A New Hand Measurement Method to Simplify
Calibration in CyberGlove-BasedVirtual
Rehabilitation
Jilin Zhou, François Malric, Shervin Shirmohammadi
Distributed Collaborative Virtual Environments Research (DISCOVER) Lab
School of Information Technology and Engineering (SITE), University of Ottawa, Canada
jzhou/fmalric/shervin@discover.uottawa.ca

Abstract— We have previously developed a prototype virtual
reality enhanced rehabilitation system using the CyberForcesystem to assist patients who have suffered from upper extremity
stroke to practice some daily-life exercises. However, fullcalibration of the system for each patient is currently not only
tedious and time-consuming, but also impractical in the case of
severely disabled hands. In this paper, we propose a practical andeasy-to-perform hand measurement method to calibrate the
CyberGlove using artificial neural networks. The neuralnetworks are trained with the hand segment sizes as input andthe manually collected calibrated data as output. The only
external device needed is a 2-D digital camera to take the pictureof the subject’s hand against a chessboard for the hand segment
sizes measurement. Subjective evaluation results for various
common hand postures show the effectiveness of the proposed
method.

extremity kinematic model which is able to accurately
quantify upper extremity arm motion using a Vicon motion
analysis system [8]. In [9], Zhou et al. proposed a data fusionbased tracking algorithm to estimate the upper limb
movements using two wearable inertial sensors placed aroundthe wrist and the elbow joints. To eliminate the problem ofphysically attaching sensors on the subjects’ arm, Mihailidis et
al studied the use of computer vision-based system in trackingof human motion in an intelligent environment [10]. However,presently it is still very challenging to track fine motor
movements such as hand or arm movements with thesemethods, and more research is still needed in this area.

Keywords- Virtual Rehabilitation, CyberGlove Calibration, Human
Hand MeasurementI.

INTRODUCTION

With recent advances in Human Computer Interfaces andVirtual Reality (VR) technologies, VR-enhanced stroke
rehabilitation applications have been actively exploredrecently [1] [2]. These VR applications offer the potential tocreate systematic human testing, training and treatment
environments which allow precise control of complex
dynamic 3-dimensional (3-D) stimulus presentations,behavioral tracking, performance measurement, data recording
and analysis [3] [4] [5]. At the Discover Lab, we havedeveloped a prototype VR rehabilitation system to assist
patients who have suffered from a stroke with upper extremity
weaknesses [6] [7]. The physical setup of the prototype adopts
the integrated CyberForce systems from CyberGlove Systems
as shown in Fig. 1. The input measurements of the wholesystem include three parts: the whole hand translational
position using the CyberForce subsystem, the rotation of theforearm using a 3-D magnetic tracker, and the hand gestureusing a CyberGlove. One limitation of the system is that the
motions of the elbow, the upper arm, and the shoulder are notmeasured which are in fact important to be monitored forupper extremity stroke rehabilitation. However, in the future,
result of other on-going research about these limitations canbe integrated into our system for the capture of the arm
movements. For example, Hingtgen et al. present an upper

Figure 1: CyberGlove system used in our haptic-enabled rehab system

The design requirements of our system were the results ofseveral consultations with the Rehabilitation Centre of the
Ottawa General Hospital. The prototype allows the patients topractice some daily-life exercises, such as moving common
objects on the shelf, pouring tea into a cup, eating soup from abowl, and tracing a maze without touching the walls. The
initial implementation was also placed under the analysis of agroup of five Occupational Therapists (OTs) that provided
thorough feedback and further amendments to the system [6].
One of the main feedbacks from the OTs is that the calibrationof the whole system is very tedious and time-consuming,
particularly for the CyberGlove. CyberGlove is a kind of hand
input device to measure human hand postures, so that the user
can intuitively interact with the synthetic objects in a virtualenvironment in a natural way [11]. The glove used for thisstudy has a total of 22 sensors placed at critical points. Due tovariations of the human hand size, the relative positions of thesensors to their correspondent critical points of the hand are
different across the users. Calibration is therefore required.
With the factory pre-installed calibration method, it takes
takes about 5~10 minutes to get a decent calibration for ahealthy person. This will be even more problematic in the caseof rehab patients who have severe disability, to the extent that
their hand’s motor functions are not physically suitable for use
in the normal calibration process.

