VR Reality Comparison Using Inertial Sensors

Reality Comparison for
Walking/Running in VR
System Using Inertial Sensor

こんにちは！
I am Ahmad Ridwan F auzi
I am from Tanaka Hiroshi L aboratory
I am from Indonesia, lives temporarily in Surabaya to finish my study
My university is Sepuluh Nopember Institute of Technology
In J apanese, my university’s name is スラバヤ工科大学
1

Background
▫ User and VR application integration need is
getting higher;
▫ There are several VR devices on market.
Comparison is needed to find which VR
device has the best realism;
▫ There are also several type of scenes in
order to be used on VR application.
Comparison among the scenes is also
needed;
▫ Walking case is chosen to be evaluated as
this is the most common people activity.
2

Objective
▫ Integrate user’s daily walking activity to the
application using inertial sensor;
▫ Evaluate which virtual reality device has the
best realistic experience (Oculus Rift, Google
Cardboard, CAVE);
▫ Evaluate which scene provides the most
realistic experience (Google StreetView,
360-Degree Camera, 3D Animation).
3

Signal Processing of Accelerometer
Sensor to Detect User’s Motion

Method #1: Threshold Method
▫ Threshold is calibrated by asking user to walk, to walk quickly,
to run, and to run quickly;
▫ 4 datasets related to those 4 actions will be obtained and the
change of distance will be calculated by this equation:
𝑑𝑖 = 𝑥𝑖
2
+ 𝑦𝑖
2
+ 𝑧𝑖
2
− 𝑥𝑖−1
2
+ 𝑦𝑖−1
2
+ 𝑧𝑖−1
2
▫ 𝑥 is the x axis of accelerometer, 𝑦 is the y axis, and 𝑧 is the z
axis. W hile 𝑖 is the i-th time the equation is used;
▫ Each dataset will be compared by the threshold value;
▫ Threshold value will be chosen if:
Number of data that exceeds threshold value on
walking dataset < walking quickly < running < running quickly
▫ If not, add threshold by 0.025 and compare again until the
condition is satisified;
▫ Speed will be calculated using the following equation:
speed = speed + 0.005 * threshold
4
𝑥
𝑦
𝑧

-0.5
0
0.5
1
1.5
2
2.5
3
0 50 100 150 200 250
Walk
0
0.5
1
1.5
2
2.5
3
1
11
21
31
41
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
211
221
231
Walk Fast
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1
12
23
34
45
56
67
78
89
100
111
122
133
144
155
166
177
188
199
210
221
232
Run
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1
12
23
34
45
56
67
78
89
100
111
122
133
144
155
166
177
188
199
210
221
232
Run Fast
Total Data: 236
Data that exceeds
value 0.25:
Walk, 83
Walk Fast, 111
Run, 112
Run Fast, 108
Data that exceeds
value 0.3:
Walk, 75
Walk Fast, 99
Run, 104
Run Fast, 107
0.25
0.3
0.25
0.3
0.25
0.3
0.25
0.3
5
The results are bad because the difference is
only just a little

Method #2: Step Detection Method
▫ This method detects the change of cosine value of the
accelerometer sensor;
▫ Threshold value will be used to increase the number of step, if the
cosine value exceeds the threshold;
▫ According to Tomlein et al. (2012), the cosine value is
calculated using this expression:
𝑑𝑖 = (cos 𝛼)𝑖=
𝑥𝑖 𝑥𝑖−1 + 𝑦𝑖 𝑦𝑖−1 + 𝑧𝑖 𝑧𝑖−1
𝑥𝑖
2
+ 𝑦𝑖
2
+ 𝑧𝑖
2
𝑥𝑖−1
2
+ 𝑦𝑖−1
2
+ 𝑧𝑖−1
2
▫ The last 10 cosine value (to filter noise) is calculated using
this expression:
𝑊𝑀𝐴10(𝑑)𝑖 = 𝑊𝑀𝐴10 cos 𝛼 𝑖 =
10𝑑𝑖 + 9𝑑𝑖−1 + ⋯ + 𝑑𝑖−9
55
▫ Every detected step will add the speed in the application;
▫ This method is selected because it is efficient and
easier to use.
6
Tomlein, Michal, et al. Advanced Pedometer for Smartphone-Based Activity Tracking. International Conference on Health Informatics, page 2, 2012.

-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
103
109
115
121
127
133
139
145
151
157
163
169
175
181
187
193
199
205
211
217
223
229
235
241
247
253
259
265
271
277
283
289
295
301
307
313
319
325
331
337
343
349
355
361
Walking
0
0.2
0.4
0.6
0.8
1
1.2
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
103
109
115
121
127
133
139
145
151
157
163
169
175
181
187
193
199
205
211
217
223
229
235
241
247
253
259
265
271
277
283
289
295
301
307
313
319
325
331
337
343
349
355
Running
0.96
0.96
7

System Design and Configuration
Components and functions used on the system

Scene Photos
3D Animation Scene Google StreetView Scene
360-degree Camera Scene
8

Oculus Rift System
Configuration
▫ PC: rendering image
▫ Smartphone: detecting
user’s motion in terms of
cosine value
▫ Oculus Rift: detecting
rotation and displaying
image
▫ Access Point: connector
for sending data of
cosine value from
Smartphone to PC
Smartphone (Motion Detector)
Detect movement using
Accelerometer
Cosine Value of X, Y, Z
from Accelerometer
Calculate
PC (Processing Unit)
Adds speed for the user to
move in application if cosine
exceeds threshold
Rotates Camera Around
Send
(via LAN)
Rift (Display)
Detect Head Rotation
Send
(via Connected USB Cable)
Display Calculated Image
from PC
Render Image
9

