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IEEE SENSORS JOURNAL, VOL. 16, NO. 17, SEPTEMBER 1, 2016 6755

IR Sensor-Based Gesture Control Wheelchair forStroke and SCI Patients

Rajesh Kannan Megalingam, Senior Member, IEEE, Venkat Rangan, Sujin Krishnan,and Athul Balan Edichery Alinkeezhil

Abstract— This paper presents a novel and simple hand gesturerecognition method to be used in rehabilitation of people whohave mobility issues particularly stroke patients and patients withspinal cord injury (SCI). Keeping in mind the reach of such asystem for a wider community of people with mobility issues,the proposed low-cost control device called gpaD—gesture padprovides an alternative solution to the joystick-based poweredwheelchair control through hand gestures. In this method, IRsensors are used for identifying the simple gestures to control thepowered wheelchair to move in any direction. In the proposedprototype system HanGes, a gesture pad that includes IR sensors,MCU and power management circuit is designed for gesturerecognition and identification and a controller for driving motorsis implemented. HanGes’s design, implementation, the responsetime calculations of the system, testing, performance evaluationwith stroke and SCI patients are discussed in detail. With theaverage success rate of gesture recognition above 99.25% andresponse time as comparable with that of commercially availablejoystick controlled wheelchair, HanGes could be a possiblealternative to the existing ones. With extensive experiments thatdemonstrate the accuracy of the system, the user experience,testing with patients, and the implementation cost indicate thesuperiority of our system.

Index Terms— Wheelchair, IR sensor, navigation, gesture,stroke patients, SCI patients, elders.

I. INTRODUCTION

GESTURE recognition methods and systems are vast innumber which are based on MEMS based accelerom-

eters [2], [4], Kinect sensors [19], Ultrasonic sensors [3],Vision sensors [38], laser based [39] etc. Several dif-ferent algorithms and techniques including pattern match-ing [26]–[28], histograms [44], [45], graph matching [41],fuzzy based [7], [13], [42], neural networks [14], [16], [43],HMM based [24], [25], [29], [30], FSM based [31], [32] etc.are used for gesture identification. Glove based gesture

Manuscript received September 4, 2015; accepted June 21, 2016. Date ofpublication June 28, 2016; date of current version August 3, 2016. The projectis funded by Amrita Vishwa Vidyapeetham, Amrita University. The associateeditor coordinating the review of this paper and approving it for publicationwas Dr. Akshya Swain.

R. K. Megalingam is with Humanitarian Technology Labs, Electronicsand Communication Department, Amrita Vishwa Vidyapeetham University,Kollam 690525, India (e-mail: [email protected]).

V. Rangan is with Amrita Vishwa Vidyapeetham University,Kollam 690525, India (e-mail: [email protected]).

S. Krishnan was with Amrita Vishwa Vidyapeetham University,Kollam 690525, India. He is now with Infosys, Pune 695583, India (e-mail:[email protected]).

A. B. Edichery Alinkeezhil was with Amrita Vishwa Vidyapeetham Univer-sity, Kollam 690525, India. He is now with the University of New Mexico,Albuquerque, NM 87131 USA (e-mail: [email protected]).

Digital Object Identifier 10.1109/JSEN.2016.2585582

device [43] uses sensors that are attached to the glove forthe users to wear and use. There are camera and Kinect basedgesture devices where a user might still be expected to wear aglove with markers. Gesture based recognition and identifica-tion finds wide use in robot control, video gaming, text editing,vehicle system control, HCI, multimedia interfaces etc. Mostof the gesture control mechanisms are targeted for gamingapplications which uses complex gestures.

Of more than fifty commercially available powered wheel-chairs we studied we could summarize the features as:Collapsible for Transport, Joystick Mount Adjustable, Arm-rest Position Adjustable, Seat to Floor Height Adjustable,Head Rest Available, Head Rest Adjustable, Tilt Adjustable,Footplate Height Adjustable, Backrest Position Adjustable,Frame made of various metals and alloys, Spring SuspensionAvailable etc. The cost ranges from USD 1500 to USD 9500.Some customized wheelchairs are even priced at higher thanUSD 9500. There are also sub-features like joystick can bemounted on either side, backrest position can be tilted to45 degrees / infinite tilting, seat to floor adjustable range etc.The most interesting thing to note here is that the controlmechanism used in all these powered wheelchairs. All of themuse only joystick control for navigation. While this mightbe treated as a standard accepted worldwide in more thana decade, it is significant to note here that most wheelchairproviders are not looking into providing alternative controlmethods for wider variety of users. This makes the scope ofthe joystick control method restricted only to users who canexert certain amount of force to push it in any direction.