m

i

1

In this paper we propose a practical calibration techniquethat is faster and simpler than existing methods, and can be
more easily used by rehab patients even with severe upper
extremity disability. Through the experiments, we found there
are direct relationships between the human hand’s segment
size and the correspondent sensor’s readings. In [18], we
exploited this relationship for the sensors on the index, middle,
ring, and pinky fingers and proposed a method using Artificial
Neural Networks (ANNs) for the CyberGlove calibration. In
fact, ANNs have been successfully applied for the calibrationsof many systems in many different engineering fields for its
usage simplicity and effectiveness in learning complex
underlying input/output patterns [19] [20] [21] [22]. In thispaper, we have retrained the neural networks with different
input/output combinations that were missing in [18], such as
hand width, and also extended the method to measure thethumb and abduction related sensors, which is physicallydifferent and more difficult from the other fingers. In addition,
we’ve performed subjective evaluations with five common
hand postures which show the effectiveness of the proposed
method.
The rest of the paper is organized as follows. Section II
discusses the background and the related work. Section III
details the procedures of our proposed method: measuring
hand segment size, collecting calibration data, and training
neural networks. Section IV presents the experimental
evaluation and results of the proposed method. Finally,
Section V concludes the paper and outlines the future work.

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Based on its applications, the existing calibration methods
for CyberGlove fall into two categories. The first category
focuses on the determination of the real joint angles of the handwith high accuracy [12] [13] [14], while the second category
aims at achieving high visual fidelity of hand postures inside
the virtual environments [15] [16] [17]. Accordingly, theirrespective evaluation methods are also different. For the firstcategory, the performances are evaluated in terms of theabsolute differences between the real values and theirinterpreted values inside the application. For the second
category, the performances are evaluated by visuallycomparing the real world hand postures and theircorresponding virtual representations. During calibration, thesubjects are usually asked to pose different postures with theirhand and move for an amount of time, or against some physicalconstraints. Therefore, for rehabilitation systems, simplifying
the calibration process is not only a desirable feature for all
patients, but also a necessary feature for some specific patientswith more severe hand disability, without which the rehab itself
cannot be performed.

r

4

18
22
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Figure 2: (a) Instrumented CyberGlove and sensor indexing, and (b) handsegments indexing used in the research.

II.

BACKGROUND AND RELATED WORK

There are a total of 22 sensors on the CyberGlove, which
are placed at critical points for the measuring of finger flexing,
abduction, thumb roll, palm arch, wrist yaw, and pitch as
shown in Fig. 2a. The sensors are light, thin strips sewn into
the glove fabric. The output of each sensor is an 8-bit unsigned
integer in the range of 0-255. Since all joint movements areless than 180 degree, 8-bit output provides a sub-degree
resolution. Given a sensor’s raw data reading x , itscorresponding joint angle is calculated as  g (r  x),(1)

where g and r denote the sensor gain and offset respectively.
Intuitively, the calibration process is to find this gain andoffset for each sensor so that the user’s hand gesture in the realworld can be faithfully reproduced in the virtual environment.
For example, when the thumb tip and the index tip are touched
together in the real world, there should be no obvious gap
between the correspondent configurations in the virtual world.
Since hand sizes for different users are different, the relative
positions of sensors to the correspondent critical points of thehand are different across the users. Calibration is therefore
required to adapt to each user’s hand size and to convert
sensor output voltages to the joint angles. Even though most of
the sensors are carefully decoupled by design, the number of
sensors in total and their combined effects on the handkinematics make the calibration not an easy task. For example,
the readings from abduction sensors are affected by theiradjacent meta-carpo-phalangeal (MCP) joints’ flexion
movements.
In [12], Griffin et al. present a calibration scheme to support
dexterous tele-manipulation. The opening/closing movement of
the end gripper on the robot manipulator is controlled by the
distance between the thumb tip and the index finger tip. With
their method, the user is first asked to place his/her thumb andindex fingertips together and maintain a rolling contact whilemoving the fingers for 40 seconds. The readings from all of therelated sensors during this period are recorded. After that, theseparation distances between the two fingertips of a nominal
hand kinematic model using the recorded data are calculated.
Taking these separation distances as the errors to minimize,
least squares method is then applied to tune all the related
sensors’ gain and offset to achieve an error-minimizing model

