. The explanation of the random errors may be measurement errors in the
sensor or some errors in the way the measurements were taken. This can be explained
as follows: The syringe has probably been unevenly pressured so the data has been
scattered and with random errors.
Quanser Board - Thermistor Sensor
Figure 48 shows disturbed thermistor data measured at the laboratory. The x-axis

Fig. 48: Disturbed steady state thermistor interference
The disturbed state-state thermistor sensor in figure 48 has a close to bell-shaped
histogram except the expected frequency is left-skewed from the center and the measure-
ments in frequency 0.45 and 1.4 are 2 and 4 is lower than the surrounding measurements.
A perfect bell-shaped histogram has the expected frequency in the middle and has sur-
rounding decreasing measurements further from the expected frequency until it reaches
zero. The data that is not the expected frequency is called random error. The explanation
of the random errors may be measurement errors in the sensor or some errors in the way
the measurements were taken.
Quanser Board - Ultrasonic Sensor
Figure 49 shows disturbed ultrasonic data measured at the laboratory. The x-axis

Fig. 49: Disturbed steady state ultrasonic interference
The disturbed steady-state ultrasonic sensor measurements in the figure 49 had many
measurements compared with the rest of the data. The frequency at ca 2.75 is ca 3500
measurements. The uttermost left frequency 0.25 has ca 1250 measurements. The rest
is 250 and less. This is very clearly a right-skewed for the expected value with the
rest of the measurements left-skewed. This is a systematic error since the left-skewed
measurements are not connected to the right-skewed expected frequency. The reason
for these systematic errors was that the ultrasonic sensor did not respond very well to
our measurements. When we held our hand over the ultrasonic sensor it was responding
better than other measurements. This means that the sensor can be calibrated or changed
to a working sensor.
3.5 STM32 HAR results
The collected data from the various activities for HAR are provided separately to this
report.
4 Analysis
4.1 Analysis of the measurement results
The resulting measurements generally did not fully provide the expected results. For the
disturbed steady-state, it was expected to receive a largely uninformative distribution of
data given that we provided various disturbances at unpredictable intervals. So for that
section, we could clearly see an uneven distribution as expected. The magnetic sensor’s

disturbed steady state was largely unaffected by the motion of the battery, and only tiny
specks of noise could be sometimes observed during the measurement process as well.
This seems to be reflected by the fact that it was the disturbed steady-state result that
most closely resembled its ’undisturbed’ counterpart, representing systematic errors.
However, for the steady-state results, we generally expected to find a histogram
distribution resembling that of the normal distribution curve, as these are random errors.
The closest results we had to this were the 3rd steady state measurement of the STM32-
Nucleo board, as shown in Figure 27, and the Quanser magnetic sensor, shown in Figure
33. But even these cannot truly be said to be normally distributed as they are not fully
symmetric, and so we can only say it indicates the presence of normal distribution
or truly random errors. The chi-squared method did not really yield desireable results
for comparison to Gaussian data, even if the Normal plot seemed to have a sufficient
gradient for these cases.
4.2 Analysis of the process of finding sensor characteristics through reading
data sheets
Determining sensor characteristics from datasheets was not always a straight-forward
task. Different key words are used, and these characteristics can often depend on the
digital architecture of the sensor and thus some understanding of the various bits and
registers that are in use by the sensors are very helpful to understanding the full breadth
of limitations present in its operation. Certain other characteristics, such as the sensor
order, didn’t seem to appear directly in the datasheets for the sensors that were of 0th
order, but for 1st order sensors we could find the given time constant.
5 Conclusion
Overall, the project was successful in demonstrating the presence of various noise
experienced during measurements, and also somewhat demonstrated the adherence of
random errors (from noise in steady-state measurements) to the normal distribution
curve. This distribution curve could maybe resemble the normal distribution more
closely if we had taken a lot more measurements (i.e. over a significantly longer duration),
or by better controlling the environmental factors at play during the steady-state (we
measured either at the laboratory or the library, both are places which can have occasional
traffic/bypassers that could cause an environmental disturbance through motion).
Additionally, some of the sensors (particularly the ultrasonic sensor) may have not
been functioning fully as intended, perhaps due to exposure to many students over the
duration of the course, as some may be rougher on the equipment than others.
We also learned the meanings of the characteristics a lot more in-depth when we had
to find them in data sheets and break down the different parts, as well as getting a closer
look at the digital aspect of customizing/configuring the operating mechanics/ranges of
sensors by setting specific bits.
References
1. Quanser sensor user manual. QNET Mechatronic Sensors

2. STMicroelectronics: LIS2DW12 datasheet
3. STMicroelectronics: LSM6DSO datasheet
4. STMicroelectronics: HTS221 datasheet
5. STMicroelectronics: STTS751 datasheet
6. STMicroelectronics: LIS2MDL datasheet

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