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DESIGNING A SYSTEM TO DETECT MOTIONLESSNESS USING TILT SENSOR

Lee Yu Hao

1, Arun Shankar Narayanan

2, Tan Kok Kiong

2

1NUS High School of Mathematics and Science, 20 Clementi Avenue 1, Singapore 129957

2National University of Singapore, 4 Engineering Drive 3, Singapore 117583

ABSTRACT

There has been an increasing need to come up with assistive technology to aid the elderly of

today. With the gradual rise of the number of people living alone in the last 10 years, there

has been an increasing need for technology regarding detection when someone needs help.

With regards to this, this research aims to develop a reliable and cheap method to produce a

system that can easily detect when someone is motionless, with simply utilising an Arduino

breadboard and the various kinds of sensors that are easily accessible. This was realised by

coming up with a sketch on the Arduino software and the most optimal code that would

achieve the most effective result. Tilt sensors, as well as other motion sensors like

accelerometers and gyroscopes were used and their effectiveness was tested based on various

experiments regarding the optimal placement position as well as the feasibility of the

solution. The results have shown that these sensors are mostly effective in detecting

motionlessness. Thus, an extremely cheap method for detecting motionlessness has been

developed, and can be further extended to be implemented for real time use.

INTRODUCTION

The amount of elderly around the world has been rapidly increasing over the last few

decades, and with that more people are living alone, increasing with an average of around

10% for the past 10 years [1]

. Thus, there has been a rising need to develop cheap and viable

technology that could effectively track when someone is motionless due to injury such as

falling or sudden problems such as fainting. Funding in this aspect has increased in many

countries, such as Singapore who has invested more in this area and held a conference known

as the SiCEX during 10th

to 13th

January 2008. [2]

This kind of technology would not only

prove useful to such people by giving them reassurance that they are being taken care of, but

also cater to the increasing amount of elderly. This can even be modified for other purposes

such as monitoring the trainee soldiers when they are engaged in open field training so that

the trainer would be notified through alarm when a trainee passes out. With motionless due to

falling making up the highest proportion of the dangers elderly face [3]

, and even though some

people may not be injured after falling, a large percentage of almost 47% people cannot get

up without assistance and remain motionless which can lead to problems such as dehydration,

pressure sores or even pneumonia. [9]

Thus, there needs to be a solution to track when the

elderly are immobile due to such accidents.

In this research a method of tracking motionlessness was experimented upon. Current

detection methods use three approaches: wearable devices, camera-based devices as well as

ambience devices [2]

, each with their advantages and disadvantages. However, even though

camera-based devices are increasingly being used, they are still relatively expensive [4]

, and

ambience devices suffer from the fact that it has a high rate of false alarm as the pressure it

senses may not be due to the user’s weight. [2]

Thus, this project aims to design a cheap and

accurate wearable device that is relatively accessible and easy to use, by using the fact that

the person’s posture will not change when immobile on the floor and thus the tilt sensors’

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status will remain constant, triggering an alarm after more than a period of relative constant

status of around approximately 2 minutes, providing reliable response when needed. By

testing normal tilt sensors, accelerometers and gyroscopes, we will see which one of these is

most suited to be used in a wearable device to detect motionlessness.

This paper will then discuss how Arduino is used to code the program, followed by the types

of tilt sensors experimented in this research and the steps required to configure them, after

which we discuss the results with regards to the accuracy of it detecting motionlessness, its

feasibility as well as comparing with other devices currently in the market, before concluding

by evaluating the systems done in this project and any future research.

METHOD AND MATERIALS

Arduino

The Arduino board is a mini computer that can read sensor measurements and generate

output voltages in order to trigger an alarm or turn on/off certain actuator. The Arduino

software is open-sourced and very easy to write code with and upload it onto the board. Since

it runs on any computer software (Windows, Mac OS X and Linux), it allows for quick and

easy prototyping, being able to easily test various ideas conveniently without using much

resources. With it being highly accessible, this becomes a great tool that can be used for this

research [9]

.

By using such an embedded system, the sensors will be able to send information to the

microprocessor that reacts to this input system based on the program written, providing an

output system that is put into action via actuators. [5]

Types of sensors tested

Three types of sensors were used in this research: the normal tilt sensor, the accelerometer,

and the gyroscope. All 3 of these sensors can be used to detect motion status and thus

measure when the orientation remains the same which is when the person is deemed as

motionless. However, they all work differently, as explained below.

