Low Cost and Fast Development of 3D Printed Gloves for 10 Degrees

of Freedom Gesture Recognition

Antonio Pallotti

1,2

, Mariachiara Ricci

, Giancarlo Orengo

and Giovanni Saggio

Department of Electronics Engineering, University of Rome Tor Vergata, via Politecnico 1, 00133, Rome, Italy

Technoscience, San Raffaele University of Rome, via di Val Cannuta 247, 00166, Rome, Italy

Keywords: Sensorial Glove, Resistive Flex Sensors, 3D Printing, Hand Motion Capture.

Abstract: The decreasing cost allows easy access and diffusion of 3D printers even for domestic use in the same way

as 2D printers. The present work proposes the development of a sensorial glove in 3D printing, featuring low

cost, easy reproduction and replacement. A 3D desktop printer, that was able to extrude different plastic

materials, was used. In order to generate the geometric shape that best suited the hand anatomy, the 3D CAD

design was based on hand photos from the top and the sagittal section. The design of the glove includes the

sensor housings, which are pockets within which the sensor can slide during joint bending. The wiring of 10

flex sensor and the acquisition board designed for a Lycra glove were easily applied to the printed glove

without modification. The glove in 3D printing was able to control virtual or mechanical hands, which

provides for surgical, military, space and civil applications. The possibility to achieve waterproofing allows

the use in applications that require contact with solvents or water. A standard test applied to six healthy

subjects demonstrated that the proposed glove achieves performances, in terms of repeatability,

reproducibility and reliability, comparable to that of the other literature gloves.

1 INTRODUCTION

Man is being able to receive stimuli from the external

environment through the senses and to carry out

operations through actuators such as legs for

locomotion and hands for the grasping or

manipulation of objects (Liu, 2011). The cognitive

functions dedicated to the hands are those most

expressed by the brain and can be investigated

through the measurement and monitoring of motor

tasks. The instruments available for the automatic

measurement of hand movements were initially

mechanical goniometers used by specialized

therapists: these goniometers take a long time (up to

30 minutes) and provide measurements of an instant

and not of a movement or sequence of gestures.

The studies proposed sensors based on different

physical principles (Dipietro, 2008), optical (Li,

2011), magnetic (Dipietro, 2003), inertial and

magnetic (Lisini, 2017), resistive (Simone, 2007),

(Gentner, 2009, Saggio, 2016), assuming that the

support is an elastic fabric like Lycra or similar

materials. PCB technology has been also used to build

inertial based hand tracking systems (O’Flinn, 2015).

The diffusion and decreasing cost of 3D printers

allows easy access even for domestic use in the same

way as 2D printers. 3D printers have been already

employed to build part of silicon sensory gloves (Li,

2018), but never used to build the entire glove. The

present work proposes the development of a sensorial

glove in 3D printing at low cost, easily reproducible

and replaceable, with the possibility of waterproofing

in view of applications that require it.

2 MATERIALS

2.1 3D Printed Glove

A 3D desktop printer, model Makerbot Replicator 2,

that was able to extrude different plastic materials,

was used in this work. In order to generate the

geometric shape that best suited the anatomy of the

hand, hand photos from top and lateral view were

taken, to yield the edge of the hand and the height of

the glove in 3D printing. Solidworks was used for

the CAD design of the glove. As with the Hiteg glove,

the 3D printed glove was considered to be of standard

size. After the Hiteg glove (Sbernini, 2018) was worn,

Pallotti, A., Ricci, M., Orengo, G. and Saggio, G.

Low Cost and Fast Development of 3D Printed Gloves for 10 Degrees of Freedom Gesture Recognition.

