Towards a User-wheelchair Shared Control Paradigm for Individuals

with Severe Motor Impairments

Alfredo Ch

avez Plascencia and Jaroslav Rozman

Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology,

zet

echova 1/2 612 66, Brno, Czech Republic

Keywords:

Assistive Technology, Sensor Fusion, Autonomous Wheelchair.

Abstract:

This paper presents a work in progress study of a novel user-wheelchair shared control paradigm for individu-

als with severe motor impairments, which consists of an optimal distribution between several modes, from full

user control up to autonomous driving one. To this end, a C400 Permobil wheelchair has been equipped with

a control command communication interface and with a scanning laser and a RGB-D sensors to carry out the

automation algorithms that are part of the robot operating system (ROS) framework. Moreover, sensor data

fusion for map making based on the Bayesian method is applied to the Xtion Pro Live RGB-D camera and the

Hokuyo laser sensor data readings. These latter are interpreted by a probabilistic heuristic model that abstracts

the beam into a ray casting to an occupied grid cell. Preliminary pilot tests were performed in two different

room shapes. The ﬁrst one in a two room laboratory with a narrow doorway, and the second one in a corri-

dor. The former experiment was dropped due to failure to success, whereas, the latter was a successful one.

This has been tested with three different modalities; hand-joystick, tongue-joystick and autonomous modes

respectively. The successful results of the second pilot-test have proven the feasibility of using a combina-

tion of autonomous and manual control of a powered wheelchair in order to continue development towards a

shared-control paradigm.

1 INTRODUCTION

Powered wheelchairs (PWC) are used to assist mobil-

ity of individuals with severe motor disabilities, such

as those with tetraplegia. Users that still maintain

some degree of motor control of arms or hands use a

joystick in order to control the direction and speed of

the PWC. On the other hand, users with more severe

or complete motor disabilities have to rely on alter-

native control interfaces that can detect head move-

ment (Christensen and Garcia, 2003), chin move-

ment (Cooper et al., 2002), gaze (San Agustin et al.,

2009), tongue movement (Xueliang and Maysam,

2010) (Lund et al., 2010) and even forehead muscular

activity and brain waves (Torsten, Felzer and Rainer,

Nordman, 2007). Thus, research has been done to de-

velop devices that can interface the remaining func-

tional parts of such individuals. Many of these inter-

faces require the user to sustain high levels of con-

centration for navigating in environments with many

obstacles. Some systems might be tedious and tire-

some to use, especially when constantly maneuver-

ing a wheelchair, while others might interfere with

the normal use of the user’s vision or head, eyes or

tongue movement. Allowing certain degree of au-

tonomy to the wheelchair might relieve the users of

the burden of long periods of concentration or fatigue,

and allow a more free use of their own eyes, tongue or

head movement while driving the wheelchair. How-

ever, a fully autonomous system is not desired, since

the users should be allowed as much control of the

wheelchair as their capabilities and degree of disabil-

ity allows them to have, and without compromising

the use of their vision, speech head movement unnec-

essarily or for prolonged periods of time.

Some related research work has been focused on

sharing the wheelchair control with the user. For in-

stance, a shared wheelchair control method for ob-

stacle avoidance is presented in (Petry et al., 2010).

This method mainly proposes using potential ﬁeld for

path planning and shares the wheelchair control to

help the user to avoid obstacles. (Faria et al., 2013)

presents a manual-shared-automatic method for con-

trolling a PWC. The automatic control consists of fol-

lowing points without human intervention, the shared

control acts in safety situations, mainly when there

246

Plascencia, A. and Rozman, J.

Towards a User-wheelchair Shared Control Paradigm for Individuals with Severe Motor Impairments.

DOI: 10.5220/0005973302460253

In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) - Volume 2, pages 246-253

ISBN: 978-989-758-198-4

is a potential collision. (Jiding et al., 2014) presents

a shared-control wheelchair based on Brain Computer

Interface (BCI), where the shared-control provides as-

sistance strategy to control the wheelchair when it is

in a collision region.

Based on the present research our main goal

is to investigate further in order to ﬁnd the opti-

mal distribution of control tasks between the user

and the intelligent wheelchair controller. This shall

help to develop a novel user-wheelchair shared con-

trol paradigm that takes into consideration the needs

and abilities of individuals with severe motor impair-

ments, and allows them to control a PWC from a

user-controlled, through a semi-autonomous up to a

fully autonomous way. The amount of autonomy in

the semi-autonomous control mode, also referred to

as ”shared-control”, should depend on the user pref-

erences, the degree of disability of the user and the

amount of input the user can give in a fast and ef-

ﬁcient way, allowing the user and the wheelchair to

”share” control tasks.

