Haptic feedback control of a smart wheelchair

Applied Bionics and Biomechanics 9 (2012) 181–192DOI 10.3233/ABB-2012-0067IOS Press

181

Haptic feedback control of a smartwheelchair

Mohammed-Amine Hadj-Abdelkadera,b,∗, Guy Bourhisa and Brahim Cherkib,∗aLaboratoire d’Automatique Humaine et de Sciences Comportementales LASC, Universite Paul Verlaine,Metz, FrancebLaboratoire d’Automatique de Tlemcen LAT, Universite Abou Bekr Belkaıd, Tlemcen, Algeria

Abstract. The haptic feedback, which is natural in assistive devices intended for visually impaired persons, has been onlyrecently explored for people with motor disability. The aim of this work is to study its potential, particularly for assistance in thedriving of powered wheelchairs. After a review of the literature for the previous related work, we present the methodology andthe implementation procedure of a haptic feedback control system on a prototype of a smart wheelchair. We will also describethe approaches utilized to determine the appropriate force feedback that will ensure a cooperative behaviour of the system, andwe will detail the two haptic driving modes that were developed, namely the active and passive modes. Experiments on a realprototype were carried out to study the contribution of the method in powered wheelchair driving and to evaluate the interest ofthe force feedback on the control joystick of the wheelchair. They are discussed on the basis of performance measures.

Keywords: Smart wheelchair, motor disability, haptic feedback, human machine interaction, cooperative systems, laser sensor

1. Introduction

This work lies within the general scope of assis-tive technologies for disabled persons. Within thisframework, the human–machine interaction is anessential feature and must be taken into accountfor every type of functionality (mobility assistance,manipulation aid, communication aid . . . ). In fact,as in every human–machine system, a dialoguemust be established between the controlledsystem and the user. However, for persons withdisabilities, this dialogue through conventionalchannels may become difficult, or even impos-

∗Corresponding author: Mohammed-Amine Hadj-Abdelkader,Laboratoire d’Automatique Humaine et de Sciences Com-portementales LASC, Universite Paul Verlaine, 7 rue Marconi,Metz Technopole 57070, France. E-mails: ma [email protected],[email protected] and Brahim Cherki, Laboratoired’Automatique de Tlemcen LAT, Universite Abou Bekr Belkaıd,BP 230, Tlemcen 13000, Algeria; E-mail: B [email protected].

sible, because of their motor and/or sensorydeficiencies.

Three senses are naturally requested for the infor-mation feedback towards the user: vision, hearingand touch. This last one is broadly defined as“haptic” by including tactile and proprioceptive infor-mation (perception of our body, limbs position. . . )and kinaesthetic (feeling of the limbs movement).A sensory deficiency can be counterbalanced bythe assistive device by calling upon another sense.For example, a mobility aid for blind people willuse the haptic sense (blind cane) or the auditorysense (a telemetric cane that returns a sonic image ofthe near environment).

The transmission channel from the user to the assis-tive device may also be defective because of themotor deficiency of the person. We can then, in somecases, use an alternative to the classical muscle con-trol by calling upon a voice recognition device. Thisassumes that the voice of the person is sufficiently

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182 M.-A. Hadj-Abdelkader et al. / Haptic feedback control of a smart wheelchair

reproducible and understandable. However, this con-trol mode allows only for discrete commands. It isalso possible to enhance and strengthen the motorcontrol by haptic information at the human–machineinterface: force feedback or tactile information. Wepresent here a study of the potentialities of haptic feed-back for assistive technologies intended for peoplewith motor disabilities. We are especially interestedin powered wheelchair control. Our matter is basedon real experiments of powered wheelchair drivingusing the Microsoft force feedback joystick Side-Winder 2™.

The composition of this paper is as follows. In Sec-tion 2, we present a review of the related work inthe literature. In Section 3, the methodology of theproposed approach is described. The experimentationprocedure and the results are then presented and dis-cussed in Section 4.