DOI: 10.5220/0007566802410247

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 241-247

ISBN: 978-989-758-353-7

241

a first photo was taken, so that the back of the hand

was visible, and a second photo was taken sideways,

so that the profile of the hand was visible. The first

geometric edge of the worn glove was automatically

extracted by a Matlab code. The .fig image containing

the edge of the glove was converted into a .sldprt file

to be processed in Solidworks. An extrusion function

was performed starting from the geometric edge of

the glove with a thickness of 1 mm. Thanks to the

second geometric edge, extracted as for the first

geometric edge, it was possible to determine the

height of the extrusion. Because the anatomy of the

hand is such that the size of the distal phalanxes is

different from the size of the carpus, a linear function

was assumed for the second geometric edge which

passed through the tip of the middle index to the wrist

joint. The extrusion of the first geometric edge was

before carried out up to the height of the wrist and

subsequently cut, linearly, to the tip of the index.

Once the extrusion was cut out, the cavity was closed

with a 1 mm thick top. The 3D printed glove was

made in less than 5 hours from a single source file.

The file contains the instructions that the 3D printer

must perform to create the entire glove in a single

print.

The used printer has an extruder, which is able to

extrude solid with a thickness of not less than 1 mm.

The material chosen for molding was Ninjaflex

(thermoplastic polyurethane) from Ninjatek: once

printed and solidified, the material has an elasticity

proportional to the thickness of the laminated sheet,

or for a slab of size x and y, of height z, the greater is

the long elasticity (x, y), the smaller is the z

dimension. In the same way, for a rectangular base

wall, dimensions (a, b) and height c, with the same

height c, the greater is elasticity, the smaller the depth

a or the width b.

To make the glove more comfortable, drilling was

inserted along the main deformation axes or along the

median axis of the five fingers. The drilling allowed

a greater elasticity of the fabric and a greater

transpiration of the hand in the glove. The design of

the glove includes the housings for the flex sensors

(Orengo, 2014, Orengo, 2018) (Flexpoint Sensor

Systems Inc., South Draper UT, USA), which are

pockets or two foils within which the sensor can slide

during joint bending. The sensor was fixed to the base

in order to maintain the same position. The used

printer was a single extruder, so that it was possible

to extrude only one filament at a time. One of the

problems of 3D printing is the creation of suspended

or bridged sections or sections that have no other

material to lean on. Two extruder printers use a

printing extruder and a support extruder that works in

parallel and prints a support that supports the

suspended parts and is soluble in hot water. The

melting temperature of the Ninjaflex, once printed, is

60 degrees. Despite only one extruder, the glove was

made as designed and the excess filaments (due to the

printer) were removed.

Figure 1 shows a picture of the CAD design, and

Figure 2 a photo of the realized glove and the wiring

of the flex sensors, taken from a Lycra glove

(Sbernini, 2018) and applied to the printed glove

without any modification. The 3D printed glove

allows the control of hand virtual limbs, as shown in

Figure 3, where the movements of the hand wearing

the printed glove simultaneously control a virtual

avatar and a robotic hand (Saggio, 2014), for surgical

(Saggio, 2015, Sbernini, 2018), military, space and

civil applications (Dipietro, 2008).

Figure 1: CAD design of the glove.

Figure 2: Flex sensors’ wiring (left down) and 3D printed

prototype (right up).

BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices

242

Figure 3: Flat position (top) and closed hand (bottom) of the

3D printed glove (left) driving simultaneously a virtual+

hand (center) and a mechanical hand (right).

2.2 Hardware

In order to show the ease of realization and use of the

3D printed glove, a ready-made apparatus composed

of the flex sensor wiring and the acquisition board,

developed from the Health Involved Technical

Engineering Group (HITEG) for a Lycra glove, was

removed and inserted into the printed glove without

any modification. The sensors were inserted into the

ready and printed pockets. In this way, replacement

of the glove in case of damages or need of different

sizes is easy, fast and cheap.