For this study, two different environment shapes

were taken into account in order to validate the pre-

liminary paradigm. The ﬁrst shape is a two room lab-

oratory and the second one is a corridor. The exper-

iments of the ﬁrst shape were dropped early due to

failure to success and are not presented in this arti-

cle. Instead an analysis on how to overcome them is

presented in the conclusion. On the other hand, the

second pilot tests were successful and are presented

in this work.

From the alternative control interfaces for a PWC,

we have chosen to use a tongue-controlled inter-

face. Using the tongue to control a PWC seems to

be a promising alternative for the following reasons.

Firstly, the tongue is able to perform sophisticated

motor control and manipulation tasks with many de-

grees of freedom. Secondly, it is able to move rapidly

and accurately and does not fatigue easily and can be

controlled naturally without requiring too much con-

centration. Georgia Institute of Technology has de-

veloped a tongue drive system (TDS) that consists of

a headset and a magnetic tongue barbell. Through a

smartphone (iPhone), the TDS is able to interpret ﬁve

tongue movement commands: forward (FD), back-

ward (BD), turning right (TR), left (TL) and stopping

(N) (Jeonghee et al., 2008), (Xueliang and Maysam,

2010), (Andreasen, 2006),(Jeonghee et al., 2012).

Similarly, an intra-oral inductive tongue control sys-

tem (ITCS) has been designed and built at Aalborg

University. It interpolates the sensor signals to em-

ulate an intra-oral touchpad that can proportionally

control the direction and speed of the PWC (Caltenco

et al., 2011).

Figure 1: The C400 permobil wheelchair equipped with a

control command communication interface and with a scan-

ning laser and a RGB-D sensors.

2 SYSTEM DESCRIPTION

2.1 Communication Interface

The C400 Permobil PWC as shown in Figue 1 comes

with an Easy Rider wheelchair interface, from HMC

International, an Easy Rider display unit and a joy-

stick. The standard joystick mode accepts as input

signals a reference value of 5V and two analog volt-

age values in the range of 4V to 6V to proportionally

move the PWC from right to left and from back to

forward. To this end, an interface to send velocity

control commands (VCC) from the computer to the

motors has been developed using an Arduino UNO

board. The Arduino board receives two VCC bytes

(one for left-right and one for forward-backward di-

rections) and generates two pulse width modulated

(PWM) signals in the range of 0 to 5V. The signals

are converted to analog voltage using a simple RC

low-pass ﬁlter and stepped-up using a single supply

non-inverting DC Summing Ampliﬁer. The resulting

voltage is used to emulate the analog joystick posi-

tion as an input to the Easy Rider interface. The Easy

Rider interface then sends the necessary control sig-

nals to the wheelchair’s motor controller.

2.2 Alternative Control Interface

The ITCS (Caltenco et al., 2011), consists of two sep-

arate parts, the intra-oral device and an external con-

Towards a User-wheelchair Shared Control Paradigm for Individuals with Severe Motor Impairments

247

troller. The intra-oral device detects tongue move-

ments and wirelessly transmit signals to the external

controller, which connects to the Easy Rider inter-

face via the Joystick Input and to the computer via

bluetooth. The ITCS’s external controller interprets

and process tongue movement signals and transforms

them into joystick or mouse commands that can be

sent to the wheelchair or the computer. The com-

puter is a Lenovo T540p with an Intel(R) Core(TM)

i5-4200M CPU @ 2.50GHz running Ubuntu 12.04

(precise), The system previously explained can be de-

picted in Figure 2.

2.3 Sensors

Data from the environment is obtained by a Hokuyo

UTM-30LX scanning laser range ﬁnder. It has a sens-

ing range from 0.1m to 30m. Measurement accuracy

is within 3mm tolerance up to 10m of the sensor’s

range. The scanning rate is 25 milliseconds across a

270 range.

The Asus Live Xtion Pro RGB-D camera, popular

in various robotic projects. It has a ﬁeld of view of

◦

H, 45

◦

V (Horizontal, Vertical). Meanwhile, the

sensor’s depth image size is 640 × 480 pixels with

a total of 307, 200 pixels. The distance of use of the

sensor is between 0.8m and 3.5m.