2. Related work

Many research works about the teleoperationassisted by haptic feedback are described in the lit-erature. These applications concern only users withoutdisability but their conclusions could be extended toconfirm the potential of the method. Thus, accord-ing to Sheridan, the force feedback in teleoperationis essential for some tasks but not for others [19].For example, for a position control in free space, andparticularly when the movements are slow, the hapticfeedback has little advantages. It is however essen-tial in handling or assembly tasks. In [2] simulationexperiments were carried out for the teleoperation ofa mobile base in a hostile environment. The authorsfound a significant decrease in the number of colli-sions by using a force feedback joystick compared to aconventional joystick. Nevertheless, the duration andlength of paths were only slightly different from onesituation to another. A similar experiment in [13] car-ried out using the 3D haptic device PHANToM™ ofSensAble, limited to 2D, leads to the same conclusion:the haptic feedback decreases the number of collisionswithout increasing significantly the navigation time.However, performances measures are not enough toconfirm the benefits of haptic feedback: in [12], mea-sures of mental workload during the teleoperation ofa helicopter show that some force feedback functionsimprove performances but significantly increase themental workload.

2.1. Haptic in rehabilitation engineering

The first applications of haptic in rehabilitationengineering have been related naturally to the com-pensation of visual impairments, particularly with theinvention of the Braille alphabet in the 19th century.Software interfaces associated with force and/or tactilefeedback have appeared much more recently allowinga haptic rendering and sensations of forms designed ona computer screen. Applications of this principle arevery varied: exploration of geographical charts, electri-cal circuits, mathematical curves, graphic documentson the web [1, 21]... The force feedback has also beenused for mobility assistance for persons with multipledisabilities, combining visual and motor impairments:the SPAM prototype (Smart Power Assistant Module)increases the resistance of the pushrims of a manualwheelchair according to the obstacles proximity [20].In the same way, the powered wheelchair describedin [14] returns information about the environment but,this time, through a force feedback joystick.

In the field of assistive technologies for persons withmotor disabilities, the haptic feedback has becomea subject of study only recently. Let’s notice thatthe force feedback is less natural in this applicationcontext. Two issues seem to capture the researchers’attention. The first relates to the access to computerfor people with motor disability, who experience trou-bles when using conventional pointing devices such asthe mouse. Experiments on a panel of persons withdisabilities have shown that a force feedback inter-face could significantly improve the performances ina pointing task [10]. These results are corroboratedby a study described in [17] bearing on a group of10 persons with motor disability. Moreover, the mod-elling of the coupling between the user and the hapticinterface has shown that it is thus possible to removethe pathological tremor of some disabled persons [15].The second issue, to which our work adheres morespecifically, relates to the driving assistance of poweredwheelchairs.

2.2. Haptic and powered wheelchairs

For many potential users, powered wheelchair con-trol remains indeed difficult, or even impossible,because of their severe motor disability [7]. Someresearch teams have endeavoured to remedy this sit-uation by developing “smart” wheelchairs [4]. The“intelligence” of the wheelchair can be defined as

M.-A. Hadj-Abdelkader et al. / Haptic feedback control of a smart wheelchair 183

the ability to perceive the external environment andto retrieve relevant information in order to carry outautonomous or semi-autonomous movements: obsta-cle avoidance, doors passing, docking, automaticcourses . . . However, the wheelchair control in auto-matic mode brings up two major problems: a technicalproblem and a psychological problem. From a tech-nical viewpoint, a perfect reliability of an automaticmotion involves the use of a variety of sophisticatedenvironmental sensors and a heavy data-processingthat are not compatible with the cost requirements ofsuch an application. From a psychological viewpoint,many potential users, on one hand, are apprehensiveto let the whole movement control to the machine, andon the other hand, want to use their residual drivingabilities as much as possible. A method to overcomethese two problems is to introduce a force feedbackon the control joystick. This force feedback is func-tion of the obstacles proximity. We can then talk aboutan “assisted” control mode: the wheelchair controlis entirely under the sole responsibility of the user,the machine, as a movement supervisor, provides himsimply with haptic information to enhance the natu-ral visual feedback. Within this context, the technicaland psychological limitations of the automatic mode donot appear anymore. However, it remains to be demon-strated that the driving performances will be improvedto a significant degree compared to the conventionaldriving mode of powered wheelchairs.