The board, which is shown in Figure 4, is drawn

in Altium Designer, has a sampling frequency of 1

KHz, the analog-to-digital resolution of 12 bits (range

0-3.3V), and communicates with the computer via

USB or Bluetooth links with 64 bytes packages. In

order to transduce the resistive variation signal

coming from the glove sensors into an electrical

potential variation, the board has 32 voltage dividers,

one for each data line. The circuit provides galvanic

isolation between the connection of the sensors and

the parts in direct contact with the computer, in order

to prevent unwanted electrical discharges onto the

subject. The circuit can drive step motors, typically

present in electromechanical prostheses for the

movement of the ends, by inserting an optional

external module called “Motor control”. The logic of

the acquisition and control board was completely

managed by a PIC 24EP512GU810 microcontroller

(Microchip). In this case, the board was powered by

the USB cable used for data transmission, otherwise,

for wireless operation, it needed a battery. The board

used in the present work was therefore oversized,

because compatible with sensory gloves featuring up

to 32 inputs from resistive sensors: considering that

the hand has 27 degrees of freedom (DoF), one can

also measure the movements of the wrist. A board

designed specifically for this job would have

occupied a smaller space, which could be integrated

into the carpus of the hand itself. The photo of the

entire system, composed of the electronic board and

the glove, is shown in Figure 5.

Figure 4: HITEG acquisition board for flex sensory gloves.

Figure 5: The measuring system: the sensory glove and the

HITEG acquisition board.

3 TESTING METHODS

Six healthy subjects were involved in the Wise test

(Wise, 1990, Dipietro, 2003), 2 males and 4 females,

with an average age of 4020 years. The glove was

worn by the hand and the electronic board was placed

on the forearm. The measurement system consists of

two areas: an area to place the hand flat on the table

and an area to grab a large mold. The subject sits on

a chair with his back resting against the back of the

chair and his hand resting on the table. The test setup

is shown in Figure 6. Before starting the test, the

subject became familiar with the glove in 3D printing.

All the sensors were checked to fit the glove, so that

all the flexed extensions of the metacarpal joints and

proximal interphalanges were detected: for the

thumb, the distal and proximal interphalangeal joint

were measured.

Microcontroller

Bluetooth

port

Sensor

inputs

Voltage

divider

LED

USB

port

Galvanic

isolation

Low Cost and Fast Development of 3D Printed Gloves for 10 Degrees of Freedom Gesture Recognition

243

Figure 6: The measurement protocol consists of two

positions, one open-handed (top) and one gripping a

cylindrical mold (bottom). The mold gripping position

corresponds to task A and B , the flat hand position

corresponds to task C and D.

The measurement protocol consisted of two tests.

The first test, performed to evaluate repeatability, was

composed of the task A and C. In the task A, the

subject placed the hand on the mold and grasped it, 6

seconds were recorded in this position, then, in the

task C, the subject places the hand resting on the table

and 6 seconds were recorded in this position. This

test was repeated 10 times (or 10 trials): this set of

measures was called a block. Both in task A and task

C, the measurement system was never removed from

one block to another. In the second test, performed to

evaluate reproducibility, the glove was removed and

worn again by the subject. This test was composed of

the tasks B and D, which were the same of the tasks

A and C, respectively.

The model adopted to study the behaviour of the

flex sensors that make up the glove was the linear one:

in task A and C a single calibration was sufficient

before starting the measurement protocol. Calibration

was performed by acquiring the average value on a 6-

second window, while the hand was flat in the resting

position on the table. The value identified was the

value of Digital Minimum. The value of Digital

Maximum was detected by placing the hand on the

mold and grasping it for 6 seconds. The average value

on this 6-second window was the Digital Maximum.

To determine a correspondence between the line of

angles expressed in degrees, for each articulation, and

the line expressed in digital values, for each sensor, a

mechanical goniometer with a sensitivity equal to 1

degree was used. In this way, it was possible to

convert the range of digital values coming from the

ADC of the electronic board, in the range of angular

values measured mechanically with the goniometer.

4 RESULTS

4.1 Repeatability and Reproducibility

Testing

The developed code organized data in the respective

5-dimensional Working matrix of the joint angles

computed by the two measurement devices, indexed

by the trial number (10), block number (10), joint

number (10), position number (4) and subject (6).

