Post on 30-Aug-2018
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Multisensor Systems & Ambient Assisted Living (AAL)
DIEEI
Department of Electrical, Electronic and Information Engineering University of Catania – Italy
14-01-2015
Cristian Orazio Lombardo cristian.lombardo@gmail.com
Technologies
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Progressive increase in the average age of the population. Over the last 50 years, the number of older persons worldwide has tripled - and will more than triple again over the next 50-year period as the annual growth of the older population (1.9%) is significantly higher than that of the total population (1.02%). The European Commission has predicted that between 1995 and 2025 the UK alone will see a 44% rise in people over 60.
THIS SITUATION ASKS FOR NEW SOLUTIONS TOWARDS IMPROVING THE INDEPENDENCE, THE QUALITY OF LIFE, AND THE ACTIVE AGEING OF OLDER CITIZENS.
Need to find new models of intervention involving a plurality of actors according to the logic interdisciplinary
New advanced, reliable and socially acceptable ICT services to cope the additional costs of the welfare state that we will face in the years to come
Why?
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OBJECTIVES:
• Extend the period in which people can live in their preferred environment by
increasing their autonomy, self-sufficiency and mobility;
• Keep health and functional capacity of the elderly;
• Increase safety, prevent social exclusion
• Support workers, family members and organizations of care;
AAL is not just technology, it requires collaboration and effective communication between stakeholders:
RESEARCHERS, PLANNERS, INDUSTRY, USERS, ADMINISTRATORS, SOCIAL WORKERS AND HEALTH CARE
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What we want to be preserved and guaranted!!!
Smart&Authonomous Sensing Systems @SensorLab
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WSN & Multi-sensor systems
• IMU • Indoor localization • Haptic assistive systems • Weak people monitoring (ADL) • Cognitive Sensor network • Hazards monitoring
•Sensor data fusion PCA, Rock Curves, Neuro-Fuzzy,
Kalman Filter, Dythering, Wavelet
•Sensor Networks
•Advanced readout
Methodologies
Ambient Assisted Living@SensorLab
Resima
•Multisensor platforms
•Customized Solutions
•Customized WSN
•Smart Phone
•Embedded Systems
•Microcontroller Systems
•Microprocessor Systems
Technologies
Smart&Authonomous Sensing Systems @SensorLab
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Systems for the Active Ageing and Visually Impaired People
Smartphone based solutions
Customized architectures
Smart&Authonomous Sensing Systems @SensorLab
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System specifications
Sensitivity to a large set of colours
Clustering features of similar tone of colours
Light Green Dark Green Green
GOAL: To identify color of the different materials to provide blind user with
an improved informations about environment.
Smart&Authonomous Sensing Systems @SensorLab
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GOAL: A Smart multi sensor strategy to assist blind people in navigation
Smart&Authonomous Sensing Systems @SensorLab
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Smart&Authonomous Sensing Systems @SensorLab
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GOAL: User tracking in indoor and outdoor structured environment
RFID Receiver Floor with passive tags
Smart&Authonomous Sensing Systems @SensorLab
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GOAL: To reproduce the real feeling provided by a traditional white cane.
ADVANTAGES : Natural codification, no physical interaction.