Few works of this type are referenced in theliterature. In [6] a joystick was specifically designed totest in a fully modelled environment a “passive” forcefeedback algorithm (the joystick resists to movementstowards obstacles) and an “active” force feedback algo-rithm (the joystick moves the wheelchair away fromobstacles). The “active” algorithm has proved to bemore effective. It has been tested using a virtual realitysystem on 5 persons with disability [16]: for 4 of them,the number of collisions in a test course has decreased,compared to driving without force feedback. In [9], theauthors describe an “active” type algorithm basedon the modified potential field method: to overcomethe difficulty of passing doors with this method, theauthors consider only the obstacles lying within ±30◦from the wheelchair moving direction to calculate therepulsive force. Note that the authors have only tested asimple door passing task with this algorithm on a com-puter simulated mobile base. However, the applicationof this method on a real wheelchair will be quite differ-ent because, first, of the real experimenting conditions

that include many constraints such as wheel slidingand the wheelchair’s big dimensions. And, second,the fact that there is a person sitting on the wheelchairwill add an extra inertia to the system which willcompletely change its dynamic characteristics. Theseworks were all performed by computer simulation,except for [6] who tested the wheelchair’s drivingperformances using only dead reckoning measures ina previously mapped and fully modelled environment.Thus, our goal is to investigate the contribution ofhaptic feedback to the driving performances in realdriving conditions, with the use of environmentalsensors and without pre-programmed paths.

3. Methodology

3.1. Introduction

Our aim is to design a new control mode for poweredwheelchairs based on haptic feedback: depending onobstacles proximity, we apply a force on the device tohelp the pilot moving towards the closest free space tothe direction he has chosen. This requires the use oftelemetry sensors on the mobile base: for this purpose,we used a prototype of smart wheelchair developed inthe VAHM project [4] that is outfitted with a Hokuyo™laser rangefinder allowing obstacles detection within a240◦ sector and up to 4 meters away. This sensor islocated at the front of the wheelchair at about 30 cmfrom the floor. It can sense all the obstacles on hishorizontal plane and thus give an idea about the shapeof the environment. However, if some obstacles aremissed, it is not critical for the task in hand since weare working on driving assistance instead of the entirelyautomatic motion, the driving security being indeedunder the control of the driver in all cases.

Given the difficulty of performing driving tests ona real wheelchair for people with disabilities, it ispreferred to achieve these experiments on a drivingsimulator. Thus, we initially conducted a series of sim-ulation experiments with a panel of persons withoutdisability. The results are described in [5, 18]. We par-ticularly noticed that in certain environmental areas,such as door passages, the use of a force feedbackimproves the driving performances.

In the second step, we implemented this method onthe VAHM wheelchair to evaluate it in real drivingconditions. This implementation and the experimentresults are presented in the following sections. These


Fig. 1. The haptic control scheme of the system.

experiments on the real prototype allowed us to per-ceive the limitations of the previously developedsimulator and will help us improving it. This way, wewill get a reliable tool to analyze this technique withpersons with disabilities.

Figure 1 shows the haptic control scheme of thesystem. The main steps of this control process can besummarised in the following points:

• First, the user applies a motion on the joystick inorder to command the wheelchair.

• The magnitude and the orientation angle of thismotion are then retrieved from the joystick andsent to the force computation algorithm which willuse them as a set point for his calculations.

• Along with the sensor measurements, the algo-rithm determines the free directions in theenvironment, and applies a force on the joystickdepending on the obstacle settings and on theselected driving mode.