Then, for each position and each subject, an array





,   1,… ,10,   1, … ,10,   1,… ,10 was

finally obtained for the i

trial, in the j

block and

related to the k

sensor. Another code provided

tabular Wise-based Range and SD values for each

subject and the mean of Range and SD values across

all participants. Only the average values are shown in

the present study. For each subject and each test, we

defined the Range as:











jk jk

kj j

RmaxX minX

(1)

where





ijk

(2)

is the average across the trials of each block. Then the

mean of 



for each position was calculated across all

joints. The standard deviation (SD) of the 





values

was calculated across the blocks, then the average

across the joints.

To evaluate repeatability (task A and C) and

reproducibility (task B and D), Table 1 compares the

full Range and SD values computed across all trials

of one block, then the average across all blocks, all

joints and finally all subjects, resulted from the 3D

printed glove. Analysis results of Table 1 are

compared with other gloves in literature based on

resistive flex sensors (RFS) by Simone (2007) and

Gentner (2009), inertial sensors (IMU) by Kortier

(2014) and O’Flinn (2015), fiber optic sensors (Opt)

by Wise (1990), Hall effect sensors (Hall) by Dipietro

(2003), Optical linear encoder (OLE) by Li (2011).

The mean SD across all subjects through the Wise test

is reported in Figure 7 for each finger joint.For the

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244

same measurements, data correlation between Range

and SD values are reported in Table 2.

Table 1: Comparison of repeatability (task A, C) and

reproducibility (task B, D), expressed as Range and SD

values resulting from the Wise test, between devices with

different sensor technology in literature and the 3D printed

glove.

Device

Task A Task B Task C Task D

Mean

Value

Range SD Range SD Range SD Range SD Range SD

printed

glove

5.94 2.03 9.04 3.67 2.44 1.2 5.77 1.95 5.80 2.21

Gentner 6.09 1.94 7.16 2.26 2.61 0.86 3.98 1.28 4.96 1.59

Wise 6.5 2.6 6.8 2.6 4.5 1.6 4.4 2.2 5.6 2.3

Dipietro 7.47 2.44 9.38 2.96 3.84 1.23 5.88 1.92 6.65 2.14

Simone 5.22 1.61

1 0.5

3.36 1.05

Kortier 1.8 0.6

1.1 0.4

1.5 0.5

Li 4.56 1.57

2.02 4.56

3.29 3.07

O' Flinn 7.54 2.11

2.27 1

4.9 1.56

Figure 7: Comparison of repeatability, expressed as Mean

Standard Deviation across all subjects through the Wise

test, between finger joints for the 3D printed glove.

Table 2: Comparison of correlation values between Range

and SD through the Wise test between the 3D printed glove.

Device CorrA CorrB CorrC CorrD

3D

printed

glove

0.988 0.996 0.999 9.984

4.2 Reliability Testing

The reliability between measures in each test was

assessed by intraclass correlation coefficients (ICCs).

ICC values were calculated for each test by randomly

choosing two trials out of two randomly chosen

blocks for each subject. The average angles of the 6

seconds of the two trials were calculated for each

subject. Then, the angle pairs of each joint from all

subjects were pooled together and an ICC was

calculated for each joint (Dipietro, 2003).

The ICC calculation was based on the comparison

of between-subject and within-subject variance,

where the within-subject variance reflects

measurement errors. If within-subject variance is low,

the ICC approaches 1 and the measurements are

considered as reliable. Conversely, if the ICC

approaches 0, a large fraction of variance is explained

by measurement errors (indicating a low reliability).

The mean out of 20 ICC calculations for each joint

was used as a measure of joint sensor reliability.

Thus, for each joint, four ICC values (one for each

test) existed. The mean ICC for each joint across tests

served as a measure of reliability for a specific joint.

ICC values are reported in Table 3, which are

comparable to gloves evaluated by Dipietro (2003),

Gentner (2009), Simone (2007), and Li (2011),

although in this study the test procedure was

somewhat different. Consequently, the repeatability

and reliability of the HITEG glove is similar to other

evaluated gloves, and also lies within the

measurement reliability of manual goniometry (Wise,

1990).

Table 3: Comparison of reliability, expressed as intraclass

correlation coefficients (ICCs) resulting from the Wise test,

between devices with different sensor technology in

literature and the MYO armband.