Normal tilt sensor

The normal tilt sensor measures the orientation of a system or the revolution of the object

with respect to the gravitational field. [6]

Sometimes known as inclinometers, they are simple

and thus used in a wide range of products and appliances. They have a cylindrical cavity

containing a metallic rolling ball inside, and when it makes contact with 2 conductive poles

found on one of the ends of the cavity at a certain angle, it will close the contacts and form a

circuit. When the ball and the poles are not in contact, the state will be known as LOW on

Arduino and when they are in contact, it will be known as HIGH which occurs when the tilt

sensors’ legs are pointing downwards. Thus when a person is in motion, the Arduino will

oscillate between LOW and HIGH status. However, once a person is motionless or falls due

to injury, it will remain either as LOW or HIGH status constantly, and once it remains in the

same status for a period of time it would trigger an alarm.

To configure the tilt sensor, firstly connect it to the breadboard with a 10kΩ resistor, and

followed by connecting 1 LED with a 150Ω resistor in parallel with each other, with it

lighting up when the state of the tilt sensor is LOW and not lighting up when it is HIGH.

Finally, a speaker is connected in parallel with a 100Ω resistor and would send an alarm

when the tilt sensor remains in the LOW status (LED switched on) for more than a period of

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time. In the HIGH status (LED switched off), the speaker will sound and should be noticed

when it sounds for an extended period of time, it is a signal that it is in the HIGH status for

more than a certain period of time depending on what was set, and thus action should be

taken in either of these circumstances. The set-up is shown in Figure 1.

Figure 1: Image of Arduino breadboard with tilt sensor in HIGH state (left) and LOW

state (right)

After which, there is a need to come up with a sketch for the Arduino that can execute this

program. It uses the fact that once the status remains constant for a long period of time, the

timer will continue running and once the status changes the timer will reset, and once the

timer exceeds a certain amount an alarm will be triggered. Please refer to Appendix Figure

A1 for the program code.

Accelerometer with an in-built gyroscope

A MPU-9150 board was used, as a 9 degree of freedom inertial measurement unit. With a 3-

axis accelerometer and a 3-axis gyroscope testing linear acceleration as well as angular speed

respectively [7]

, both instruments would be used to test when the person remains motionless,

which occurs when the values do not oscillate and remains relatively constant. (Refer to

Figure 2 for an image of it).

Figure 2: Image of MPU-9150 (accelerometer and gyroscope) board attached to

Arduino breadboard

The accelerometer is sensitive to changes caused by movement, and uses various tiny crystals

that generate voltage when a force is applied on it, which translates into the reading on the

accelerometer.

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For a gyroscope, it uses gravitational acceleration to determine the object’s orientation with

respect to the Earth. [8]

With a rotor that is fixed in place within a bigger wheel on a spinning

axis, the rotor will be able to detect the orientation with the help of gravity when the axis

starts to spin. By measuring angular velocity, it can tell the orientation and motion of the

object is attached to simply if the values deviate by a huge amount.

These 2 tilt sensors would be used to test for the orientation as well, and the fact that they are

on the same board makes it easier to execute the testing of these two sensors. (Refer to the

sketch used to obtain readings in Figure A2 of appendix). To test for motionlessness, we have

to rely on the fact that accelerometer and gyroscope readings in the x, y and z axis would

remain relatively constant due to the lack of motion.

Tests done on each of these sensors

Once they were successfully configured, tests were run on each of the tilt sensors to test their

accuracy. Various actions (remaining motionless, walking, sitting, squatting) which are

actions the elderly usually do were done randomly with each being done over a time interval

of approximately 2 minutes, and the sensors were attached to various places (ankles, hips as

well as arms). (Refer to Figure 3 in the appendix for an image on how it was attached).

However, since most of these sensors are relatively flexible and can be placed in various

places with similar result, we would be testing more of the accuracy of the tilt sensors, with

20 test runs (of 1 random action being done each time) and the results tabulated.

For the MPU-9150 board, accelerometer and gyroscope values would be obtained, and would

be programmed on Arduino IDE to be able to distinguish motion from motionlessness based

on a threshold value (maximum-minimum being greater than a certain amount, refer to

figure A3 for the sketch). After which, it would undergo a similar test as the tilt sensor and

their accuracy would be tested with 20 test runs (of 1 random action being done each time)

and the results tabulated.