for each user. Menon et al. extended this method to all fivefingers for the applications of virtual reality simulation for
astronaut training and telemedicine [13]. In [14], Fischer et al.
propose a nonlinear learning calibration using a neural network
technique for a dataglove to control a four-finger robotic hand.
The neural network is trained to learn the mapping from thedataglove data to the fingertips’ positions. To get the actual
Cartesian positions of the fingertips for the training, each of thefour fingers is marked with a colored pin and two extra
cameras are used. From the grabbed image and the known
operating parameters of the cameras, the fingertips’ positions
are then recovered. The method can achieve fingertip position
errors of less than 1.8 mm. However, this method has a numberof limitations. First, this calibration method needs timesynchronization between the dataglove data and the visual data.
Second, two calibrated color cameras are needed to recover the
3-D Cartesian fingertips positions from the images. Third, only
the positions of the fingertips are calibrated.
In our VR-based rehabilitation applications, we are moreinterested in the global visual satisfaction of all the joints
involved in the hand movements inside the virtual environment
than only absolute fingertips positions. As such, the above
methods do not apply to our VR Rehab system. For example,
when a patient practices holding a cup and moving it on a shelf,
the contacts between the hand and the cup cannot be simplified
into the contacts between the fingertips and the cup. It is very
possible that the fingertips positions are correct but the middle
joints are not.
There are other approaches that measure more than just thefingertips, but they also have their limitations. In [15] Kahlesz
et al. propose a method to account for the cross-coupling
effects between the MCP joints’ flex sensors and the inbetween abduction angles. For each abduction sensor, an isosurface is created based on its readings under threeexperimental trajectories: only the left flex sensor bended, bothleft and right flex sensors bended, and only the right flex sensor
bended. The values of this surface are stored in a look-up tablefor fast evaluation of the abduction angle. This is in contrast to
the previously discussed methods which aim at the absoluteaccuracy of the joint angles between the virtual and the real
hand. In [16], B. Wang et al. assume the effects from theneighboring flex movements are linear. Equation (1) thenbecomes

abd  gabd (rabd  xabd )  (kl rl  kr rr  b),(2)

where rabd is the calibrating abduction sensor’s reading, g abd
and xabd are the gain and offset of the sensor, rl and rr are
adjacent MCP flexion sensor readings, kl , k r , and b are thecross parameters for the neighboring flexion sensors. Duringcalibration, the subject needs to move the neighboring MCP
joints both independently and jointly. Least square method isthen applied to get these cross parameters. Following the same
strategy, the authors calibrate the thumb related sensors. At thistime, the subject is asked to constraint his/her index and thumbtips on to a bottle cap to compute the cross parameters. Both[15] and [16] focus on the coupling effects among the sensors.
However, as will be seen in this paper, for the Rehabilitation

postures, the coupling effects among the sensors do not play animportant role.
In [23], Kahol et al. propose a human anatomy based
Hidden Markov Model (HMM) to recognize everyday human
movements. Instead of modeling the gestures as sequences ofstatic poses, the author suggested that they can be modeled as
events occurring in the segments and joints of the human body.
The events in joints are detected as stabilization of the angles
between adjacent segments at the joints while the events forsegments are detected as local minima in the segmental force.
These two human anatomy-based HMMs are coupled together
using the body distance between the segments and the joints.
This gives us the idea that the hand movements can also bemodeled as the events occurring in the hand segments andjoints. The relationship between the selected hand segments
size and the correspondent sensors reading can be exploited forthe calibration of the CyberGlove.
III.