3 SENSOR FUSION AND

MODELS

The laser can measure the distance to an object quite

accurately. However, there is an uncertainty in the

Figure 2: Overview of the System.

pulse that is reﬂected back to the sensor. On the other

hand, the RGB-D sensor also returns the depth to each

pixel, which uncertainty also need to be modeled. The

approach taken by (Moravec and Elfes, 1985), (Elfes,

1989b) and (St

an et al., 2005) to model the oc-

cupied and empty regions of the sonar beam can be

taken into consideration to model the uncertainty in

both sensors data readings.

RGB-D and laser probabilistic sensor data fusion

is proposed to complement both sensors ﬁeld of view,

(Ch

avez and Karstoft, 2014). Thus, Elfes (Elfes,

1989a) has proposed in his previous work the use of a

recursive Bayes formula to update the occupancy grid

for multiple sensor observations.

4 SOFTWARE ARCHITECTURE

Robot operating system (ROS) (Quigley et al., 2009)

is proposed as the software architecture to achieve

the different modes the user can select to drive the

PWC. The navigation stack (NS), which is a set of

conﬁgurable nodes, has been conﬁgured properly to

the shape and dynamics of the PWC to be performed

at a high level. Broadly speaking, the heart of the nav-

igation stack is the move base node which provides

a high level interface between odometer, PWC base

controller, sensors, sensor transforms, map server and

monte carlo localization algorithm (AMCL) nodes to

the local and global planners.

5 METHODS

One participant with no previous experience driving

a PWC participated in this pilot study. To this end, a

5 × 2 × 2 factorial experiment has been designed with

the following factors (independent variables):

1. Control mode [M] with ﬁve levels:

• M

(autonomous-mode)

• M

(user-mode using a usb gamepad joystick)

• M

(user-mode using wheelchair’s joystick)

• M

(user-mode using tongue-joystick)

2. Wheelchair velocity [V ] with two levels:

• V

(low velocity, 0.186

sec

)

• V

(high velocity, 0.243

sec

)

3. Number of obstacles [O] with two levels:

• O

(few obstacles on the path, 5 obstacles)

• O

(plenty obstacles on the path, 10 obstacles )

Moreover, the following dependent variables are

used to compare performance of each test scenario:

ICINCO 2016 - 13th International Conference on Informatics in Control, Automation and Robotics

248

1. T

(path’s completion time)

2. V

(average velocity)

3. N

(number of collisions)

4. d

(path’s distance).

The experiment design is three-dimensional in the

within subject variables. However, it is not fully fac-

torial, since the tongue-joystick control mode (M

)

did not perform the experiment using different veloc-

ity conditions [V ], giving a total of 18 within subject

conditions, instead of 20.

A graphical user interface (GUI) has been imple-

mented, where three modes can be selected by the op-

erator.

• Joystick mode (M

), the PWC is able to be ma-

nipulated by the commands emulated from the

control level, e.g. forward (F), backward (B),

right (R), left (L) and stopping (S). This can be

done using any of the three control interfaces

(wheelchair joystick (M

), gamepad joystick

)or tongue joystick (M

)).

• Semi-autonomous mode (M

), The process is

comprise of the following steps; 1.- Real obsta-

cles have been placed in the environment. 2.- A

global map with no obstacles is used. 3.- Joystick

mode is applied in order for the user to rotate the

PWC left or right while trying to ﬁnd a suitable

path. And, once this path has been spotted the user

can select the (F) joystick command. 3.- Then, a

shared-control algorithm is activated that ﬁnds a

suitable goal in the desired direction at the end of

the map. 4.- Afterwrads, the NS uses this goal

to move the PWC towards that direction avoiding

obstacles on its way till either the user cancel the

action or the PWC reaches the goal coordinates.

• Autonomous mode (M

), the user selects the co-

ordinates of a destination point on a map of the

known environment using the ITCS. The user can

always be able to override autonomous control

and take control of the wheelchair at any moment.

The C400 Permobil wheelchair serves as experimen-

tal testbed. The tests were carried out with real data

in an indoor laboratory environment. To this end, the

map of the indoor environment was built prior to the

tests, this was achieved by using the fused Hokuyo

laser and RGB-D data, which have been placed in

front of the PWC.