• A haptic feedback is perceived by the user, whowill adapt his motion on the joystick either by fol-lowing the algorithm’s chosen directions if theymatch his intentions or by adapting his force tokeep the same heading even though it is not asuitable algorithm’s choice. This may happen, forexample, in the case the user wants to dock at atable; the algorithm in this case will tell him thatthis may not be a free direction, without prevent-ing him from achieving his goal.

Notice that, in a haptic control process, the com-putations should be performed at a relatively highfrequency in order to ensure a continuous interactionwith the human. Thus, the algorithms that will be usedmust not require heavy calculus nor handle a hugeamount of data.

The haptic information in this context could be veryuseful for some persons with motor disability who havedifficulties moving their heads and cannot see all theobstacles in the environment. The advantage of thiscontrol method is that the control device is bilateral:it allows the user to send commands and to perceiveinformation at the same time. So the user does not needto switch his attention between several components todrive the wheelchair.

3.2. Force feedback algorithm

We used a grid-based method to compute the feed-back forces. We followed the same steps as in theVFH method introduced by [3]. The VFH method wasinitially used for obstacle avoidance for autonomousmobile robots outfitted with ultrasonic sensors anddead reckoning. It allows taking account of the sen-sor’s measuring errors by filling a Cartesian grid withcertainty values. These values match the actual posi-tions of obstacles. In this approach, we adapted theVFH method to the utilisation with the laser sensor,by computing an obstacle density within each cell ofthe Cartesian grid instead of certainty values, and wedid not use the dead reckoning process to calculate thewheelchair displacement because of the biased esti-mation it induces. We also wanted our system to beadaptable on every type of powered wheelchairs andthus, we tried to make it less dependent of additionalsensors. Furthermore, dead reckoning systems are noteasy to implement on wheelchairs. Consequently, theCartesian grid in our method will be reset and filled upover again at each control loop cycle.

In addition, the Cartesian grid was centred at thelaser sensor’s position instead of the wheelchair centrepoint to reduce the grid’s uncovered area. However, atthe end of the algorithm, the resulting directions will


be converted to the wheelchair’s control frame centredat the midpoint of the rear wheels axis. For this imple-mentation, we chose a grid of 50 × 50 dm2 divided intocells of 1 dm2 each as shown in Fig. 2.

In each control loop cycle, we compute the forcefeedback as follows:

1) Reading measurement values from the laserrangefinder,

2) Filling up the Cartesian grid by incrementing thevalues of the cells that match an obstacle position.If there are many obstacle detections within thesame cell, then its value will be incremented tomatch the number of detections. This allows us tohave an indication of the obstacles density withineach cell (Fig. 3),

3) Then we apply the potential field principle [11] tothe grid, i.e. we suppose that each cell will apply arepulsive force on the wheelchair; the force mag-nitude is given by the formula (1), where c (i, j)is the density value associated with the cell (i, j),d (i, j) is the distance between the sensor and thecell, a and b are the coefficients we use to get azero magnitude for the farthest cell.

m(i, j) = c(i, j). (a − b.d(i, j)) (1)

i

j

Laser Sensor

Unknown Area

5 m

5 m

Fig. 2. The Wheelchair and the laser sensor in the Cartesian grid.

4) The grid is then split into 5◦ sectors, as shownin Fig. 4. For each sector we compute the polarobstacle density that is the sum of the values ofall cells within the same sector. We get thus apolar histogram of polar densities. To avoid largedensities variations in the histogram, we applythe smoothing function (2), where h(i) is the polarobstacle density for the ith sector.

h(i) = 3h(i − 2) + 4h(i − 1) + 5h(i) + 4h(i + 1) + 3h(i + 2)

19(2)

5) In the polar histogram, there are peaks showingthe obstacles directions and valleys representingthe free directions in the environment (Fig. 6).This distinction is made using an empiricallychosen threshold. Notice that the selected val-leys should be large enough in order to match thespaces that the wheelchair can easily get through.Hence, the valleys must be at least 25◦ large (i.e.hold at least 5 sectors), which corresponds to a0.9 m door passage at the farthest limit of themeasured area on the Cartesian grid.