The existing methods like head gesture based, hand glovebased, chin based control mechanisms are uncomfortable tothe stroke and SCI patients and the user is expected to wearthem in one form or other and/or undergo training in usingthem. Any mechanism must be user friendly and comfortablefor the users and the proposed hand gesture based controlmechanism proves to be simple and user friendly which usesonly five hand gestures for the wheelchair control. In additionthe suggested hand gestures are very natural to the users.Too many hand gestures might leave the users confused,particularly when it comes to stroke and SCI patients, whereextreme care must be taken to ensure the safety of the users.

Hand gesture to control a powered chair is not a commonone and even in those few research works published earliercamera based hand gesture recognition [46]–[48] is used toidentify the gesture which requires a complex system to do theimage processing and there by affecting the cost. There is also

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6756 IEEE SENSORS JOURNAL, VOL. 16, NO. 17, SEPTEMBER 1, 2016

Fig. 1. HanGes complete system.

an accelerometer based hand gesture recognition discussed ina research work [49] for wheelchair control method. But thisone is only suitable for users who have control over theirhands and also not field tested. Our earlier work which waspublished uses IR Camera and Intel Processor board for handgesture image capture, recognition and identification [33].

In this research paper we present a novel approach in gesturecapture and identification which is very simple. Our surveyshows that this approach has the potential to replace the currentconventional joystick based control in powered wheelchairswhich can be made available to the users at a lower cost.It can also be used by wider variety of users including eldersand physically challenged other than stroke and SCI patients.Figure 1 shows HanGes wheelchair complete system.

We did an initial survey at the Physical Medicine andRehabilitation Department of Amrita Institute of MedicalSciences, Amrita Vishwa Vidyapeetham University, Cochin,Kerala, India about the hand gesture based wheelchair. Thefollowing questionnaire was given to the doctors at the depart-ment.

1. How many patients visit your clinic/hospital in a day towhom the wheelchair is suggested?

2. While prescribing a wheelchair what all factors do youconsider in mind?

3. Do people ask for the type of wheelchair or do yousuggest based on their physical condition?

4. Has wheelchair got any role in skin breakdown?5. How many people switch on to manual wheelchair even

when they are suggested to use automated wheelchairs?6. On what criteria do you suggest a wheelchair to your

patient?7. What do you suggest your patient to buy or rent a

wheelchair?8. How often do you suggest them to change the wheelchair?9. What type of wheelchair do you suggest to your patients

at present?The summary of the answers to the questionnaire is pro-

vided here. The wheelchair is suggested to at least threepatients daily who visit this department which puts totalnumber of patients for whom wheelchair is suggested, at1095 every year. While prescribing the wheelchair the doctorsconsider primary diagnosis, co-morbiditis, functional status,

goal, socio-economic status, and potential for return to workas the primary factors. The doctors suggest the patients whattype of wheelchair is best for them. The prevailing wheelchairs(joystick based) are unsatisfactory for the population (strokepatients and SCI patients) that the doctors treat. For patientswith severe quadriplegia it takes time to recover some motorcontrol in their hands. HanGes can be used only by thosepatients who are able to move their hands back and forth toa certain extent without external help. There might be heatproduced because of the battery underneath the seat whichmight cause damage to the skin of the user who are seated.As the existing powered wheelchairs with joystick controlare not suitable for the patients whom the doctors treat, theysuggest to buy only a manual wheelchair. They rarely suggestto change the wheelchairs. The answers only point to thefact that the existing joystick based control mechanism is notsuitable for stroke patients and SCI patients and that which issuitable for them is not available in India.

Any other control method other than joystick based, isnot a worldwide standard and is very costly even if it isavailable at few places, which is not affordable to manyusers. In developing nations and underdeveloped nations mostSCI patients and stroke patients who are in need of poweredwheelchairs couldn’t buy one, because they are not availableor have to imported at a huge cost. For example in India,a joystick based powered wheelchair is the only option andpriced anywhere in the range of USD 1200 to USD 5000. Thisis very costly for people of middle or lower class sections ofthe society who would need a powered wheelchair.

II. BACKGROUND

According to WHO every year, between 250,000 and500,000 people suffer a spinal cord injury (SCI) all over theworld. As per WHO, physical barriers to basic mobility is oneof the three factors that result in the exclusion of many peoplewith SCI from full participation in society. For improving careand overcoming health, social and economic barriers of peoplewith SCI, WHO suggests to use appropriate assistive devicesthat would help people to perform everyday activities. It alsostates that only 5-15% of the people with SCI in low andmiddle income countries have access to assistive devices thatthey need. There are about 400,000 SCI patients in USA alone,with about 15,000 new injuries every year [1].