Methodology and solutions • Vibration motors positioned on the handle reproduce the “bump” feelings. • An inclinometer is used to measure the cane tilt • A ultrasound sensor measures the distance from the obstacles
Smart&Authonomous Sensing Systems @SensorLab
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Improving the perception of weak signals by added noise
Smart&Authonomous Sensing Systems @SensorLab
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The effect of background noise
Smart&Authonomous Sensing Systems @SensorLab
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The effect of contour noise
Smart&Authonomous Sensing Systems @SensorLab
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[kg]
Goal: Development of a low cost, low power, minimally invasive sistem for fall detection, posture and activity rate monitoring
Sensor: Accelerometer
ACTIVITY / INACTIVITY
Each phase of the Fall Dynamic Behaviour has to be checked in time and amplitude
Smart&Authonomous Sensing Systems @SensorLab
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Systems for the Active Ageing and Visually Impaired People
Smartphone based solutions
Customized architectures
Smart&Authonomous Sensing Systems @SensorLab
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Methodology • Multisensory datafusion • Multi-feedback approach • Fall Detection • Fall typology Identification • Feedback evaluation • Skill monitoring • Smart processing • Activity rate monitoring • Scheduled Monitoring • Event Triggered Monitoring • Autonomous • Caregivers alerting
Goal: Development of a platform for fall detection, cognitive capability monitoring and activity rate
Technology • Low cost custom module • Customized App • Inertial Sensors • Bluetooth Communication
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Regione Siciliana ASSESSORATO REGIONALE DELLE ATTIVITA’ PRODUTTIVE
AVVISO PUBBLICO PER LA CONCESSIONE DELLE AGEVOLAZIONI IN FAVORE DELLA RICERCA, SVILUPPO ED INNOVAZIONE PREVISTE DALL’ART 5 DELLA LEGGE REGIONALE 16.12.2008, N. 23
Linea di intervento 4.1.1.1 - POR FESR Sicilia 2007-2013
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Env . quantities Compass, a x , a y , a z
d i
Pre-processing&Elaboration algorithms
User Inertial Trilateration Env . status
User - Environment Interaction
User - Environment Contextualization
Environment Info
Decision Support System
User skill
Message Center
Supervisor GUI
User
x
z
y
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
GOAL: Provide the user with a smart form of indoor assistance (safety and efficient site exploration/exploitation) by: •awareness of User-Environment Contextualization …the user status related to the environment status •awareness of User-Environment Interaction …physical interaction between user and environment
Target Users • Visually Impaired • Weak users (e.g. elderly)
Advantages • To provide high level form of information to users and caregivers • Spatial and temporal continuous form of assistance • Low costs, easy installation & maintenance • Low impact on the pre-existing environment
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Env . quantities User: , di, a x , a y , a z
Pre-processing&Elaboration algorithms
User Inertial Trilateration Env . status
User - Environment Interaction
User - Environment Contextualization
Environment Info
Decision Support System
User skill
Message Center
Supervisor GUI
User
x
z y
•Multisensor nodes •Inertial Unit •Coupled US distance sensors
•User position •Environment safety •User behaviour
•Candidates messagges for the User •User/env representation for the supervisor •Messagges for the User
•Suggested actions for the supervisor
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IRIS Features
Frequency band: 2405 MHz to 2480 MHz ISM band
Transmit (TX) data rate: 250 kbps
RF power: 3 dBm (typ)
Receive Sensitivity: -101 dBm (typ)
Outdoor Range: > 300 m
Indoor Range: > 50 m
Current Draw: 16 mA (Receive mode)
IEEE 802.15.4 compliant RF transceiver
Battery 2X AA batteries Attached pack
External Power 2.7 V - 3.3 V Molex connector provided
User Interface 3 LEDs Red, green and yellow
Wireless Sensor Network
USER NODE
ENVIRONMENTAL NODE
SERVER NODE
Star Net Topology
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User Node Sensors
Wireless Module
Ultrasonic Sensor
9DoF Inertial Module
Digital Compass
40 kHz
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USER MODULE
9 Degrees of Freedom - Sensor Stick
Accelerometer (ADXL345) Gyroscope (ITG-3200) Magnetometer (HMC5883L)
Accelorometer
Compensated Compass
SENSORS ALGORITHMS
User Posture
User Heading
Dead Reckoning
User Heading
Magnetic Field Meas.
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Environmental Node Sensors
Wireless Module
Temperature Sensor
Ultrasonic Sensor
Smoke Sensor
Gas Sensor
40 kHz
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MODULO UTENTE
AURICOLARE
CUFFIA
Environmental Node
REAL VIEW OF THE HARDWARE IMPLEMENTATION Headset
User Module
Earphone
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HW IMPLEMENTATION OF THE ULTRASOUND MODULE (US TX – User Node)
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
40 kHz – Oscillator Stadium
Driver Stadium
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HW IMPLEMENTATION OF THE ULTRASOUND MODULE (US RX – Environmental Node)
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
Selective Amplifier Gain Stadium Comparator
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Reconstruction of the single step is performed by the combination of
the Heading θi and the StepSize ΔSi .