After the valleys detection, two different ways wereused to send the force feedback to the joystick:

• Active force feedback: the force direction is themiddle of the closest valley to the user’s chosendirection. However, for each user the force mag-nitude should be adjusted. For safety reasons, thewheelchair motion must be started by the triggerof the joystick. This is due to the use of a position-sensing joystick that cannot sense forces. The usercan stop the wheelchair simply by releasing thetrigger.

• Passive force feedback: in this case, the joystick ishold at its central position with a medium strengthresistive force. When the user drives towards a freedirection, the force magnitude is reduced progres-sively to a fixed level, depending on whether theuser is at the centre or at the border of the valley.However, when the user drives towards an obsta-cle (indicated by a peak on the histogram) then theresistive force magnitude is increased to push theuser away to the free space. The wheelchair canbe stopped by releasing the joystick (no trigger isneeded).


Fig. 3. Cartesian grid filled up with certainty values, displayed using greyscale.

120°

240° 0°

Fig. 4. Cartesian grid split into sectors.

3.3. Force sending method

After determining the resulting direction in the envi-ronment by the previously shown algorithms, a force

is sent to the joystick in that direction. However, thejoystick allows for many types of force feedback andwe must find the most convenient way to send forcesin order to ensure a cooperative behaviour, especiallyfor the active mode. We found first that sending a con-stant force to the device does not allow the driver tohave the entire control on the machine and that thejoystick actions have become tough. A constant forceon the joystick is indeed very hard to counter and isnot a suitable solution for this kind of systems. Aftermany trials, we agreed that the best way to apply forcesis to produce a ‘spring’ effect on the joystick. Thus,the chosen direction will be the rest zone of a springof a well adjusted stiffness. This way, the user willbe able to choose easily another direction if he wantsto, and will perceive enough strength on the joystickto figure out the positions of obstacles. Note that thespring effect stiffness was adjusted during the trailruns.

This type of effect was also applied to the pas-sive mode because it allows the wheelchair controlto be stable (i.e. it returns to a steady position when


Fig. 5. Example of force application in the active mode.

there is no action on the system’s actuator). However,an inertia effect was also manually implemented tomake the spring’s resistance appear gradually accord-ing to whether the applied direction is close to thealgorithm’s choice or not. Therefore, the system’sbehaviour will be entirely cooperative and will allowthe user to have the full control on the wheelchair.Figure 5 show an example of force application in theactive mode.

We also applied some control on the joystick’smotion amplitude, such that the wheelchair’ speed inthe chosen direction will depend on the distance toobstacles in that direction. Thus, the speed of the vehi-cle will be at the maximum when the space in thedirection of motion is entirely free, and it will decreaseprogressively if the sensor detects some obstacles infront of the wheelchair. Then, when the obstaclesbecome too close, the algorithm will switch to anotherdirection (supposed to be free) or will not send anyhaptic feedback if no free space is perceived.

4. Experimentation and results

To evaluate this approach, we asked a group of 10valid and healthy users (9 men and 1 woman) to drivethe wheelchair in an experimental environment, madeup of 3 areas, involving different driving behaviours.Users were first given some trial runs, and then theystarted driving the wheelchair through the 3 areas.Each user began by driving without force feedback.After that, the force feedback was enabled and theuser drove again using successively the passive andactive algorithms. For all the users, the experimentswere held on different sessions and distributed overseveral days in order to avoid biasing the results bylearning effects.

To evaluate the driving performances, we recordedthe course completion time (without counting the stop-ping time) and the number of collisions for each zoneand each driving mode. The results presented here arethe averages (for the completion times) and the sums


Fig. 6. The polar histogram and the valleys detection.