Each year, about 15 million people worldwide are affectedby stroke and 80% of the people affected by stroke are havingmobility issues [50]. The assistive devices in the market arevery generic in nature and very few of them available aretoo costly for the people of developing and underdevelopednations. Nuclear families which enveloped the western nationshave found its way into many developing nations and the needfor such assistive devices have increased manifold in these lowincome countries. The research work presented here is aboutIR sensor based assistive device at an affordable price, whichwould help people with mobility issues.

Joystick control for powered wheelchair is the commerciallyavailable one for most of the users. Literature survey showsthat tongue based [12], [17], head motion based [2], [8],keypad based [11], brain control based [10], [15], [23],

MEGALINGAM et al.: IR SENSOR-BASED GESTURE CONTROL WHEELCHAIR FOR STROKE AND SCI PATIENTS 6757

Fig. 2. HanGes system architecture.

EOG based [18], vision based [20], [22], joystickbased [34]–[37] etc. control methods are also available.There are few companies which do make customizedwheelchairs for the quadriplegics.

HanGes permits the use of hands, fingers or even the leg tocontrol the wheelchair. With the HanGes, the users can exertprecious little force, which may only be sufficient to just movetheir limbs alone. As gesture is part of human nature, gesturecontrol is very easy compared to control anything that is aliento human body. HanGes is very simple to use, as one need notcontrol any other device, other than performing simple handgestures on a smooth platform, for directional navigation.

HanGes has the option for customization of gpaD where theusers - quadriplegics or stroke patients can use it with ease.The user is expected to use only five simple hand gesturesfor the wheelchair control, namely: forward, reverse, right, leftand brake. The sensor setup inside the gpaD can be rearrangeddepending on the degree of movement of hands irrespective ofleft or right hand. The gpaD can be fixed to the arm rest eitherleft or right and also to the foot rest if an user is comfortableusing one of the feet. The development of HanGes systemincluded a gpaD, wheelchair frame, wheels, the PMDC motorsand motor drivers.

The rest of the paper is organized as follows. The systemarchitecture, design and implementation of HanGes is pre-sented in Section III. Section IV - Testing and Evaluation,presents the evaluation of the HanGes system with volunteers,to know how well the system works as intended. In section Vwe present the discussion based on the evaluation results.

III. ARCHITECTURE, DESIGN AND IMPLEMENTATION

Figure 2 shows the systems architecture of HanGes withvarious modules. The heart of the system, gpaD (Gesture Pad)is connected to the motor driver which in turn is connectedto the motors and wheels. The directions of the motorsare controlled through the MCU of gpaD using high powermotor drivers. The gpaD is made up of a small case withan IR Sensor-Detector pair array, a microcontroller, powercontrol circuit, power switch, speed control knob, connector,and battery charge indicator. This case is covered with atransparent sheet over which the user places the hand forthe gestures to control the wheelchair navigation. The user is

Fig. 3. gpaD architecture.

Fig. 4. gpaD Design.

Fig. 5. Suggested gestures.

expected to make only five simple hand gestures for forward,reverse, left, right and brake by placing his/her palm over thegpaD surface. The gpaD schematic is shown in Figure 3. ThegpaD as implemented by us is shown in the Figure 4.

The five different hand gestures to control the movementof wheelchair in any direction is shown is Figure 5. For thefive gestures, the user is expected to move the hand over thegpaD in only four directions. The forward, reverse and brakegestures require moving the hand (either left or right) up anddown only, over the gpaD. There are two options for Brake.The user can move the hand to the edge of the gpaD as shownin the Figure 5 or the user just removes the hand from thegpaD which is called an empty gesture. The users can rest theirforearm on the arm rest of the wheelchair and perform gestures


by placing their palm over gpaD to move the wheelchair in theintended direction. The IR sensor signals are captured by theMCU of gpaD, processed and the gesture is identified. Oncethe gesture is identified, the next step is to move the wheelchairin the direction corresponding to the gesture which is providedby the two 24VDC, 320W, 3A Geared Permanent Magnet DCMotors with an output RPM of 4200.

The PWM outputs generated by the MCU are used fordriving the Pololu High-Power Motor Driver 24v23 CS, whichenables bidirectional control of high-power PMDC brushedmotor. This driver which consists of a discrete MOSFET H-bridge is efficient enough to deliver a continuous 23 A withouta heat sink. To summarize, ‘HanGes’ consists of a wheelchair,gpaD, batteries, PMDC motors and high-power motor drivers.

The IR sensor array is based on the principle of reflection ofIR rays from the incident surface. IR LED emits a continuousbeam of IR rays. Whenever a reflecting surface (in our case ahand) comes in front of the IR receiver (a photodiode), the raysare reflected back and captured by the photodiode, which gen-erates the current. The amount of current generated depends onthe amount of IR rays incident on the photodiode. In case ofgpaD, the reflectance method is used for capturing the IR rayswhere the user’s hand serves as a reflecting surface. IR sensortechnology is used in wide range of applications [5], [6], [9].