THE PROPOSED METHOD

In this paper, we calibrate the sensors on the CyberGloveusing ANNs. There are three main reasons for choosing ANNsfor the calibration of the CyberGlove. First, ANNs have beensuccessfully applied for the calibrations of many systems inmany different engineering fields. In [19], a neural network isused to learn the mapping between the actual distorted image
points and the corresponding pinhole camera image points to
reduce the nonlinear effects of an imaging system based on
CCD cameras. In [21], an industrial prototype microwave sixport instrument has been calibrated using the ANN techniquewhich is able to achieve high accuracy over a wide dynamic
range. In [20], R. Anchini et al. propose a neural network based
method for the reconstruction of 3-D coordinates of object
using the 2-D coordinates in two images planes from a dualcamera measurement system. In [22], J. M. Dias Pereira et al.
apply an ANN for minimizing temperature drift errors ofconditioning circuits and the experimental results show asignificant improvement in the measurement accuracy. Second,
we have noticed that there are some direct and consistent
relationships between the human hand’s segment size and thecorrespondent sensor’s readings through the experiment. ANN
is a very powerful tool to find such underlying pattern or
relationships, even in a large dimension system. Third, once thefinal neural networks are found for each sensor, the generationof calibration data for any new subject is simple and fast. As
mentioned above, simplicity of the calibration is very important
for those patients with upper extremity weaknesses.
In our proposed method, the only external hardware needed
is just a regular 2-D digital camera. We take a picture of thesubjects’ hand against a chessboard. From these 2-D images,
we extract the sizes of the subjects hand segment. These handsegments sizes are the inputs for the neural networks. The
chessboard here is used as the reference for the normalizationof hand segments’ size, so the zoom of the camera or therotations of the hand relative to the chessboard do not affect thetesting results. For the training of ANN, the outputs are themanually collected calibration data for these subjects. Different
neural networks were trained with these experimental data forthe sensors on the CyberGlove. The selection of the final neuralnetworks for each sensor is based on the minimum meansquare error between the training sets and the evaluation sets.
In this section, the proposed method is elaborated in threesteps: Measuring hand segments sizes, collecting calibration
data, and training of the neural networks.
A. Measuring hand segments sizes
In order to calibrate the CyberGlove output, the manuallycalibrated results have to be related to the actual subject’s handsize. We decided to take a picture of the subject’s hand andcompute the size of the hand segments with Matlab’s image
registration tool. The procedure is as follows. Predefined andvisually identifiable locations on the hand, corresponding to thecentre of each joint, the tip of each finger and the center of thewrist flexion, are marked with a color dot (we used blue foreasy identification). The subject then places his/her handagainst a chessboard. For that, there is no strict requirement on
the camera except that the full hand image and chessboard iscaptured. This is also quite suitable for rehab patients, as no
physical movement or hand action is necessary to take thepicture. In Matlab, we create a same size chessboard as areference and use the image registration tool to find the linear
transformation between the raw image and this reference image
as shown in Fig. 3. The image pixel selection tool is then used
to get the positions of all the marked dots on the hand. Fromthese dot positions, we compute the length of the segments
defined in Fig. 2b. The segment sizes are normalized by using
their ratio with the diagonal of the aligned image frame. This
ensures that the measurements are independent of the camera
resolution.
We have collected 25 subjects’ right hand data for the
training and the evaluation of the neural networks (22 men and3 women, aged 22-38). The average length and standard
deviation for each segment is plotted in Fig. 4. For the same
segments, our collected hand parameters are generally
consistent with the hand anthropometric data from Buchholz et
al. in terms of the ratio of the segment to the hand length [24].
The small differences are due to the fact that our positioningof the wrist point is closer to the MCP joint. This generalconsistency supports our method of extracting the handparameters from a 2-D hand image. Different from popular
hand models, we do not consider the position of the carpometa-carpal (CMC) joint since it is not easy to be accurately
located visually.

Figure 3: (a) Original image, (b) Aligned image

Figure 4: Statistics of hand segment sizes based on 25 subjects.
References to the segments can be found in Fig. 2.

B. Collecting calibration data
Using the traditional technique that comes with standard
CyberGlove system, calibration data for subjects wereobtained by manually adjusting the gain and the offset of eachsensor using the device control utility of the CyberGlove
driver software. The device control utility provides a graphicalhand model which renders the hand configuration in real timeas the sensor’s parameters are being adjusted. Fig. 5 illustrates
the hand kinematic model used in our calibration. The
kinematic model for the middle, the ring, and the pinky fingers
are the same as that of the index finger. For sensors on eachfinger which measure the MCP, PIP, and DIP joints except the
thumb, a subject is asked to keep the correspondent joint atzero degree first to get the offset for the sensor and thenconstraint at 90 degree to get the sensor gain according to (1).
For some subjects who have difficulties in bending DP joints
to 90 degrees, the constraints are set to 60 degrees. After thegain and the offset appear fine for these static poses, thesubject then smoothly moves from one static pose to anotherstatic pose to make sure that there is no outlier situation during
the hand movements. Using the same software, Lu andHuenerfauth designed a more detailed protocol for participants
who are deaf and use American Sign Language [25]. Indeed,
any calibration methods proposed by other researchers can beused here to collect the calibration data for the training of theneural networks.
Using this “standard” method, each calibration takes
around 5 to 10 minutes for a healthy person. This tediouscalibration process is mainly caused by the coupling effects ofthe sensor gain and its offset for the final joint angle as
described in (1) and it tires the subject quickly. To minimize
the subject bias, we calibrate each subject 3 times and all thecalibrations start with a randomly generated calibration data.
Calibration does not stop until both the operator and thesubject agree that the calibration is satisfactory. The
calibration for the thumb usually takes a longer time comparedwith that of the other ones. The main difficulty with the thumbcalibration is that its movement simultaneously affects boththe thumb roll and the thumb-index sensors. Even with bigger
efforts, the subjects are generally more satisfied with thecalibration results of the index, the middle, and the ring
fingers.