The absolute position is obtained by cumulating step by step the
relative positions
ΔS1
ΔS2
ΔS3 ΔS4
x
y
ΔSi
θi
X’
Y’ = ΔS *cos(θ)
X’ = ΔS *sinθ)
Y’
N
E
Dead Reckoning Techniques Walking Path Reconstruction
• The position error increases step by step. • It needs periodic correction throught
absolute measurement of position. • Usefull to evaluate short distance path • Useful to estimate physical capability of
the user
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USER LOCALIZATION - Ultrasound Trilateration
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
Time of Flight (TOF) of the Ultrasound Wave is converted in Distance
TX – User Module RX – Environmental Module TX
RX
RX
RX
RX
RX
RX
RX
RX
RX
TX
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ʃ
Algorithms for
Step Size (S)
Estimation
Dynamic Sdin
Static Sstat
ΘK
SK
S
θ
XY
Compass
Gyroscope
Accelerometer ΘComp
ΘGyro Processing
Processing Kalman
Filter
Kalman Filter
HEADING ESTIMATION
STEP SIZE ESTIMATION
WALKING PATH ESTIMATION
Dead Reckoning Techniques Schematization
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Multisensor Systems & Ambient Assisted Living (AAL)
DIEEI
Department of Electrical, Electronic and Information Engineering University of Catania – Italy
15-01-2015
Cristian Orazio Lombardo cristian.lombardo@gmail.com
Algorithms and Signal Processing
Smart&Authonomous Sensing Systems @SensorLab
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USER LOCALIZATION - Ultrasound Trilateration
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
Time of Flight (TOF) of the Ultrasound Wave is converted in Distance
TX – User Module RX – Environmental Module TX
RX
RX
RX
RX
RX
RX
RX
RX
RX
TX
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Master send Acki
Envir. Nodej
User Nodek
Start Timer (usec)
Generate US Pulse (10ms)
US Signal
Received
TOF=0
TOF<>0
ArrayTOF
TOF received from all Envir.
nodes
TOFi
Send Serial Information
of TOF to Labview
NACK - Repetitions
Filter TOF Min!=0
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Characterization of the Distance Measurement
• Range of the measured distance: 0 cm – 700 cm
• Step measurement: 10 cm
• Refresh time: 1 s
• Sampling frequency: 20 sample/s
• Number of acquired samples: 20 for each distance (1 s)
• Variable influence measured: Environmental Temperature
US-TX Circuit Master Node
Experimental Setup
US-RX Circuit
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
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Uncertainty about 2cm
Characterization of the Distance Measurement
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24,5 °C
25,0 °C
25,5 °C
26,0 °C
26,5 °C
27,0 °C
27,5 °C
28,0 °C
0 cm 100 cm 200 cm 300 cm 400 cm 500 cm
TEMPERATURE DRIFT DURING CHARACTERIZATION
Current Distance
Tem
pe
ratu
re
v(T)=331,4 + 0,62 T [m/s] where T is temperature in °C
Velocity of sound waves:
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0
2
4
6
8
10
12
0 cm 100 cm 200 cm 300 cm 400 cm 500 cm
Distance
Measurement Error without Temperature Compensation
Erro
r [c
m]
v(T)=331,4 + 0,62 T [m/s] where T is temperature in °C
Velocity of sound waves:
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2
3
2
3
2
3
2
2
2
2
2
2
2
1
2
1
2
1
dYyXx
dYyXx
dYyXx
d1
d2
d3
3131
2121
31
2
3
2
1
2
3
2
1
2
1
2
3
21
2
2
2
1
2
2
2
1
2
1
2
2
2YYXX
YYXX
YYYYXXdd
YYYYXXdd
x
3131
2121
2
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1
2
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2
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331
2
2
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2
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1
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221
2YYXX
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YYXXddXX
YYXXddXX
y
2
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33131
2
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22
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YYXXddyYYxXX
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System of nonlinear
polynomial equations
Final linear system
Let us consider only 3 sensors; assume that we can
measure the distance with no errors
(X3,Y3)
(X1,Y1)
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(X2,Y2)
2D-Trilateration: Operating principle A Sensor Network for US-based indoor localization
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Eng. Salvatore La Malfa
2D-Trilateration: uncertainty sources
List of uncertainty sources that could affect the accuracy
and the precision of the measurement of the distance:
Uncertainty Sources Affecting the accuracy
Xi, Yi coordinates are given with finite accuracy
Actual speed of acoustic waves
Operator errors
…
Affecting the precision
Acoustic noise in the US frequency range
Electric noise in the HW equipment
Fluctuation of the microcontroller oscillator
Quantization noise due to the finite resolution of the
microcontroller timer
…
?