In case of gpaD, when the user places the hand over itfor a gesture, either a gesture is identified or not identified.When the expected gesture is identified, it is a successful eventand a failure, if not. This is a random event as the user canchoose to show any hand gesture at any time. The result of onehand gesture identified is not dependent on the previous handgesture identified. This can be modeled as Bernoulli’s trialprocess, in which each trial has two possible outcomes: successor failure. In addition, the outcome of a trail is independentof the outcome of another trial. The Bernoulli distribution isa discrete distribution with two possible outcomes, success,which can occur with probability p and failure, that can occurwith probability 1-p, where 0 < p < 1. The probability densityfunction is given by,

p(x) ={

(1 − p) f or x = 0

p for x = 1(1)

where x = 0, represents the failure outcome and x = 1, thesuccessful outcome. Equation 1 can also be rewritten as,

p (x) = px(1 − p)1−x (2)

In equations 1 and 2, x is a random variable, usually denotedby X which denotes success or failure of the trial, where

X = (X1,X2,X3....) (3)

where the random variable Xi simply records the outcome ofthe trail i. Thus equation 1 can also be written as

P(Xi = 1) = p, P(Xi = 0) = (1 − p) (4)

If there are n number of trials then the joint probabilityfunction of (X1,X2,X3....Xn) trials is given by

fn (x1, x2, .....xn) = p(x1,x2,.....xn )(1 − p)n−(x1,x2,.....xn ) (5)

Fig. 6. Hand rotation with respect to the wrist.

where (x1, x2, .....xn) ∈ {0, 1}n

If we assume that each user of HanGes can perform ngestures of which the outcome is represented by the randomvariable gi, where gi can take the values ’0’ for failure toidentify a gesture intended by the user and ’1’ when a gestureis identified successfully. The integer i can vary from 1 to n.

G = (g1,g2,g3....gn) (6)

If there are N number of users then we have

G1 = (g11,g12,g13....g1n)

G2 = (g21,g22,g23....g2n)

.

GN = (gn1,gn2,gn3....gnn) (7)

where G1 to GN represents the Bernoulli trial processes for1 to N users respectively. The random variables for each ofthe users can vary randomly as each of the user can performany number of trails n1 to nn.

A. IR Sensor Array DesignThe size of the gpaD depends on the size of the hand

of the user. One advantage of the gpaD design is that itis customizable. The size of the IR sensor array can becustomized to the size of the user’s hand. As the human handsize is not fixed and varies user to user, extreme care has to betaken while designing gpaD for stroke and SCI patients wheretheir hand movements are also restricted.

B. Hand PositioningAs per the medical standards [21], a person with healthy

hands can rotate the hand with respect to wrist over a flatsurface, either left or right only to a certain degree. The normalulnar deviation θ1 (right side) ranges from 30° to 50° andnormal radial θ2 (left side) deviation is 20°.

As shown in Fig. 6 the user has the freedom to move thehand with respect to the origin i.e. the wrist. The wrist positionis taken along x axis and the neutral position, along y axis.The angle of deviation is with respect to the wrist and neutralposition only. For stroke patients with affected hands and forthe SCI patients who have regained some motor control canalso be expected to have the angle of deviation only within


TABLE I

HANGES % SUCCESS RATE FOR GESTURE CHANGES

this range. The gpaD can be customized in such a way that thehand gestures can be recognized (either left of right) even witha 7° - 9° ulnar deviation and 7° - 9° radial deviation. Similarlyhow much a healthy person able to move the hand back andforth over a surface depends on the size of the surface and thesize of the full length arm of the person.

C. Hand SizeThere are various ways for measuring the size of the hand.

In our case, the size of the hand of the user is assumed to be arectangle without considering the thumb. The size is assumedto be mxn where m is the height and n is the width of thehand as shown in the Figure 6.

The gesture recognition ξ can be modeled as a function ofthe size of the hand (m, n), the ulnar and radial deviations(θ1 and θ2) and the distance δ that an user is able to movethe hand back and forth. So the success rate depends on thegesture recognition ξ which is a function of (m, n), (θ1, θ2)and δ.

Success rate = ξ((m, n), (θ1, θ2), δ),

where (m, n) is dependent on the size of the each user’s hand,(θ1, θ2) is dependent on how much each user able to movethe hand left and right and δ is dependent on how far backand forth the user is able to move the hand. All these threeparameters of the stroke and SCI patients have to be found outin advance and accordingly the size of the gpaD is decided.When the user shows the gesture by placing the hand over thesmooth platform of the gpaD, a particular pattern of IR sensorsfrom the IR Sensor array inside the gpaD are activated and thegesture is identified by the microcontroller which generated therequired control signal to motors of the wheelchair to movein the intended direction (left, right, forward, backward andbrake). This pattern varies for each of the five gestures.