RESULTS AND DISCUSSION

Tilt sensor

Each result is expressed as a percentage, as a success rate for how effectively tilt sensors can

detect motionlessness. The results are tabulated below.

Table 1: Table of tilt sensors’ effectiveness under different conditions

Areas tilt sensor was placed Effectiveness in determining motionlessness

with random actions done (%)

Ankle 65

Arm 60

Hip 80

Clearly, from the results we can see that hips provide the most accurate result followed by the

ankles and arms at a similar number. This is probably due to how putting the sensor at the hip

is relatively close to the centre of mass of the body, which represents one’s overall motion

and thus is the most sensitive in changes when there is motion, changing the state of the tilt

sensor and resetting the timer. On the other hand, for arms and ankles, they represent the

movement of only that body part, so if one was sitting and only moving their arms when the

sensor is attached to the legs, it would have almost no effect on it and in some cases resulting

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in the tilt sensor remaining in the same state and thus showing a supposedly motionless state

when the person is not.

It was noticed that it is easily able to detect when someone is walking (due to the constant

change of state of the tilt sensor), with the ball constantly oscillating between touching and

not touching the poles. Thus, the tilt sensor is easily able to detect these actions, and would

not matter where it is placed on the body so long as it is at a place where any kind of

movement can be sensed.

However, when it comes to actions such as sitting or squatting, it becomes a bit different

since these actions do not have much movement involved but yet cannot be considered as

completely motionless to raise an alarm. Regardless, the tilt sensor did not have a 100%

success rate regardless of where it was placed on the body, and this can easily be explained

by the fact that if the movement made or done is not significant enough to move the ball

away from the contacts and vice versa, the state of the tilt sensor would not change, causing

an alarm to be raised even when the person is not completely motionless and in need of help.

Secondly, even if the tilt sensor was slightly more sensitive to movement, the problem faced

during the experiments was that the tilt sensors’ legs have to be faced downwards constantly

for it to work as the poles are placed below within the tilt sensor, so the ball will only be in

contact with the poles at that orientation. Once the tilt sensor is oriented in other ways, any

movement will not cause any change as the ball will not be in contact with the poles no

matter how the tilt sensor is shaken as the poles only occupy the small bottom area in the

sensor. This results in inaccuracies where an alarm is sounded even though the ball inside the

tilt sensor is moving rigorously due to motion.

As a whole, tilt sensors are seen to be relatively easy to operate and are inexpensive to use,

providing one with details on whether the wearer is motionless or not, but as a trade-off for

inexpensiveness, it comes with several accuracy issues such as the fact that relatively small

actions will not cause a change in state in the tilt sensor, combined with the fact that the tilt

sensor’s legs has to face downwards and that it is relatively bulky to be wearing the entire

Arduino breadboard, makes it a challenge to not only make sure it can detect motionlessness

effectively, but also get people to wear these. A further development into tilt sensors and their

compatibility with Arduino, as well as reducing the breadboard size would significantly

reduce the problem.

Accelerometer and gyroscope

The MPU-9150 board went through various actions (ie: completely motionless, sitting,

squatting as well as walking), and graphs of the readings on the accelerometer as well as the

gyroscope were plotted. The raw data is found in Table A1 in the appendix.

-500

0

500

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57

Gyroscope no movement

X-axis Y-axis Z-axis

-50000

0

50000

1 5 9 131721252933374145495357

Accelerometer no movement

X-axis Y-axis Z-axis

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Figure 4: Graph of gyroscope and accelerometer readings in the X, Y and Z axes as a

function of time in seconds in different conditions

Firstly, let us analyse the gyroscope’s readings. It has the largest deviation for more active

motions like walking, followed by sitting then squatting, with completely being motionless

having the lowest deviation. This is explained by the fact that when one is completely

motionless one has zero angular velocity, and the slight deviation in readings is caused by air

resistance and other measurement errors caused by the gyroscope. However, when one is

even in slight motion, this causes the object to have a slight angular velocity in either of the x,

y or z axis, and that change in orientation causes changes in angular velocity which results in

a huge deviation, with bigger actions such as walking having greater deviation than those of

smaller actions like simply sitting or squatting. We calculate from the data that the threshold

for being considered as being motionless is having a maximum deviation of around less than

4500 for the x-axis, less than 7600 for the y-axis and less than 1800 for the z-axis in terms of

degrees per second. The gyroscope is relatively accurate in determining one’s motion and

orientation, and thus would be the most useful in determining one’s motionlessness based on

the deviation over a period of time not exceeding the amounts above. By using the sketch in

figure 6(the sketch only shows one kind of axis but it can be replicated for the others) to test

if it can detect motionlessness, it was relatively effective as shown below at all 3 areas that it

was attached to. With random actions (sitting, squatting, walking or motionlessness) being

done and the sensor having approximately a 98% success rate of distinguishing motion from

motionlessness by using the threshold value and resetting it after a time interval, we can see

that the gyroscope is the most effective tool in detecting motionlessness.