Google Cardboard
System Configuration
▫ Display Smartphone:
displaying image and
detecting rotation
▫ Motion Detector
Smartphone: detecting user’s
motion in terms of cosine
value
▫ Access Point: connector for
sending data of cosine value
from Motion Detector
Smartphone to Display
Smartphone
▫ Cardboard: place display
smartphone in order to
display VR image
Accelerometer
Cosine Value of X, Y,
Z from Accelerometer
Calculate
Display Phone (Processing
Unit and Display)
move in application if
cosine exceeds threshold
Detect Head Rotation and
Send
(via LAN)
Phone (Motion Detector)
Render and Display
Calculated Image
10

CAVE System
Configuration
▫ PC: rendering image
▫ Smartphone: detecting
user’s motion in terms of
cosine value
▫ Access Point: connector for
sending data of cosine
value from Smartphone to
PC
▫ Polhelmus G4 Sensor:
detecting rotation
▫ Projectors: displaying
image
Phone (Motion Detector)
Accelerometer
Cosine Value of X, Y, Z
from Accelerometer
Calculate
PC (Processing Unit)
move in application if
cosine exceeds threshold
Send
(via LAN)
CAVE (Display)
Display Calculated Image
from PC
Render Image
Polhelmus G4 (Rotation Sensor)
Get rotation data
Send
(via USB)
11

Experiment Method
Explaining the Experiment Purpose and Method

Experiment Purpose
▫ Experiment plays the main role in this
research as it aims to compare the most
realistic device and scene;
▫ It is mainly to understand which device and
scene that has the most realistic feeling of
all;
▫ It uses questionnaire from the participants
to determine the most realistic device and
scene.
12

Experiment Method
▫ This experiment involves 1 1 participants;
▫ Explanation of how system works is given to
know the purpose of the experiment;
▫ Participants are asked to walk, to run each
for 20 seconds for each scene and device;
▫ After each device finished, 1 device 3
scenes, they have to fill in the
questionnaire;
▫ The previous step is repeated until all 3
devices finished.
13

Experiment
Result
The result is divided into two categories, one is for
the devices, another one is for the scenes

Comparison by Device
0
1
2
3
4
5
6
7
Immersion Dizziness
Oculus Rift
Very Poor Poor Fair Good Excellent
▫ Oculus Rift
▪ Immersion’s average, 4.09
▪ Dizziness level average, 1.91
▫ Google Cardboard
▫ CAVE
Number of People
Rank
Average: 4.09 Average: 1.91
14

0
1
2
3
4
5
6
7
8
Immersion Dizziness
Google Cardboard
▫ Oculus Rift
▫ CAVE
Number of People
Rank
14

0
1
2
3
4
5
6
Immersion Dizziness
CAVE
▫ Oculus Rift
▫ CAVE
Number of People
Rank
14

0
1
2
3
4
5
6
Oculus Rift Google Cardboard CAVE
Google StreetView Realism Feeling
Realism Feeling
Comparison by Scene
▫ Google StreetView realism feeling
▪ Using Oculus Rift, 2.27
▪ Using Google Cardboard, 1.91
▪ Using CAVE, 2.1
▫ 360-degree Camera realism
feeling
▪ Using CAVE, 2.91
▫ 3D Animation realism feeling
Number of People
Rank
Average: 2.27 Average: 1.91 Average: 2.1
15

Realism Feeling
Comparison by Scene
▪ Using CAVE, 2.1
feeling
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
360-degree Camera Realism Feeling
Number of People
Rank
15

Realism Feeling
Comparison by Scene
▪ Using CAVE, 2.1
feeling
0
1
2
3
4
5
6
7
8
3D Animation Realism Feeling
Number of People
Rank
15

0
1
2
3
4
5
6
7
Google StreetView Acceleration Feeling
Acceleration Feeling
Comparison by Scene
▫ Google StreetView acceleration
feeling
▫ 360-degree Camera acceleration
feeling
▫ 3D Animation acceleration feeling
Number of People
Rank
16

0
1
2
3
4
5
6
7
360-degree Camera Acceleration Feeling
Comparison by Scene
feeling
feeling
Number of People
Rank
16

0
1
2
3
4
5
6
7
8
3D Animation Acceleration Feeling
Comparison by Scene
feeling
feeling
Number of People
16
Rank

0
1
2
3
4
5
6
7
Google StreetView
Overall Realism
▫ Google StreetView overall realism
▪ Oculus Rift, 2.64
▪ Google Cardboard, 2.1
▪ CAVE, 2.18
▫ 360-degree Camera overall
realism
▪ CAVE, 3.27
▫ 3D Animation overall realism
▪ CAVE, 4.54
Number of People
Rank
17

0
1
2
3
4
5
6
7
8
360-degree Camera
Overall Realism
▪ CAVE, 2.18
realism
▪ CAVE, 3.27
▪ CAVE, 4.54 Rank
17
Number of People

0
1
2
3
4
5
6
7
8
3D Animation
Overall Realism
▪ CAVE, 2.18
realism
▪ CAVE, 3.27
▪ CAVE, 4.54
Number of People
Rank
17

Summary
▫ Integrating user’s walking motion
proved to be successful. But it still needs
some improvements;
▫ According to the device comparison result,
CAVE is the best device, following
Oculus Rift and Google Cardboard;
▫ The scene result shows that 3D
animation scene is the clear
winner, following 360-degree camera and
Google StreetView.
18

どうもありがとうございました。
Any questions, advices, and critics are very well welcomed

VR Reality Comparison Using Inertial Sensors

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