IV. TESTING AND EVALUATION

A. Success Rate

Ten volunteers were asked to perform the same gesturerepeatedly, 20 times. A gesture combination of forward to left,forward to right, forward to reverse, forward to brake puts thenumber of gesture combination with respect to forward at four.There are five gestures in total (for left, right, forward, reverseand brake) which will make the number of different gesturecombinations, twenty. A total of 4000 gestures were capturedand analyzed to find the success rate. The Table I shows thesuccess rate of all the 20 different gesture changes. Each of thesuccess rate listed in the table represents the average successrate of 10 participants for a particular gesture change.

TABLE II

CONFUSION MATRIX FOR THE GESTURE CHANGES FROM ALL OTHERGESTURES TO FORWARD GESTURE

TABLE III

CONFUSION MATRIX FOR THE GESTURE CHANGES FROM ALL OTHER

GESTURES TO REVERSE GESTURE

TABLE IV

CONFUSION MATRIX FOR THE GESTURE CHANGES FROM

ALL OTHER GESTURES TO RIGHT GESTURE

TABLE V


ALL OTHER GESTURES TO LEFT GESTURE

TABLE VI


ALL OTHER GESTURES TO BRAKE GESTURE

The confusion matrices for each of the gesture changes areprovided in the Tables II - VI. It is clear from the confusionmatrix that when a user makes gesture change, the confusedgesture category is that of the previous gesture from which theuser intended to change it to the new gesture. For example, asshown in Table II, when the user wants to change the gesturefrom Reverse to Forward, the confused category is Reverseitself and not any other gesture. This is true for all otherconfused categories as listed in the Tables II - VI.


TABLE VII

HANGES RESPONSE TIME FOR THE GESTURE CHANGESW.R.T. FORWARD GESTURE (IN SECONDS)

TABLE VIII

HANGES RESPONSE TIME FOR THE GESTURE CHANGES

W.R.T. RIGHT GESTURE (IN SECONDS)

TABLE IX

HANGES RESPONSE TIME FOR THE GESTURE CHANGES

W.R.T. REVERSE GESTURE (IN SECONDS)

B. HanGes Response Timings

For each of the gesture changes with respect to a chosengesture, the response timings are listed in the Tables VII to XI.

The response time is based on the time taken for the rearwheels to start to move from the moment the participant movedthe hand over the gpaD to make the new gesture from an oldergesture. The hand gestures of the volunteers were analyzedto find these response timings. These response timings weremeasured based on the hand gesture changes made by the10 volunteers who repeated these gesture changes 10 timesand average response timings are listed in the tables.

The various response timings plot is given in the Figure 7.Overall, twenty plots are shown in this figure for the twenty

TABLE X

HANGES RESPONSE TIME FOR THE GESTURE CHANGESW.R.T. LEFT GESTURE (IN SECONDS)

TABLE XI

HANGES RESPONSE TIME FOR THE GESTURE CHANGESW.R.T. BRAKE GESTURE (IN SECONDS)

Fig. 7. Gestures vs response time.

different gesture change possibilities. The red colored plotsrepresent the four different gesture changes with respect to(w.r.t.) the forward gesture. The blue colored ones show theresponse timings w.r.t. reverse gesture. The green ones arew.r.t. right gesture, where as the black one represents responsetimings w.r.t. the left gesture. Finally the violet color representsthe brake to other gesture change response timings. From theFigure 7 we note that, for the gesture changes from Right/Left/ Forward/ Brake to Reverse, the response time is slightlyhigher than one second. For all the other gesture changes,the response timings are within the range, from 0.3 secondsto 0.8 seconds.


TABLE XII

HANGES VS JOYSTICKWHEEL - RESPONSE TIME IN SECONDS

C. HanGes Evaluation

Evaluation of HanGes took place in three different phases:(i) comparing the response timings of HanGes with one ofthe joystick based control powered wheelchairs from themarket; (ii) evaluating based on the participant experience whovolunteered to test the wheelchair; (iii) evaluating based on thedoctors’ and patients’ experience at Physical Medicine andRehabilitation Department of Amrita Institute of Medical Sci-ences, Amrita Vishwa Vidyapeetham University, and AmritaKripa Charity Hospital, Mata Amritanandamayi Math, both inKerala, India.

In the first phase of HanGes evaluation, we measure theresponse time of gpaD when connected to the powered wheel-chair. For comparing the response timings of HanGes withthat of the commercially available wheelchair, we bought ajoystick based control powered wheelchair from the market.This wheelchair we name as JoystickWheel. The responsetimings for all the gesture changes are found using thiswheelchair too. The response timings of HanGes as comparedto JoystickWheel is shown in the Table XII. Each gesturechange timing listed in the Table is the average of 10 repetitivegesture changes of the values plotted in the Figure 7.