The RGB-D catches 3D depth data that is pro-

jected into 2D to ease the integration process. At the

same time, the laser ﬁeld of view also catches 2D

depth data. Furthermore, sensor registration is used

to align both sensor readings, so they can not suffer

from misalignment that can potentially cause errors

Figure 3: Schematics of the map.

in the fused grid maps. In each measurement the laser

scans a total of 512 readings distributed along 180

Meanwhile, the ROS depthimage to laserscan pack-

age is used to project RGB-D readings into 2D. Then,

each sensor reading is interpreted by the heuristic sen-

sor model. Afterwards, the recursive Bayes formula

is applied to fuse and update the data in each proba-

bilistic grid map, e.g. the RGB-D and laser maps.

To visualize the map making process and the nav-

igation, Figure 3 shows the RVIZ which is a ROS vi-

sualization tool. In the right part, the PWC-URDF

(uniﬁed robot description format) model can be seen,

the local map is represented as inﬂated sky-blue ob-

stacles that surround the URDF. The obstacles in the

global map are represented as black and the location

of the main obstacles are surrounded by a green el-

lipse, while the light and dark grays represents the

empty and unknown areas respectively, the fused sen-

sor readings that are used for localization, navigation

and to feed the local map can be spotted as red con-

tour that are situated in front of the PWC. And, the

path travel can also be seen on the empty area of the

map.

6 MAIN FINDINGS AND

RESULTS

Figures 4, 6 and 8 show the bar plots of the pilot tests

dependent variables T

, V

, N

and d

outcomes for

the factors ([M], [V ], [O]) and their corresponding lev-

els (M

, M

), (V

, V

) and (O

, O

It can be observed in Figure 4 that (O

, V

, M

)

and (O

, V

, M

) takes almost the same time to

travel the path even though the velocities are differ-

ent. Whereas, (O

, V

, M

) and (O

, V

, M

) takes

different time due to the reduction of obstacles. Then,

when driving the PWC in M

, (O

, V

) takes less

time than (O

, V

), in the same way (O

, V

) takes

more time than (O

, V

). It can also be observed

that (O

, M

) is the longest path’s time of all modes.

Towards a User-wheelchair Shared Control Paradigm for Individuals with Severe Motor Impairments

249

Figure 4: Time of the path.

−5 0 5 10 15 20

−1.5

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

localization

planning

obstacles

(a)

0 5 10 15 20

−1.5

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

localization

planning

obstacles

(c)

−5 0 5 10 15 20

−1.5

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

localization

planning

obstacles

(b)

−5 0 5 10 15 20

−1.5

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

localization

planning

obstacles

(d)

Figure 5: Autonomous mode (M

); a) fast velocity with

many obstacles. b) slow velocity with many obstacles c)

slow velocity with few obstacles. d) fast velocity with few

obstacles.

This fact could be because the user was inexperienced

with the ITCS. And, it is believed that this situation

can be improved by practicing using the tongue to

control the wheelchair. In the other hand (O

, M

)

takes less time, this is due to the fact that there is

less obstacles and the user is able to control better the

ITCS. However, the shortest path’s time of all modes

is (O

, V

, M

). It is important to mention that

user was proﬁcient and has experience in controlling

hand-operated joysticks like the (M

Figure 5 shows the M

plots of the path traveled

by the PWC for the different velocities and and also

shows the location of the obstacles. The blue path cor-

respond to the global planner whereas the red paths

correspond to the position of the PWC based on the

localization algorithm. The planner is computing new

paths constantly from its current position to the goal

while the PWC is traveling, producing a different ﬁnal

path when compared with the initial one. In this con-

text the PWC is able to follows constantly and quite

accurately the re-planned paths, hence the ﬁnal path.

Figure 6: Average velocity.

Figure 6 depicts the results of the V

pilot test.

It can be seen that the average velocity between

, V

, M

) and (O

, V

, M

) is almost the same

even though the PWC was run in two different veloc-

ities. The reason for this could be that while driving

in (V

, M

), there is a delay for the PWC to react to

follow the initial path allowing the global planner to

constantly computing paths a bit longer than it should

be. The situation changes in M

when there is O

this means that the planner computes a path with less

pronounced curves, making the PWC to follow closer

the constantly computed paths. In the other hand,

, V

, M

) takes slower velocity when compared

with (O

, V

, M

). The fact for this could be that the

initial point was in the right side bottom of the map,

allowing the planner to plan a more straight path, so

the PWC did not face sharp curves as in the later case.