(a)Figure 5: Hand kinematic model for calibration adapted from [25]. Refers
to Fig. 2a for sensor indexing(b)
Altogether, 75 sets of calibration data were collected forthe training of the neural network. The mean and the standard
deviation of the calibration data for all 19 sensors are plottedin Fig. 6a and Fig. 6b. Three sensors will not be covered inthis paper: Sensor 20 which measures how much the pinky
rotates across the palm toward the thumb, and Sensors 21 and22 at the wrist which measure pitch and yaw of the palm
relative to the wrist. Readings from Sensor 20 are notconsistent while Sensor 21 and 22 are not considered because
their relative positions to the hand vary every time the user
puts the glove on.
It is interesting to see that Sensor 11 for middle-index
abduction has the largest standard deviation for the sensor
offset. At the same time, it has the smallest mean for thesensor gain. Considering the coupling effects between thesensor gain and offset for the final joint angle as shown in (1),
we cannot conclude that Sensor 11 has the largest variation
among all the sensors across all the subjects. In fact, all 4
abduction sensors (Sensor 4, 11, 15, and 19) have a relatively
large variation for the offset but a small mean for the gain.
This is due to the fact that these abduction sensors are bendingat the rest position while other sensors are flat at the rest
position as illustrated in Fig. 2a. In this regard, the offset andgain of the sensor does vary for different hand sizes. It is
exactly this relationship between the hand size and thecorrespondent calibration results we are exploiting in thispaper.

Figure 6: Statistics of sensor values based on 28 subjects’ calibration
data: (a) offset, and (b) gain
TABLE I.

INPUT-OUTPUT RELATIONSHIP FOR NN TRAINING

Input
Hand Segments(Fig. 2b)

OutputSensor Number (Fig. 2a)

1,2,3,24

Index⁰5, 6, 7

4,5,6,24

Middle⁰

8,9,10

7,8,9,24

Ring⁰

12,13,14

10,11,12,24

Pinky⁰16,17,18

13,14,19,24

Thumb⁰1,2,3

19, 20,24

Thumb-index*

4

20, 21,24

Middle-index*

11

21, 22,24

Ring-middle*

15

22, 23,24

Pinky-ring*19

⁰3 sensors on each finger, *abduction sensor, Segment 24 is the hand width
calculated by adding segments 16, 17, and 18.

C. Training the Neural Networks
There are 3 sensors on each finger except the thumb for themeasurements of the outer, the middle, and the inner joints
respectively. For the thumb, there are also 3 sensors: the outer,the inner, and the roll sensors. Therefore, the networks used in
the experiments have 3 inputs and 6 outputs for each fingerrespectively. For each of the 4 abduction sensors, there are 3
hand segments contributing to the final joint angle. The
relationship between the input and the output of the training
neural networks are listed in Table I. Other machine learning
methods could be employed to learn the relationships but here
we use Artificial Neural Networks (ANN) given our previous
positive experience with them [26].
There are a total of 75 sets of calibration data from 25
subjects (3 calibrations each) for the combined training andtesting of the neural networks. The types of networks used inthese experiments are regular feed-forward networks. The first
obstacle faced was to properly choose the ANN architecture tosolve our problem. We automate this selection task by using
the workflow shown in Fig. 7. The architecture of theindividual ANN is selected at random within a range ofpossible architectures. We allowed either 1 or 2 hidden layerswith between 2 to 10 neurons each, and trained for 100
epochs, on 50-80% of the whole data (39 sets) – the rest wereset aside for testing. The choice of these parameter ranges are
believed to be sufficient for the type of relationship being
modeled, as suggested by ANN guidelines [27] [28].
Levenberg-Marquardt learning algorithm is used for its goodlearning time in our testing. This type of ANN generation andtraining was performed 50 times, keeping the 5 best networks,
the ones with the least mean square error on the validation
data compared with the original calibration results. The final
generated calibration data is the averaged results of these five
best neural networks. It should be noted that the wholeprocedure of obtaining calibration data, generating the ANNs,
training, and testing them will yield results specific to thesystem from which the data originated; they might not apply toother systems.
Data
set