-20000

0

20000

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57

Gyroscope walking

X-axis Y-axis Z-axis

-20000

0

20000

40000

1 5 9 131721252933374145495357

Accelerometer walking

X-axis Y-axis Z-axis

-10000

-5000

0

5000

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57

Gyroscope sitting

Sitting Sitting Sitting

-20000

0

20000

1 5 9 131721252933374145495357

Accelerometer sitting

X-axis Y-axis Z-axis

-5000

0

5000

10000

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57

Gyroscope squatting

Squatting Squatting Squatting

-10000

0

10000

20000

1 5 9 131721252933374145495357

Accelerometer squatting

X-axis Y-axis Z-axis

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Table 2: Table of gyroscope’s effectiveness under different conditions

Areas gyroscope was placed at Effectiveness in determining motionlessness

with random actions done (%)

Ankle 100

Arm 95

Hip 100

As for the accelerometer, there will definitely be a greater deviation with a greater

acceleration for bigger actions such as walking, as acceleration = force/mass and with the

accelerometer having such a low mass, forces on the object will cause an apparent change in

acceleration. From the graphs above, we can see that walking causes a very obvious deviation

in acceleration, but however sitting and squatting seems to have relatively similar graphs to

being completely motionless. This makes it harder to detect motionlessness as these graphs

are relatively the same making it difficult to distinguish between what is happening at that

point in time. A probable reason to why this is so is the fact that in actions such as sitting and

squatting, we tend to move our body parts only very slightly in a gradual movement as

rotations, and accelerometers cannot sense rotation whereas gyroscopes can, since

accelerometers cannot distinguish the acceleration from movement and acceleration from

gravity. Since the accelerometer is only able to measure linear acceleration, thus rotations

from sitting or squatting cannot be measured accurately and thus results from those will be

similar to that of being motionless as seen above. From the results below, they are clearly not

as effective as the gyroscope at detecting motionlessness, even with a threshold value for

acceleration x-axis at 2670, y-axis at 3240, and z-axis at 1310 in m/s2. Since this measures

acceleration and not angular velocity, a different sketch is required to effectively reset the

maximum-minimum values since there is always gravitational acceleration present. (Part

different from sketch A3 found in figure A4 in the appendix).

Table 3: Table of accelerometer’s effectiveness under different conditions

Areas accelerometer was placed at Effectiveness in determining motionlessness

with random actions done (%)

Ankle 80

Hip 80

Arm 75

From this report we can compare the differences between the accelerometer, gyroscopes and

the tilt sensor. Tilt sensors are definitely less accurate compared to accelerometers and

gyroscopes as they provide mathematical data which can be analysed, as opposed to the

normal tilt sensors which rely on a make or break contact mechanism that would work for

drastic actions, but for smaller actions they would simply have no effect on changing the state

of the tilt sensor. Nevertheless, even though tilt sensors are not as precise and accurate as

accelerometers and gyroscopes, it is relatively able to detect changes in motion and

orientation, and able to sense motionlessness but with the problem that it is relatively

inaccurate at times, as small changes in motion are not able to change the state of the tilt

sensor and thus giving an inaccurate result. However, they are relatively cheap, inexpensive

as well as easy to operate, with almost no calculations required to determine if someone is

really motionless or not.

Accelerometers and gyroscopes present a more accurate analysis on whether the object or

person in question is really motionless or not, however they require a mathematical analysis

of the data given and are relatively more expensive than a typical tilt sensor. However, with

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such sensors becoming more readily available, they have begun to take their place over the

normal tilt sensor. Furthermore, since gyroscopes are able to detect rotation with angular

velocity whereas accelerometers cannot, they seem to be the most suited device for detecting

motionlessness.