Moreover, (O

, M

) and (O

, V

, M

) show the

slowest and fastest traveled paths of all modes respec-

tively.

Figure 7 shows the plots of the path travel by the

PWC in its independent variables, as in the M

mode

the path traveled by the PWC follow quite accurately

the path planned by the global planner.

The collision outcome corresponding to the de-

pendent variable N

is depicted in Figure 8. It can

be noticed that the PWC touches obstacles just in M

one in (O

, V

), two in (O

, V

) and one (O

, V

)

respectively.

Figure 9 shows the path for the USB gamepad joy-

stick mode M

for different velocities (V

, V

) and

for different obstacles (O

Figure 10 depicts the path travel distance d

outcome, it clearly shows the longest distance

is (O

, V

, M

) as well as the shortest one

, V

, M

Figure 11 shows the path for the PWC joystick

mode M

for different velocities (V

, V

) and for

different obstacles (O

Figure 12 and 13 show the the mean and vari-

ance of the planned paths and the executed ones in

and M

respectively. Whereas Figure 14 depicts

ICINCO 2016 - 13th International Conference on Informatics in Control, Automation and Robotics

250

0 5 10 15 20

−1.5

−1

−0.5

0.5

Path in X [m]

Path in Y [m]

Path of the robot

localization

planning

obstacles

(a)

0 5 10 15 20

−1.5

−1

−0.5

0.5

Path in X [m]

Path in Y [m]

Path of the robot

localization

planning

obstacles

(c)

0 5 10 15 20

−1.5

−1

−0.5

0.5

Path in X [m]

Path in Y [m]

Path of the robot

localization

planning

obstacles

(b)

0 5 10 15 20

−1.5

−1

−0.5

0.5

Path in X [m]

Path in Y [m]

Path of the robot

localization

planning

obstacles

(d)

Figure 7: Semi-autonomous mode (M

); a) fast velocity

with many obstacles. b) slow velocity with many obstacles

c) slow velocity with few obstacles. d) fast velocity with

few obstacles.

Figure 8: Collisions.

−5 0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

USB Joystick

(a)

−5 0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

USB Joystick

(c)

0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

USB Joystick

(b)

0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

USB Joystick

(d)

Figure 9: USB gamepad joystick mode (M

); a) fast ve-

locity with many obstacles. b) slow velocity with many ob-

stacles c) slow velocity with few obstacles. d) fast velocity

with few obstacles.

the tongue joystick mode (M

) with many and few

obstacles.

Figure 10: Distance of the path.

−5 0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

WC Joystick

(a)

−5 0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

WC Joystick

(c)

−5 0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

WC Joystick

(b)

−5 0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

WC Joystick

(d)

Figure 11: PWC joystick mode (M

); a) fast velocity with

many obstacles. b) slow velocity with many obstacles c)

slow velocity with few obstacles. d) fast velocity with few

obstacles.

Figure 12: Mean.

When comparing M

and M

in Figure 4 for dif-

ferent obstacles and time paths’ travel, one can ob-

serve that M

completes all the paths in a slightly

longer time span than M

. This fact can also be re-

ﬂected in Figure 6 where V

in M

is slower than M

The reason for this situation is that in M

mode, a

global map with no obstacles has been used to ﬁnd

a suitable goal in the desired direction which makes

Towards a User-wheelchair Shared Control Paradigm for Individuals with Severe Motor Impairments

251

Figure 13: Variance.

−5 0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

Tongue Joystick

(a)

−5 0 5 10 15 20

−1

−0.5

0.5

1.5

Path in X [m]

Path in Y [m]

Path of the robot

Tongue Joystick

(b)

Figure 14: Tongue joystick mode (M

); a) many obstacles.

b) few obstacles.

the planned path not taking into account the obstacles.

Then, the NS relies only in the DWAP to avoid obsta-

cles towards the goal making the new re-calculated

paths with more pronounced curves and by conse-

quence longer paths. However, according to Figure

8, M

touches more obstacles than M

, this situation

could be because in M

the PWC seeks to go to the

end of the map avoiding obstacles on the way and

not taking into account the obstacles for the initial

path planning as it is done in M

. According to these

two modes, if one wants to go faster and care for not

touching obstacles, the M

is a good choice. When

taking an observation to M

in their three control in-

terfaces M

, M

and M

, Figure 6 shows that the

fastest mode is M

and this is due to the PWC runs

without any computer interface and uses all the mo-

tors’ power, in the other hand the other two modes

runs with a computer interface making them slower.