For the second phase of evaluation, two groups of volunteerswere chosen. One group of volunteers was people who werealready using joystick based powered wheelchairs. The secondgroup of volunteers was people who have never used anykind of wheelchairs. The first group of volunteers, seven innumbers, has experience in using the joystick wheelchairs inthe range of 6 months to twelve years. They were asked to ratethe joystick wheelchair and also HanGes after their experiencein the scale of 0 - 5, where 5 being the highest score. Theywere also asked about their preference (either joystick basedor HanGest control) in using wheelchair. Their ratings andpreferences are shown in Table XIII.

Two of these volunteers have given the ratings as 1 forjoystick based wheelchair. These two volunteers felt that the

TABLE XIII

GROUP 1 USER RESPONSE

TABLE XIV

GROUP 2 USER RESPONSE

joystick based control mechanism was very uncomfortableand they were forced to use it because they didn’t have anyother option. Five of the seven volunteers would prefer to useHanGes. One of the volunteers who has been using joystickwheelchair since 2012 would not prefer gesture based HanGes.This volunteer could not lift or move the both the hands eitherright or left, which is very crucial for anyone to use gesturebased HanGes. As mentioned earlier, the user must be ableto move their hand over gpaD in left / right/ forward/ backdirections at least to the minimum extent so that gpaD couldidentify the gesture.

The second group of 10 volunteers was given both thejoystick controlled wheelchair and gesture based HanGes. Thisgroup of volunteers has no prior experience in using any kindof wheelchair. After using the joystick controlled wheelchairand gesture based HanGes, they were also asked to rate thesame way as in the first group and were asked about theirpreference. Their ratings and preferences are given in theTable XIV. Eight of the ten volunteers would prefer gesturebased HanGes and five of them gave 5 as their ratings. Onlyone of them gave 3 as the rating for HanGes.

In the final phase, HanGes was taken to two hospitals,Physical Medicine and Rehabilitation Department AIMS, andAmrita Kripa Charity Hospital, both in Kerala for testing withthe patients. We were in touch with the doctors of the Rehab.Dept. of AIMS for more than one and half years who gave usvaluable suggestions in shaping the design of HanGes. As pertheir suggestions we built a ramp as part of safety measure andconducted a ramp test. The ADA (Americans Disabilities Act)


TABLE XV

HANGES RATINGS BY DOCTORS AND PHYSIOTHERAPISTS

standard states that the maximum slope of the ramp shall be inthe ratio 1:12 for commercial use. It states that if the heightof the ramp is 1 inch, then its length is supposed to be 12inches or one foot. The ramp height, length and the angle ofinclination are specified by the ADA standard for commercialpurpose. A 12 feet length and 1 foot high ramp was setup forthis test and HanGes successfully completed the test. The testwas overseen by a physician from the Rehab. Dept. , AIMS.

HanGes with the user seated on it was made to climb up anddown the ramp 25 times and the climbing was smooth all thetimes. The electric brake which is part of the motor could stopHanGes at any time during climbing up or down the ramp.A doctor from the Rehab. Dept. of AIMS was also presentduring the test. When HanGes was taken to this Rehab. Dept.,four doctors and two physiotherapists sat on HanGes used thegpaD to navigate before giving to the patients for testing. Theywere also asked to rate HanGes on the scale of 0 - 5, ‘0’ beingWorst and ‘5’ being Best. Table XV shows the scores givento HanGes by the doctors and the physiotherapists.

Three stroke patients (two male and one female) of age 20,27 and 68 volunteered for testing HanGes at the Rehab. Deptof AIMS who were undergoing physiotherapy. They had noprior experience in using wheelchair themselves only that theywere taken in manual wheelchairs by the caretaker to meetthe physicians or for strolling outside to have a break. Theycannot use joystick wheelchairs because their hands have notsuch motor control to hold on to the joystick and push it indifferent directions to control the wheelchair. 27 and 68 yearsold patients require help to lift them even though they haveregained some level of movements in their body. The 20 yearold stroke patient can walk slowly with someone near to help.But this patient regained some motor action in left hand butnot in the right hand. The 27 year old patient regained somemovement in the right hand but still couldn’t move the handsas normal person. The 68 year patient gained better motoraction in right hand during the stay at the rehab. center. The20 year old volunteer couldn’t use the right hand as it is stillrecovering from the stroke impact. So the gpaD was fixed onto left side of the wheelchair for this volunteer to test it. Thisis shown in Figure 8 (c) where as in Figures 8 (a) and 8 (b)the gpaD is fixed to the right side of HanGes.