However, these devices do not come with their problems. Tilt sensors as mentioned earlier

are not sensitive enough, but even sensors like the accelerometers and gyroscopes are not

perfect, with slight fluctuations in the data even when motionless caused due to problems

such as air resistance as well as precision errors with the MPU-9150 device. Furthermore,

taking the maximum minus the minimum value across a time period as the deviation may not

be the most suitable method. Thus, maybe this can be investigated further into and tested to

see if there are better ways to accurately detect motionlessness. Lastly, the results above may

not be entirely accurate due to the unreliability of some devices like the tilt sensor, and thus

this experiment should be done even more times to obtain a reliable range of results.

Nevertheless, we can see that for a wearable device, accuracy still forms the most important

component, and thus even though gyroscopes are slightly more expensive and require in

depth analysis of the mathematical results obtained, but it still can effectively differentiate

motion from motionlessness, and thus in this case would be considered to be the most suited

for a wearable device.

CONCLUSION

This research paper shows the potential to use such tilt sensors to detect motionlessness.

With various tilt sensors tested and their advantages and disadvantages presented, it provides

data required to take this research to the next step and see how feasible it is to be

implemented in real life. Not only would this be useful to detect elderly living alone that

might be motionless due to injuries, it can also be used for other purposes such as for soldiers

who are motionlessness in combat trainings. In terms of cost, these products are also

relatively cheap and accessible, which thus presents a possibility of using these in the real

world, especially the gyroscope which according to the data provided gives the most accurate

result on detecting motionlessness.

Overall, this research widened the application of such tilt sensors in various purposes, not

simply just used for stabilizing objects via orientation but can also be used to save lives as a

whole by detecting motionlessness with relatively cheap and accessible devices, and this is

something which can be looked into in the future.

RECOMMENDATIONS FOR FUTURE WORK

Even though this research has suggested possible implementations for tilt sensors, there are

many other things that can be improved to make it viable in the near future.

Feasibility

Such devices have to be investigated into to make them convenient to wear, as if the

breadboard was simply too big it would make it extremely difficult for people to be willing to

use such devices even if it improved their safety. In order to make it useable, the system has

to be converted to a much smaller form factor embedded system so that it can be

implemented as actual detection devices that are wearable by the user.

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Accuracy

Tilt sensors have a decent amount of accuracy but they need to be improved to almost

perfection if they want to be implemented. The normal tilt sensor has to be modified in such a

way that it can change state in motion regardless of how it is oriented, and not simply work

only when the legs of the tilt sensor are facing downwards. Furthermore, the other two

sensors, the accelerometer and the gyroscope may need to be further researched into to see

where the threshold deviation is before it can be considered as completely motionless, or to

see if simply taking the minimum value from the maximum value as the threshold value is the

most accurate way or if there are better ways or algorithms to accurate separate motion from

motionless objects.

Detection of other injuries

Other than detecting motionlessness due to injury, these sensors could be used to detect other

injuries such as major falls or other physical injuries based on identifying suspicious

activities that happen during injuries such as a slower acceleration reading on the

accelerometer, lower gyroscope readings than usual etc. This can be investigated further into

to see how these sensors will fare in different kinds of injuries.

Quality and quantity of data

Due to lack of time during the research, a limited amount of data could only be obtained.

Thus, if possible one could try to obtain a larger amount of results from each of the sensors.

Also, it is important to see if taking the threshold value for the accelerometer and gyroscope

by taking the maximum minus the minimum value is considered the best option for tracking

motionlessness.

ACKNOWLEDGEMENTS

1. Mentor Dr Tan Kok Kiong for giving me the opportunity to do the project.

2. Engineer Arun Shankar Narayanan for supervising my project

3. My family and friends for their support and encouragement throughout the project

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REFERENCES 1. Edward Malnick.2014. “10 per cent rise in number of people living alone” The Telegraph

2. Yu Xinguo.2008. “Approaches and Principles of Fall Detection for Elderly and Patient”.

3. Stefano Abbate, Marco Avvenuti, Paolo Corsini, Janet Light and Alessio Vecchio (2010).

Monitoring of Human Movements for Fall Detection and Activities Recognition in

Elderly Care Using Wireless Sensor Network: a Survey, Wireless Sensor Networks:

Application-Centric Design, Yen Kheng Tan (Ed.), ISBN: 978-953-307-321- 7, InTech,

4. Frank G. Miskelly.2001. “Assistive technology in elderly care”.p458.

5. Choo Wai Heng. 2013. C520A embedded system. p11-14.

6. Subir Das. 2014. “A Simple, Low Cost Optical Tilt Sensor”. International Journal of

Electronics and Electrical Engineering Vol.2, No.3.