Randomly split set
in Training_set
and Testing_set

Training
set

Generate randomarchitectures for ANN withrandom training parameters

Testing
Set

ANN

Test

>minimumSelect the best
ANNs

Figure 7: ANN architecture generation workflow

IV.

RESULTS AND DISCUSSIONS

The mean and the standard deviation of the error between
the final testing results (output of the ANNs for all subjects)
and the original calibration data are plotted in Fig. 8a and Fig.

8b respectively. For the sensor’s offset calibration, the averagemean error for all 19 sensors is 0.4642. Among them, four
abduction sensors have the largest error standard deviations.
This conforms to the original collected calibration data where
the abduction sensors also have the largest offset standard
deviations as shown in Fig. 6. Three thumb sensors haverelatively large error standard deviations compared with thoseon the other four fingers. For the analysis, we normalize theerror standard deviation in terms of its ratio to thecorrespondent sensor’s mean offset. The result is plotted in Fig.
9. In this regard, Sensor 1 (thumb roll) and Sensor 4 (thumbindex abduction) have the largest errors of 0.2586, and 0.2543
respectively. If we only consider the sensors on the index, themiddle, the ring, and the pinky fingers, the average error ratio
for the sensor offset is 0.0645. For the sensor’s gain calibration,
the average mean error for all 19 sensors is 0.0001. Three
thumb sensors have the largest standard deviation errors. Afterthe normalization, the ratio of error standard deviation to thecorrespondent sensor’s mean gain is plotted in Fig. 10. Sensor
11 (middle-index abduction) has the largest error of 0.3045. On
the other hand, referring to Fig. 6b, the mean of Sensor 11’s
gain is actually the smallest, so its effects on the final joint
angles are not so formidable. If we only consider the sensors on
the index, the middle, the ring, and the pinky fingers, theaverage error ratio for the sensor gain is 0.0731.
If we analyze the offset and the gain errors between thetesting results and the original calibrated data together, they arequite uniform for all the sensors on the fingers excluding the
thumb. For the thumb sensors, there are two main reasons forthe large error variations. First, we did not take into account theposition of the CMC joint in our hand parameter extraction
while it is directly related to the thumb roll and the thumbindex abduction sensor. Instead, we expected the neuralnetworks to learn the relationship through the sizes of
Segment 19, 20, and 24 (Referring to Fig. 2b). However, since
we were not strict on the position of the thumb when the hand
pictures were taken, the variances of Segment 19 do not reflect
the real situation faithfully. Second, as aforementioned, thecollected calibration data for thumb roll and thumb-index
abduction sensors are not as good as other sensors. Since boththe input and the output for the training of the neural networks
for the thumb sensors suffer from the reliability, it is not
surprising to have large error variations among the related
sensors.

(a)Figure 10: Ratio of the gain error standard deviation to the corresponding
sensor mean gain

A. Subjective Evaluation
The overall performance of the proposed method is tested
both on subjects whose data were used and on subjects whose
data were not used in the training process. From their handparameters extracted from the pictures, the corresponding
calibration data were generated using the trained neuralnetworks. Altogether, 15 subjects were involved in the testing.
Five of them were used in the training set. The subjects gave ascore for sensors on each finger with a value from 1 up to 5
inclusive, with 1 being the most negative answer, 3 being
acceptable and 5 being the most positive one. Five gestures
used in the manually collection of the calibration data were
evaluated. The average results are listed in Table II.
(b)
Figure 8: Statistics of test results and the original calibration data: (a)Offset error, and (b) Gain error