Two more volunteers (both female) aged in the range of50 - 60 were also given HanGes for testing, at the AmritaKripa Charity hospital, Mata Amritanandamayi Math, Kerala,India. Out of these two volunteers, one volunteer was usingthe joystick based wheelchair for over 12 years. The second

Fig. 8. Stroke patients using HanGes at the Rehabilitation Department,Amrita Institute of Medical Sciences, Kerala, India.

volunteer was using the manual wheelchair for about twomonths. In case of the first volunteer, both the hands couldn’tbe moved over gpaD to any extent and external help wasrequired to place the hand over the gpaD. Also the fingerswere twisted in one direction. This volunteer felt very uncom-fortable in using HanGes. She was a healthy person till hercollege and due to some disease which affected her spinal cord,both arms were rendered crippled. Her legs also got affected.The second volunteer had some accident and couldn’t walkwhich forced her to use a wheelchair.

The patients were asked to rate HanGes based on theirsatisfactory level (Best, good, moderate and unsatisfactory)after using it. Three of them gave the satisfactory rating as“Best”, one gave “Moderate” and one was “Unsatisfactory”.Figure 8 (a), (b) and (c) shows the three stroke patientswho are undergoing physiotherapy as part of rehabilitationprocess at Rehab. Dept. of AIMS testing the gesture controlledHanGes.

V. DISCUSSION

‘HanGes’ gpaD mechanism is based on hand gestures, wenamed this powered wheelchair system as HanGes. This sys-tem indicates the feasibility of simple hand gesture control fornavigation in powered wheelchairs for patients with mobilityissues. A pad which has a smooth glossy surface called gpaDwas designed to identify the hand gestures of users who canmove their hands only to a certain extent including stroke andSCI patients, as against hand movements of healthy person.As gpaD is designed with IR sensors and detectors and anMCU along with few other simple electronic components thecost is only about 70 USD. As the size and the movement ofthe hand varies depending on the severity of the stroke andSCI affected patients, the gpaD design can be customized foreither of the hands.

The success rate in identifying the correct gestures as shownin Table I is very much assuring. Overall success rate in recog-nizing the gestures is 99.25% which is extremely encouraging.In many cases we see that the success rate is 100%. Theresponse timings of HanGes as listed in Tables VII to XIand in the Figure 7 show that it is well within the range of0.3 seconds to 0.8 seconds. For any gesture change to Reversegesture, the response time is around one second. The responsetimings of a commercially available joystick based wheelchair


is given in the Table XII along with HanGes response timings.HanGes’s performance is similar or better in many cases andnot bad in few other cases where the response timings areclose to one second.

The evaluation of the HanGes by two groups of volunteers- one experienced users and the other, new users, as givenin Tables XIII and XIV is a pointer that HanGes can beaccepted by the users of wheelchairs. Only with five handgestures - forward, reverse, right, left and brake which areintuitive, the gesture based control method using gpaD issimple for anyone to master. This evaluation by the joystickbased powered wheelchair users who who are elders and/orphysically challenged indicates that HanGes can be used evenby others who are not stroke patients or SCI patients. Five outof seven of these volunteers would prefer to use HanGes overjoystick based wheelchairs.

Five patients including three stroke patients, one patientwith issue in spinal cord and one patient with injury inlegs volunteered to test HanGes. These patients were partof Rehabilitation Department of Amrita Institute of MedicalSciences, Kerala, India and Amrita Kripa Charity Hospitalof Mata Amritanandamayi Math, Kerala India. The feedbackand evaluation by these patients are very promising that threeof them rated HanGes as ‘Best’ and one as ‘Moderate’ andother as ‘Unsatisfactory’ when they were asked to rate HanGesbased on their experience. These ratings point to the fact thatHanGes can succeed as a commercial product too, apart fromproviding a mobility aid to stroke patients and SCI patientsfor whom a commercially available powered wheelchair isnot available to them in their countries or is very costly for acustomized wheelchair in countries which are available withfew wheelchair manufacturers.

The doctors’ advice and help was sought many times evenfrom the beginning of the design and making of gesturebased HanGes. One of the doctors visited three differenttimes the Humanitarian Technology Lab where HanGes wasbeing developed to give feedback about design. The doctorsindicated that such a gesture based wheelchair control wouldprovide mobility to all those stroke and SCI patients who arepsychologically affected severely as those who were carryingout day-to-day life as every other healthy person in this world,suddenly feel hopeless after the stroke attack or SCI. To someextent they also think themselves they have become burden totheir near and dear ones, as a full time caretaker is needed toattend to them for all mobility related issues. HanGes wouldbe a boon to such people and would serve as a booster totheir mental strength and give confidence to them. The doctors(who also tested HanGes themselves) and the authors hopethat HanGes will greatly help in their rehabilitation processand rebuild their lives.