1 1 1
2 2 2
3 3 3
ˆ ( )
ˆ ( )
ˆ ( )
d d u d
d d u d
d d u d
ˆ ( )
ˆ ( )
u u
u u
x x u x
y y u y
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A Sensor Network for US-based indoor localization
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NiYX ii ,...,2,1,
Nidi ,...,2,1
!
1,2,..., ( ,3)3 !3!
Nj M M C N
N
Sensor nodes coordinates
Distance between user node and
the i-th sensor node
(X1,Y1) (X2,Y2) (X3,Y3)
(X4,Y4)
(X5,Y5)
(X6,Y6)
(X7,Y7) (X8,Y8) (X9,Y9)
(X10,Y10)
(X12,Y12)
(X11,Y11)
d1 d2
d3
d4
d5
d6
d7
d8 d9
d10
d11
d12
1
T Tx x
A B A A A By y
We have 2M equations and 2 unknowns
M = number of 3-combinations from a set of N elements
(Xij,Yij) the i-th corner of the j-th triad
dij the user distance from the i-th corner of the j-th triad
Let us denote by:
1 2
1
2
1
2
2
1 2
122 2
( , ,..., )
( , ,..., )
1
x x xN x
y y yN y
N
N
N
d
dd d d
d
c c c c fx
c c c c gy d dd
We simply solve a least squares problem
Constant elements matrix, we calculate this
2N+2 numbers only once, as their values
depend only on Xi, Yi The symbolic form for the C matrix is nontrivial!
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
STA (Single Trilateration Algorithm)
IF YOU HAVE N>3 measured distances
LEAST MEAN SQUARE ALGORITHM
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NiYX ii ,...,2,1,
1,2,...,id i N
!
1,2,..., ( ,3)3 !3!
Nj M M C N
N
Sensor nodes coordinates
Distance between user node and
the i-th sensor node
(X1,Y1) (X2,Y2) (X3,Y3)
(X4,Y4)
(X5,Y5)
(X6,Y6)
(X7,Y7) (X8,Y8) (X9,Y9)
(X10,Y10)
(X12,Y12)
(X11,Y11)
d1 d2
d3
d4
d5
d6
d7
d8 d9
d10
d11
d12
We have 2M equations and 2 unknowns
M = number of 3-combinations from a set of N elements
(Xij,Yij) the i-th corner of the j-th triad
dij the user distance from the i-th corner of the j-th triad
Let us denote by:
2 2 2 21
2 2 2 2
2 2
2 2
1 2 1 21 2 1 2
1 3 1 3 1 3 13 1 3
2 1j j j j j jj j j j j
j j j j jj j jj j j
X X Y Yx X X Y Y
y X X Y Y X
d d
d Xd Y Y
We solve this system M
times obtaining M different
solutions for user position.
Finally, we apply some kind
of filtering.