Table II: Subjective Evaluation Results
Posture

T

I

M

R

P

A

TA

Ave

4.70

4.38

4.5

4.54

4.25

4.42

4.62

4.48

4.62

4.08

3.66

4.21

4.20

4.25

4.54

4.23

4.41

4.0

3.79

4.04

4.29

4.5

4.41

4.20

4.0

4.38

3.95

4.45

4.25

3.95

4.16

4.16

4.29

3.87

3.91

3.92

3.83

3.79

4.33

3.99

T: thumb; I: Index; M: middle; R: ring; P: pinky;
A: abduction sensors; TA: thumb abduction sensors; Ave: Average

Figure 9: Ratio of the offset error standard deviation to the corresponding
sensor mean offset

In general, the results for all five postures are positive which
proves the relationship between the measured hand segment
size and the calibration parameters. It is reasonable that thefirst posture gets the highest average score since all the sensors
are in the zero state. Another noticeable fact is that the results
for the subjects in the training sets are better that those not in
the training sets. This suggests that more subjects are needed
for more distributed training sets. Posture 5 is mainly used forthe testing of the 5 abductions sensors. Interestingly, thisposture also affects the reading for the MCP joints since theseflexion sensors are bended in another direction. There areoccasionally not acceptable results for some joints.
B. Comparison with Existing Methods
Compared with the standard calibration technique for
CyberGlove, which takes about 5 to 10 minutes, our proposed
method is very simple to use and the experimental results arealso promising. The only external device needed is a 2-Ddigital camera to take the subject’s hand picture. The wholeprocess can be easily automated and lasts no more than aminute. This simplicity and quickness is extremely importantto the CyberGlove based haptic rehabilitation system where
the patient may have difficulties to finish the calibration if theprocedure takes too long or complex.
No direct comparison can be made between our work andthat in [12] and [13] since the collection of the calibration data
for the training of the neural network is different. In those two
papers, the authors focused on the coupling effects among thesensors while those coupling effects are not considered in thefactory manual calibration software used for our collection of
the training data. However, for the five tested postures, thecoupling effects among the sensors do not play importantroles.
Finally, it should be noted that the proposed method is notmeant to replace current techniques. Instead, it is a new
approach where any existing calibration techniques, including[15] and [16], can be used as input to the training of the neuralnetworks. We do believe the inclusion of these coupling
effects for the collection of the training data will further
improve the experimental results and expand the application of
the proposed method to more diversified hand postures.
V.

FUTURE WORK AND CONCLUSION

Currently calibration of the CyberGlove is time-consuming
and depends on the experience of the operator on thecalibration routine. A good calibration may take about 5~10
minutes for a single user due to the large number of DOFs of
the hand and the number of sensors on the data glove. Thistedious calibration process should be especially avoided in
VR-based rehabilitation applications where the patients may
not be able to complete the physical movements required forthe calibration. In this paper, we proposed a practicalcalibration method for the CyberGlove using neural networks.
The inputs to the neural networks are the sizes of the handsegments which can influence the corresponding sensors of the
CyberGlove, and the outputs are the offset and the gain foreach sensor. In this way, only a 2-D hand picture is required
for the generation of the required calibration data. Anotheradvantage of the method is that it is not a replacement of thecalibration techniques proposed by other researchers. Anyother calibration methods can be used for the collection of data
sets for the training of the neural networks.
In our current implementation, we have manually collected
25 subjects’ calibration data for the training of the neuralnetworks. Different neural networks with random parameters
have been generated with random sets of the training data andthe testing data. The best 5 neural networks for each sensor
which had the least mean square errors between the training
set and the testing set were saved. We’ve tested the resulting

neural networks for 15 subjects using 5 common handpostures. The subjective results are very positive. The error
analysis shows that the variations for the sensors on the index,
the middle, the ring, and the pinky finger are quite uniform
while the thumb and abduction related sensors have larger
variations. However, the performance for the subjects in the
training set is slightly better than those not in the training set.
This suggests that more distributive training sets are needed.
As we have noticed during the calibration process, thethumb-index abduction sensor and the thumb roll sensor are
the most time-consuming due to their coupling effects. We
have also noticed that the thickness of the human hand varies alot. We also did not consider the cross-coupling factors suchas adjacent MCP joints’ flexion movement on the abduction
sensors. We believe that the inclusion of these factors willfurther improve the results and expand the application of the
proposed method for more diversified hand postures. Finally,
we will automate the whole calibration procedure andintegrate it into our rehabilitation system.

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