(for collisions) of measures taken on 4 courses for eachuser and each mode. For the active and passive forcefeedback algorithms, a record of the resulting direc-tion and of the resistive force magnitude, respectively,allowed us to measure the algorithm’s “follow-up rate”.This rate is the percentage of driving time during whichthe user was applying a direction that belongs to thevalley chosen by the algorithm. This will help us toassess the algorithm’s accordance with the user choicesand will allow us to estimate their efficiency. In addi-tion, we used the TLX – Task Load Index – method [8]to evaluate the drivers’ workload in each mode. Thissubjective questionnaire includes a user’s estimationof the levels of concentration he reached, of the physi-cal effort he spent, of the temporal constraints and thefrustration degree he felt, and of the performance’ levelhe accomplished. Each of these ratings is given by theuser himself, on a scale from 0 to 100%, accordingto his own appreciation. The higher will be the work-load, the tougher was the driving task. Hence, we candetermine the less laborious mode for the users.

For the active force feedback algorithm, more train-ing was needed in order to adjust the force magnitudefor each user. The tuning was made by varying theforce magnitude, corresponding to the spring effectstiffness, till we get a value that allows for reasonableand relevant feedback. This adjusted force magnitudeshould help the user in the navigation task withoutforcing him to an unwanted direction or getting himfrustrated. Notice that the obtained level of force wasapproximately the same for all the users.

The first zone of the course (Fig. 7) is made up ofa 180◦ corners combined with door passages of 0.9 mwide. Moreover, the corridors of this zone are relativelynarrow (1.35 m, 1.4 m, 1.5 m) which makes the passingof corners very difficult. Notice that the wheelchair isabout 1.2 m × 0.8 m and that it needs a large manoeu-vring area to steer correctly. The user starts drivingfrom point D1 and travels to the point A1. The resultsfor this area are shown in Table 1. We notice firstthat the completion times for the passive and activemodes were shorter than the completion times for the


A1

D 1

Fig. 7. First experimental zone.

A2

D2

Fig. 8. Second experimental zone.

normal (without force) mode. An ANOVA analysiswith a 0.05 alpha-level confirmed this observation. Wenoticed also that the number of collisions was reducedwhen the force feedback was applied, especially forthe active algorithm. For the passive and active forcefeedback modes, the users admitted that the wheelchairdriving was easier in the cluttered areas because theywere able to recognize straightforwardly the free direc-tions and to move through. The follow-up rates showthat the compliance between the user and the algorithmwas average in this zone.

The second zone of the course is mainly made upof 2 corridors of about 1.8 m wide, with 3 door pas-sages of 0.9 m wide and some obstacles impedingmanoeuvres nearby (Fig. 8). The starting point is D2,

D3

A3

Fig. 9. Third experimental zone.

Table 1Time averages, collisions and follow-up rates for the first zone

User Driving mode Times (s) Collisions Follow-uprates (%)

Without force 40.38 21 Passive 35.83 1 60.34

Active 32.76 0 66.27Without force 57.75 5

2 Passive 47.82 4 50.27Active 35.58 2 61.70Without force 40.54 4








10 Passive 41.91 2 46.93Active 38.45 1 51.90


Table 2Time averages, collisions and follow-up rates for the second zone


Without force 51.42 01 Passive 47,02 0 84.00










10 Passive 52.40 0 71.40Active 51.77 0 56.94

the finishing point is A2, and the results are shown inTable 2. The shape of this zone was less complicatedthan the others, and thus the driving performances werenearly the same for all the users and all the modes.There were though some collisions in the normal andpassive mode. We can particularly notice the highfollow-up rates which indicate a good efficiency of theforce feedback algorithms in finding free directions.According to the users, the level of frustration in thiszone was less than in the others because there were notcomplicated situations.

The third zone of the course is smaller than the oth-ers, but it is mostly made up of 2 narrow passages(about 0.85 m wide) that are difficult to cross (Fig. 9).D3 is the starting point and A3 is the endpoint. Thereis also a door passage of 1.5 m wide that is hard to getthrough because it is located just after the first narrowpassage. The results of this zone are shown in Table 3.As in the first zone, we found a decrease in the com-pletion times that was statistically confirmed by a 0.05

Table 3Time averages, collisions and follow-up rates for the third zone


Without force 17.44 11 Passive 16.04 1 69.84










10 Passive 19.53 1 45.87Active 18.66 0 47.78

alpha-level ANOVA analysis. The number of collisionswas also reduced for the active mode and the follow-uprates were still close to the average.