7 CONCLUSIONS

In this described preliminary study, two environment

setups were used for the pilot tests; a two room lab-

oratory and a corridor shape. The successful results

of the second pilot tests in their independent variables

have established the basis for:

• Preparing the framework for a full factorial de-

sign. For that, there is a necessity to overcome

the issues experienced in the experiments. A sim-

ilar problem experienced in both experiments is

that the planner plans the path too close to the ob-

stacles, especially when it goes through a narrow

doorway or turning around a wall corner. In the

case of the ﬁrst experiment the wheelchair could

not go through a doorway because a ROS secu-

rity plugin canceled the navigation. The solution

to this problem is to develop two ROS plugins; the

ﬁrst one for global voronoi path planning (Garrido

et al., 2006), and the second for escape-security

conditions. In the second experiment the PWC

touched some obstacles, especially in M

. The so-

lution to this is to improve the shared-control al-

gorithm, e.g. by taking into account the map with

obstacles when calculating the path to the desired

direction.

• Testing the system with higher speed. We have

tried to push the navigation to its limits and over

its limits with the actual high velocity with no suc-

cess. However, we believe that the main reason is

that the performance of the system was not opti-

mal. Therefore, an optimal relation between path-

following and high-speed must be found in order

to test the system at higher speed.

• Testing the system in all the independent variables

under challenged conditions, e.g. paths where the

PWC must go through narrow doorways from one

room to another, in cluttered spaces and, also with

dynamic obstacles.

• Making a proper ANOVA analysis using at least 5

participants per control-mode and several repeti-

tions per experimental condition. Randomization

will be necessary.

The actual shared-control strategies have mainly fo-

cused on sharing the control of the PWC to the user in

collision situations. On the other hand, this study has

shown a preliminary optimal distribution of tasks and

has provided the basis for further research on optimal

distribution of high-level control tasks between the

autonomous and the user-controlled modes for dif-

ferent situations depending on the control interface

and the users’ abilities. We believe that this research

will lead to the development of a novel shared-control

paradigm for individuals with severe motor impair-

ments. The capabilities of the shared control algo-

rithms need to be tested in indoor environments. The

abilities and needs of users with high level spinal cord

injury are different from those of spastic users, or

users with ALS (Simpson et al., 2008). Therefore, the

amount of user-control and the amount of automation

required is different for each user and each situation.

This not only depends on the amount and quality of

input the user can give to the system, but also on the

context and the environment that the system is operat-

ing in. Therefore, we also believe that when this new

ICINCO 2016 - 13th International Conference on Informatics in Control, Automation and Robotics

252

paradigm is fully implemented it will help wheelchair

users with severe motor impairments to interact more

efﬁciently with the environment.

ACKNOWLEDGMENT

This work was supported by The Ministry of Educa-

tion, Youth and Sports from the National Programme

of Sustainability (NPU II) project ”IT4Innovations

excellence in science - LQ1602 and by the project

IGA FIT-S-14-2486. It was also performed in col-

laboration between Brno University of Technology,

Czech Republic and H

ector Caltenco who is with the

Department of Design Sciences, Certec, Lund Uni-

versity, Sweden.

REFERENCES

Andreasen, S. L. N. S. (2006). An inductive tongue com-

puter interface for control of computers and assistive

devices. IEEE Transactions on biomedical Engineer-

ing, 53(12):2594–2597.

Caltenco, H. A., Lontis, E. R., and Andreasen, S. L. (2011).

Fuzzy Inference System for Analog Joystick Emula-

tion with an Inductive Tongue-Computer Interface. In

15th Nordic-Baltic Conference on Biomedical Engi-

neering and Medical Physics (NBC 2011), volume 34,

pages 191–194. IEEE, Springer Berlin Heidelberg.

avez, P. A. and Karstoft, H. (2014). Map building based

on a xtion pro live rgbd and a laser sensors. Journal

of Information Technology & Software Engineering.

Christensen, H. V. and Garcia, J. C. (2003). Infrared

Non-Contact Head Sensor, for Control of Wheelchair

Movements. Assistive Technology: From Virtuality

to Reality, A. Pruski and H. Knops (Eds) IOS Press,

pages 336–340.