7. Oliver J. Woodman. 2014. An introduction to inertial navigation.

8. http://www.livescience.com/40103-accelerometer-vs-gyroscope.html.

9. “How often falls occur” http://www.learnnottofall.com/content/fall-facts/how-often.jsp.

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APPENDIX

Figure A1: Sketch of Arduino IDE for the tilt sensor

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Figure A2: Sketch of MPU-9150(accelerometer and gyroscope) board in Ardiuno IDE

Figure 3: Image of tilt sensor and Arduino breadboard being attached to waist (top

left), ankle (top right) and arm (bottom)

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14

Figure A3: Sketch of MPU-9150 board (gyroscope) on Arduino IDE checking whether

the deviation exceeds the threshold value

15

Figure A4: Deviation of sketch from A3(void loop part) of MPU-9150 board

(accelerometer) on Arduino IDE checking whether the deviation exceeds the threshold

value

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Table A1: Table of raw data from accelerometer and gyroscope readings under

different conditions

Motionless

Gyroscope

(deg/s)

Accelerometer

(m/s2)

X-axis Y-axis Z-axis X-axis Y-axis Z-axis

-20 22 -55 -1500 164 17092

-45 18 -52 -1472 64 17072

-39 4 -48 -1404 48 16960

-28 42 -50 -1528 64 17064

-45 8 -43 -1380 56 16744

-58 19 -41 -1308 112 16996

-31 17 -31 -1456 -132 17084

-33 2 -65 -1468 176 16976

-65 -12 -43 -1568 88 17004

-27 35 -31 -1468 156 16936

-72 20 -40 -1440 172 16884

-68 27 -23 -1516 56 16984

-42 24 -44 -1432 228 16908

-34 7 -31 -1432 124 17104

-53 51 -44 -1448 100 16944

-49 16 -26 -1396 100 17004

-76 41 -43 -1400 -64 17144

-60 36 -49 -1408 64 17068

-55 56 -42 -1440 -104 16992

-27 247 -59 -1412 248 17008

-41 38 -42 -1496 64 16920

-12 36 -48 -1392 4 16840

-43 29 -38 -1408 108 16988

-68 18 -26 -1476 164 17144

-44 28 -40 -1472 220 16896

-48 3 -49 -1440 52 17136

-49 -243 -47 -1360 -44 16848

-77 17 -50 -1336 160 16968

-54 29 -38 -1440 72 16924

-55 29 -36 -1448 100 16804

-72 -250 -62 -1528 216 16908

-82 -7 -70 -1384 36 16964

-55 8 -45 -1440 88 17060

-44 4 -59 -1476 76 17096

-59 1 -75 -1468 68 17108

-22 47 -36 -1464 32 17060

-31 11 -41 -1404 8 16916

-26 29 -47 -1464 80 17140

17

Motionless

Gyroscope

(deg/s)

Accelerometer

(m/s2)

-45 12 -62 -1512 188 17004

-58 20 -46 -1508 -28 16924

-52 29 -39 -1460 88 17092

-54 40 -51 -1392 172 16940

-61 38 -56 -1324 0 16932

-48 1 -34 -1540 172 16952

-43 7 -24 -1360 148 16992

-54 41 -44 -1448 108 17096

-52 14 -38 -1520 200 17084

-45 15 -51 -1448 4 17060

-53 22 -36 -1448 -132 17128

-47 38 -37 -1472 8 16992

-3 13 -69 -1432 -248 16992

-70 9 -38 -1440 104 16856

-38 31 -52 -1388 108 17096

-34 34 -54 -1452 44 17092

-68 28 -47 -1416 36 16876

-41 43 -49 -1440 48 16900

-50 49 -51 -1420 228 17324

-43 -1 -247 -1436 64 17036

-64 15 -69 -1308 -48 17004

-21 4 -38 -1436 52 17040

Walking

Gyroscope

(deg/s)

Accelerometer(m/s2)