With the turning radius of 22 inches, maximum speedof 3.8 miles/hour, weight of 120 pounds, range of travel9.3 miles/charge, weight of the person seated 220 pounds,motor power of 320 watts, HanGes meets the technical speci-fications of a commercially available joystick control poweredwheelchairs. There are various powered wheelchair modelsavailable in the market with various features by many wheel-chair manufacturers: Collapsible for Transport, Joystick Mount

Adjustable, Armrest Position Adjustable, Seat to Floor HeightAdjustable, Head Rest Available, Head Rest Adjustable,Tilt Adjustable, Footplate Height Adjustable, Backrest Posi-tion Adjustable, Spring Suspension Available etc. All thesefeatures can be incorporated in HanGes too as it is partof the wheelchair design. Even though there are so manydifferent features which are part of wheelchair, the controlmethod used in all these models is only joystick control.But this control method is not suitable for all people withmobility issues and they users of these wheelchairs are leftwithout any option when it comes to the control method. Sevenexperienced wheelchair users, ten volunteers who never usedany kind of wheelchairs, four doctors, two physiotherapistsand five patients volunteered to test gesture based HanGes andevaluated it. Based on their feedback and with the price tagat less than 1000 USD, it is our sincere hope that the gesturebased HanGes would serve as an alternative to the existingcontrol method, help all people with mobility issues and be acatalyst in the rehabilitation of stroke and SCI patients.

ACKNOWLEDGMENTS

The authors would like to thank Sadguru Sri MataAmritanandamayi Devi, Founder and Chancellor, AmritaVishwa Vidyapeetham University for her support throughoutthis project. They would also like to thank Dr. Ravi Sankaran,Clinical Asst. Professor, other physicians and physiotherapistsof Physical Medicine and Rehabilitation Dept., Amrita Insti-tute of Medical Sciences, Kerala, India and Amrita KripaHospital, M A Math, Kollam, India for their support andhelp in testing HanGes with patients. They also thank all thepatients and volunteers who participated in the evaluation ofHanGes.

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Rajesh Kannan Megalingam is currently theDirector of HuT Labs and an Assistant Professorwith the Department of Electronics and Communi-cation Engineering, Amrita Vishwa VidyapeethamUniversity, India. His research areas include robot-ics, rehabilitation technology, VLSI, and embeddedsystems. He has seven and half years of industryexperience and 11 years of research experience.He was a VLSI Design and Verification Engineerat various companies such as STMicro Electronicsand Insilicon Incorporation in Bay Area, CA, for

six years. He has several research publications in conferences, journals, andbook chapters. He has won several awards including the Outstanding BranchCounselor and Advisor Award from the IEEE, NJ, USA, the OutstandingBranch Counselor Award from the IEEE Kerala Section and Award ofExcellence from Amrita University.

Venkat Rangan received the degree (Hons.) fromAmrita Vishwa Vidyapeetham University, India.Amrita University is the highest ranked private uni-versity in India, led by its Chancellor, AMMA,one of the foremost humanitarian leaders. In 2003,AMMA appointed Dr. P. Venkat Rangan as the ViceChancellor of Amrita University, where he has estab-lished numerous world class centers of excellencein wireless networks, cybersecurity, and e-learning.He founded and directed the Multimedia Laboratoryand Wireless Networks Research at the University of

California, San Diego, where he served as a Professor of Computer Scienceand Engineering. He is an internationally recognized pioneer of research inmultimedia, and served as the Program Chair of ACM Multimedia 1993, andan Editor-in-Chief of ACM/Springer Multimedia Systems Journal. He alsofounded Yodlee, Inc., a global pioneer in Internet E-commerce, for whichhe was featured on the cover page of Internet World Magazine in July 2000as one of the top 25 Internet entrepreneurs. He was a recipient of the U.S.National Young Investigator Award and is a Fellow of ACM.

Sujin Krishnan was born in Kerala, India, in 1992.He received the B.Tech. degree in electronics andcommunication engineering from Amrita VishwaVidyapeetham University, Kerala, India, in 2014.He was a Research Assistant at HuT Labs. He iscurrently with Infosys, Pune, India. His researchareas include VLSI, embedded systems, databases,and information retrieval.

Athul Balan Edichery Alinkeezhil was bornin Kerala, India, in 1991. He received theB.Tech. degree in electronics and communicationengineering from Amrita Vishwa VidyapeethamUniversity, Kerala, India, in 2014. He is currentlypursuing the master’s degree in computer sciencewith the University of New Mexico, USA. He was aResearch Assistant at HuT Labs. His research areasinclude VLSI, hardware oriented security, and trustand embedded systems. He was awarded with theRichard E. Merwin Scholarship (2013) by the IEEE

Computer Society. He was a Student Ambassador from 2013 to 2014.

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