Table 4 shows the results for the entire experimentalenvironment. We can see that there were less colli-sions with the active force feedback algorithm thanwith the others, as shown in the former works shownby [5, 6]. The follow-up rates were above the aver-age most of the time. We should notice that severalcomplicated shapes of the environment have neededmany manoeuvres in order to avoid collisions. How-ever, these constraints were not taken into account inthe obstacle avoidance algorithm and generated situa-tions where the resulting direction was not matchingthe user’s choice. Notice also that a valley representsonly 25◦ among the possible 360◦, which is less than10% of the possible directions. This explains why thefollow-up rates were not very high.

During these experiments, we used the TLX ques-tionnaire to evaluate the drivers’ workload at the end of


Table 4Time averages, collisions, follow-up rates and workloads for the

whole itinerary

User Driving mode Times (s) Collisions Follow-up Workloadrates (%) (%)

Without force 109.24 3 38.801 Passive 98.89 2 71.39 41.80

Active 94.91 0 72.02 24.73Without force 139.35 7 49.73

2 Passive 121.35 5 59.89 64.47Active 100.95 2 67.53 42.07Without force 106.18 6 64.40








10 Passive 113.84 3 54.73 13.73Active 108.87 1 52.21 24.07

each series of tests. At the beginning the active modeseemed to be heavier than the others. But once theusers were used to it, they felt more comfortable withthis mode and gave it a good appreciation as we cansee in Table 4 where, in most cases, the active modehas got smallest workload values. The force feedbackhas clearly brought a good contribution to the naviga-tion task in different ways: the active mode allowedto reduce errors and to make the driving smoother,whereas the passive mode helped to decrease the armtiredness for the drivers because of the adapted resistiveforce.

We tried also in these experiments to record the joy-stick commands in order to analyse the driver’s actionson it and to compare the different driving modes interms of comfort. We recorded thus the joystick mag-nitude and the angle differences at each control cycleand took the highest angular difference for each exper-iment. However, the results were almost the same in

all the situations which leads us to suppose that therewere no changes in the driving behaviour for thesethree modes.

Finally, we should note that the grid-based methodfor the determination of force feedback was initiallyused for small and fast mobile robots that do not needany manoeuvres during their motion. Moreover, thisobstacle avoidance method does not include any pathplanning technique, thus the driver was supposed tohandle the manoeuvres by himself. Nevertheless, thismethod allowed us to get an immediate response fromthe control system and was very useful once the goodwheelchair orientation was found.

5. Conclusion

The experiments presented here were carried outto evaluate the contribution of force feedback in thewheelchair driving task. Although we could not carrythem out with users with disabilities for safety reasons,they were still informative on the force feedback con-tribution to the driving enhancement in real conditions.Thus, they allowed us also to perceive the shortcom-ings and limits of the previously designed simulatordescribed in [5, 18]. Hence, we acquired a good knowl-edge to improve this simulator in order to performexperiments with disabled persons. With such a tool, abetter evaluation of the technique could be achieved, aswell as the evaluation of other works intended to assistand help people with motor disabilities. We think alsothat the choice between the passive and active feed-back should be given to the user who can select themost convenient mode for himself. A good future per-spective would be the use of other haptic devices thanthe classical joystick and to compare these differentdevices in terms of performances, acceptability andworkload. Finally, all these approaches should respectthe basic ergonomic and usability rules and should alsobe accepted by the conventional wheelchair users inorder to find their place among the currently availablerehabilitation techniques.

Acknowledgements

The authors would like to thank the LASC Lab-oratory members who contributed to this work byperforming the experiments on the wheelchair.


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