X-axis Y-axis Z-axis X-axis Y-axis Z-axis

-15977 8383 -10376 -204 -5124 8060

-1793 13172 -2738 -2584 -5264 20088

-1293 -4816 -7334 -2088 -4244 15108

3952 -175 7731 -1172 -2600 14988

-2419 -4487 11844 -1112 -744 15372

2822 -467 12710 2720 796 14548

-10044 11167 -3639 -1412 -156 16812

16857 -2688 4893 -7688 -2444 13912

16109 984 -227 -2768 5172 17252

-6753 1338 -2438 -6172 3732 17008

-13891 1369 -2723 -3620 864 14392

10256 286 -2116 -5516 1068 14932

9888 -1944 -3131 -6948 4776 17364

-9465 -2845 -7377 -3360 2904 17516

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-6801 -1474 -8001 -2072 -1624 16628

5878 -1271 -4230 -4152 -1172 15844

3224 68 -1551 -4632 80 16720

-821 898 1006 -3676 412 16432

352 1548 4791 -4016 612 16316

911 2694 8628 -4536 1332 16472

-499 2698 7153 -6628 3308 15624

435 722 5679 -5644 3184 15172

270 1656 2952 -6596 5916 15696

-2677 810 5210 -6796 3032 14248

-4742 1255 4212 -6648 2824 16340

-4339 601 468 -6156 -352 15812

5086 -604 462 -7664 1808 15276

-87 -245 -119 -6500 3936 15464

-4015 88 -1492 -5120 2276 15500

1434 284 -1708 -6204 2248 15708

-1266 -93 -3166 -7128 2152 15472

-2737 -798 -5033 -6148 1224 16128

-1543 -1732 -5248 -5648 156 15568

-886 278 -2157 -6340 -1364 16400

-1193 1083 -483 -3536 -1936 15596

3970 1496 1274 -5508 -1976 15532

7405 1859 977 -4652 580 16784

1790 765 -10 -4252 1780 16888

-459 140 -994 -5660 996 16580

-1146 -152 -871 -5964 1116 15556

-363 372 188 -6716 940 15608

-1089 453 912 -6740 904 16448

-2707 -132 1905 -5480 364 15584

-13 -82 3495 -6580 -64 15404

2214 1006 5021 -7968 1272 14056

2502 1060 1683 -9356 2264 16072

50 -177 161 -5072 4232 14924

-4450 -483 -1960 -7128 3668 15584

-438 -1347 -1465 -6200 2816 14880

-4788 -1622 -2198 -6280 1720 16140

-6039 -743 -2558 -5972 -1480 15476

-294 1390 -713 -7824 -2364 15868

21 453 468 -5628 -2472 15628

1122 382 3714 -5720 -2296 15644

6245 675 5594 -6752 -516 15052

8852 3103 8578 -8232 4020 15284

-5443 -263 1797 -7460 4996 16492

65 -1601 2335 -5124 3104 13680

19

Sitting

Gyroscope

(deg/s)

Accelerometer

(m/s2)

X-axis Y-axis Z-axis X-axis Y-axis Z-axis

99 204 190 -1396 4120 16424

-107 113 -255 -2280 4284 16312

-193 -32 -73 -1856 4380 16332

125 295 174 -1940 4360 16216

18 161 101 -1980 4520 16392

-127 13 -22 -2236 4524 16140

-120 14 -96 -1852 4536 16188

-145 87 -102 -1936 4424 16620

-103 38 -105 -2196 4252 16276

-54 -253 -276 -2064 4260 16120

-182 -124 -304 -1980 4416 16384

101 89 -224 -2192 4472 16312

-220 -92 -291 -2000 4408 16444

-47 27 47 -1888 4564 16240

104 148 -49 -1876 4452 16236

-137 -26 -36 -1992 4580 16264

-36 77 24 -1960 4748 16264

-48 28 -57 -1964 4364 16356

-53 -52 -222 -2096 4292 16344

-104 -66 -175 -1932 4408 16228

-251 -107 -21 -1976 4644 16408

15 132 189 -2004 4516 16280

-80 74 49 -2196 4516 16356

-169 -28 -80 -1988 4340 16320

-79 -12 -18 -1956 4572 16276

87 118 95 -2012 4464 16544

258 179 167 -2004 4580 16360

-254 -237 -246 -2060 4392 16232

682 -859 -1200 -2184 4496 16396

530 -259 -972 -1648 5188 15792

-404 -558 -1003 -1556 4564 16428

442 62 99 -1500 4932 15956

-625 -717 -456 -1740 4148 16372

473 190 -5 -1856 4552 16176

-56 -236 -291 -1652 4852 16060

70 140 -113 -1600 5068 16276

81 273 58 -1736 5052 16196

-4131 -1597 -1670 -7712 4276 15360

-5873 -1747 -3977 -7056 1684 14824

-3614 70 -5391 -6684 -216 16776

20

Sitting

Gyroscope

(deg/s)