MTA Filter
d1
d2
dN
(x1,y1)
(x2,y2)
(xM,yM)
(x,y)
…
…
Under certain conditions, this
approach can improve the
robustness and the precision
of the localization system
ENHANCED TRILATERATION ALGORITHM
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
MTA (Multiple Trilateration Algorithm)
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The system was characterized in a real environment, by a non uniform grid [100, 50, 10] cm
Room area: 43 m2. N=7 sensor nodes
Characterization of the trilateration Algorithm
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2 2.5 3 3.5 4 4.5 5 5.52.5
3
3.5
4
4.5
5
5.5
6
6.5Results MTA Algorithm
X axis(m)
Y a
xis
(m)
measured position
expected position
standard deviation
DISTRIBUTION OF RESIDUALS – MTA
m
Map Rappresentation of the Results of the Static Characterization of the MTA trilateration Algorithm
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
Measurement Grid = 108 points
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0 20 40 60 80 100 120-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
Measurement Points
ST
A-M
TA
(m)
Differences Between STA-MTA Results
Mean value
-0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.10
5
10
15
20
25
STA-MTA(m)
Distribution of the Differences Between STA-MTA Results
histfit
STA – MTA COMPARISON
Difference between Residuals of STA Algorithm and MTA Algorithm
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
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1 2 3 4 5 6 7 80
1
2
3
4
5
6
7
8
9Results of MTA
X [m]
Y [
m]
Measured Path
Nominal Path
Sensor Position
1 2 3 4 5 6 7 80
1
2
3
4
5
6
7
8
9Results of STA
X [m]
Y [
m]
Measured Path
Nominal Path
Sensor Position
Walking Path: Rectangular Path 11 m long; Number of Repetition of the Walking Path: 10
Dynamic Evaluation of the Trilateration Algorithms
RESIMA: a new WSN based paradigm to assist weak people in indoor environment
Results of MTA
100%
Nom
EstimNom
wdWD
WDWDJ
STA MTA
JWD% 12,7 3,5
Results of STA
Env. node
WD=Walking Distance
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User Posture Estimation
The user posture can be estimated merging roll and pitch estimation:
𝑟𝑜𝑙𝑙 = tan−1𝐴𝑧
𝐴𝑦2+𝐴𝑥
2
𝑝𝑖𝑡𝑐ℎ = tan2−1𝐴𝑥
𝐴𝑦2 + 𝐴𝑧
2
z
y
x
Ax , Ay , Az = Signals of Accelerometer
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θ = Roll; ϕ = Pitch
-15°<φ<15°
NO
ERECT
BOWED
-45°<θ<-90° or
45°< θ <90°
LIE DOWN LATERAL
YES
20°<φ<60°
-70°<φ<-60°
-20°<φ<-15°
ERECT/BOWED 15°<φ<20°
ERECT/SITTING
SITTING -60°<φ<-20°
SITTING/LIE DOWN
60°<φ<70° BOWED/LIE DOWN
φ>70° LIE DOWN PRONE
LIE DOWN SUPINE
YES
YES
YES
YES YES
YES
YES
YES
NO
NO
NO NO
NO
NO
NO
NO
POSTURE RECOGNITION ALGORITHM
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𝜶 𝜽
𝒓𝑼𝑺
𝒓𝑶𝑩
Actual Implementation of Interaction with the Environment
Intersection between Safety range of the User
and of the Objects
Comparison between attitude of the user and direction of the distance between user position
and drawing point
- Object contour is drawed by connecting drawing point having distances each other of about 50-60cm
1° Step
2° Step
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TEST 1: Obstacles Avoidance
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0,10,25
0,50,75
User safety range[m]
FN
TP
Test Constrains: User is free of moving inside the environment. Each collision is considered a failure, each avoidance of the object is considered a positive result
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TEST 2: Fruition of Services
0%
20%
40%
60%
80%
100%
0,1 0,25 0,5 0,75 11,25
User safety range[m]
FN
TP
Test Constrains: User moves toward a service, a positive result was considered when user stops walking near the service at less than 50cm