Cooper, R., Boninger, M., Kwarciak, A., and Ammer,

B. (2002). Development of power wheelchair chin-

operated force-sensing joystick. In [Engineering in

Medicine and Biology, 2002. 24th Annual Conference

and the Annual Fall Meeting of the Biomedical En-

gineering Society] EMBS/BMES Conference, 2002.

Proceedings of the Second Joint, volume 3, pages

2373–2374. IEEE.

Elfes, A. (1989a). A tessellated probabilistic representation

for spatial robot perception and navigation. Proceed-

ings NASA Publications, 3(N90).

Elfes, A. (1989b). Using occupancy grids for mobile robot

perception and navigation. Computer, 22(6):46–57.

Faria, B. M., Reis, L. P., and Lau, N. (2013). Manual, au-

tomatic and shared methods for controlling an intelli-

gent wheelchair: Adaptation to cerebral palsy users.

In: 13th International Conference on Autonomous

Robot Systems and Competitions, pages 26–31.

Garrido, S., Moreno, L., Abderrahim, M., and Monar, F. M.

(2006). Path planning for mobile robot navigation

using voronoi diagram and fast marching. In IROS,

pages 2376–2381. iSBN: 1-4244-0259-X.

Jeonghee, K., Xueliang, H., and Maysam, G. (2008). Wire-

less control of powered wheelchairs with tongue mo-

tion using tongue drive assistive technology. An-

nual International Conference of the IEEE Engineer-

ing in Medicine and Biology Society. IEEE Engi-

neering in Medicine and Biology Society. Conference,

2008:4199–4202.

Jeonghee, K., Xueliang, H., Minocha, J., Holbrook, J.,

Laumann, A., and Maysam, G. (2012). Evalua-

tion of a smartphone platform as a wireless interface

between tongue drive system and electric-powered

wheelchairs. IEEE Trans. Biomed. Engineering,

59(6):1787–1796.

Jiding, D., Zhijun, L., Chenguang, Y., and Peng, X.

(2014). Shared control of a brain-actuated intelligent

wheelchair. pages 341–346. IEEE.

Lund, M. E., Christiensen, H. V., Caltenco, H. A., Lontis,

E. R., Bentsen, B., and Andreasen, S. L. N. (2010).

Inductive tongue control of powered wheelchairs. In

Proceedings of the 32nd Annual International Confer-

ence of the IEEE Engineering in Medicine and Biol-

ogy Society, pages 3361–3364. Isbn = 978-1-4244-

4123-5.

Moravec, H. and Elfes, A. (1985). High resolution maps

from wide angle sonar. In Robotics and Automation.

Proceedings. 1985 IEEE International Conference on,

volume 2, pages 116–121. IEEE.

Petry, M., Paulo, A. M., Braga, R., and Paulo, L. R. (2010).

Shared control for obstacle avoidance in intelligent

wheelchairs. in IEEE Conference on Robotics, Au-

tomation and Mechatronics, pages 182–187.

Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T. B.,

Leibs, J., Wheeler, R., and Ng, A. Y. (2009). ROS: an

open-source robot operating system. In ICRA Work-

shop on Open Source Software.

San Agustin, J., Mateo, J., Paulin, H. J., and Villanueva,

A. (2009). Evaluation of the potential of gaze input

for game interaction. PsychNology Journal, 7(2):213–

236.

Simpson, R. C., LoPresti, E. F., and Cooper, R. A.

(2008). How many people would beneﬁt from a smart

wheelchair? Journal of Rehabilitation Research and

Development, 45(1):53–72.

an, P., Kulic, M., and P

reu

cil, L. (2005). Robust data

fusion with occupancy grids. In IEEE Transactions

on Systems, Man, and Cybernetics, pages 106–115.

IEEE.

Torsten, Felzer and Rainer, Nordman (2007). Alternative

wheelchair control. In Proc. Int. IEEE-BAIS Symp.,

Res. Assistive Technol., pages 67–74.

Xueliang, H. and Maysam, G. (2010). Evaluation of a wire-

less wearable tongue-computer interface by individ-

uals with high-level spinal cord injuries. Journal of

Neural Engineering, 7(2). ISBN: 17412560.

Towards a User-wheelchair Shared Control Paradigm for Individuals with Severe Motor Impairments

253