Accelerometer

(m/s2)

-334 17 -116 -2060 4668 16212

169 92 10 -1912 4832 16048

-57 74 -200 -2048 4764 16136

160 108 -15 -1648 4976 16148

-65 -145 -321 -1792 4948 16132

-257 -69 -162 -1884 4880 16300

-107 156 -69 -1736 4844 16320

-114 -255 -155 -1752 4652 16256

-38 101 -212 -2044 5108 15964

38 -39 -139 -1940 5000 16140

-243 -161 -347 -2048 5180 16512

-1995 -2830 -689 -2700 5504 17048

-4638 -5564 -1676 -488 4560 16620

-2798 -1318 62 2428 1756 16340

242 2040 -197 220 2452 16544

-239 532 -259 -728 3108 16884

487 639 66 76 2424 16340

1280 666 -380 -156 2868 16760

856 567 -23 -636 3460 16640

335 -201 -359 -460 3104 16736

764 286 28 -408 3700 16748

562 115 141 -556 3696 16744

736 318 -4 -620 4076 16828

Squatting

Gyroscope

(rad/s)

Accelerometer

(m/s2)

X-axis Y-axis Z-axis X-axis Y-axis Z-axis

-204 -236 126 -984 4460 16304

256 62 75 -1360 4644 16452

496 219 18 -1324 4964 15880

20 93 -324 -1512 4956 16232

-387 -193 -133 -992 4348 16624

-315 -3 -86 -1000 4672 16328

3 167 -42 -1220 4648 16384

-357 330 -92 -784 4212 16216

222 1085 -31 -1680 3572 16676

-290 -1570 540 -1156 5300 15844

-371 732 -580 -1464 4684 16500

462 -535 138 -1168 4780 16280

230 141 9 -1260 4704 16424

-681 112 -485 -1888 5100 16044

21

Squatting

Gyroscope

(rad/s)

Accelerometer

(m/s2)

-903 -1420 -747 -1456 5456 15672

-1674 -1128 802 -960 4940 16464

1098 5924 -1096 -2012 3816 15636

2659 -2451 1716 -808 4416 16416

-717 1747 -322 -1008 4360 16932

-280 -1538 435 -1768 4660 16392

-1195 446 -527 -2276 5448 16248

-112 -89 -491 -616 4292 16188

1157 777 40 -1724 4776 16416

-99 -379 126 -1660 5136 16076

-11 -95 -138 -1332 5072 16348

-484 -1106 89 -1344 5332 16172

400 19 -253 -432 4292 16256

574 756 17 -1744 5816 16044

-173 418 -139 -1568 4752 16196

-442 -1258 -58 -1652 5168 16068

-1174 281 -483 -1024 4820 16396

480 -172 -147 -1116 4220 17464

1920 15 1678 -344 3352 17460

622 1227 -784 -2232 6108 16204

-1103 -674 -162 -976 4708 16268

698 441 306 -1304 4468 16620

-563 127 -58 -1340 5372 15984

172 -223 -2 -1408 4800 16140

-153 192 -219 -1316 5160 15880

-149 -98 -15 -1088 4884 16184

-83 -47 -3 -1180 5008 16184

-208 -37 -503 -1640 5132 16436

-912 -998 -340 -1688 5812 16004

162 595 281 -240 3836 16604

1015 1458 -325 -2012 4984 16264

-240 -924 202 -1676 5200 16236

-383 588 -351 -1220 4812 16304

-230 -204 -112 -1496 4636 16144

-16 50 -114 -1416 4680 16164

171 -251 93 -1492 4888 16060

129 193 215 -1352 5032 16164

158 266 138 -1192 5176 15996

681 360 -188 -1604 5112 16248

64 -467 -161 -1276 5172 16096

-352 524 -241 -1248 5084 16184

-286 -804 1825 104 3244 16924

22

Squatting

Gyroscope

(rad/s)

Accelerometer

(m/s2)

463 822 -2206 -2452 6804 15508

336 -91 1546 -1112 4860 16132

1137 450 -535 -1772 5348 16500

589 -585 -592 -2560 6032 15616