Device

Sensing

tech

ICC

Min Max Mean

3D

printed

glove

RFS 0.69 0.83 0.73

Gentner RFS 0.87 0.98 0.93

Dipietro Hall 0.7 1 

Simone RFS 0.79 1 0.95

Li OLE 0.88 0.99 0.95

5 DISCUSSION

5.1 Test Results

The mean Range and SD values obtained in Table 1

are lower than those obtained by Dipietro (2003) with

Hall sensors, but higher than Wise (1990) with optical

sensors, higher than Gentner (2009) and Simone

(2007) with resistive flex sensors, and much higher

than O’Flinn (2015) and Kortier (2014) with inertial

sensors, which get the best results but with an

expensive apparatus. In the linear model of the glove,

the proximal thumb finger is the one with the highest

SD. It should be noted that Simone does not provide

the results for the C and D tests: if the two tests had

been excluded from our protocol, it would have

performed a mean Range of 4.19 and a mean SD 1.62.

The tasks C and D have lower values than the

corresponding A and B and this is consistent with the

Low Cost and Fast Development of 3D Printed Gloves for 10 Degrees of Freedom Gesture Recognition

245

previous studies: placing the hand in a rest state

introduces a lower error in terms of reproducibility

and repeatability than the grasping of a mold, which

may occur from time to time with not negligible

variations. The Range values from B to A and D to C

are higher, and this result is also consistent: removing

the glove introduces reproducibility errors. The

Range-SD correlation values for the whole test are

consistent with the previous studies (Gentner, 2009).

These results indicate a linear relationship between

the Range and SD and furnishes a comparable

estimation of measurement repeatability. The

ICC=0.73 for the glove indicates a reasonable

reliability.

5.2 Technical Improvements

If a printer capable of making holes below 1 mm size

had been used, it would be possible to make a smaller

and more diffused drilling along the whole fabric: this

would allow a further study of elasticity of the fabric

with respect to the geometry of the holes (circle,

square, star, sigmoid, etc.). However, drilling could

result unnecessary using a more efficient extruder, to

obtain 0.5 mm thick substrate, or a more elastic

filament. In fact, an advantage in making a glove in

3D printing is the possibility of waterproofing: the

glove can be printed as a single fabric without seams

or welding or use of glues. Being a single plastic

fabric, made according to the anatomy of the hand, it

can be impermeable to water and then used in new

applications, where the man is in contact with

solvents or in applications in contact with water.

The 3D printed glove proposes applications in

new environments where the natural hand can already

operate, or in environments where there is no risk for

the human being. A hand in boiling water suffers

burns as a result of scalding. The 3D printed glove, as

a sensory glove, was not designed to have thermal

insulation. If the 3D printed glove was immersed in

100 degrees of boiling water, the hand itself would

suffer burns. The 3D printed glove proposes a new

fabric and a new manufacturing technique. Studies on

heat transmission problems of ambient-hand can be

carried out in future works. The Ninjaflex producer

(Ninjatek) declares a glass transition temperature of

35 °C and a melting point of 216 °C. In the future,

studies of the effects of pressure, temperature,

humidity on the 3D printed glove could be carried

out. To study the effects of these parameters on the

glove worn by human hands in order to assess their

safety, there must first be an approval by the scientific

and ethical committee.

The proposed 3D printed glove could be a new

fabric to be used in the measurement of hand

movements, but currently the studies are limited only

to kinematics, and do not investigate other sectors

such as chemistry. The 3D printed glove has printed

pockets, where the bending sensor can be inserted

even during printing. In this case, the sensors were

inserted once the 3D printing finished the process.

Likewise, the wiring can be allocated between two

layers of material during 3D printing. In this case, as

a first work, the wires are visible, because the Hiteg

sensor glove was reproduced, using the same sensors,

wires and electronics, but changing the material of the

glove's fabric. The glove with wire communication is

waterproofed, if the electronics is in a non-aquatic

environment. In order for the electronic board to be

wearable in an aquatic environment, the electronics

must be waterproofed (starting from the case), so that

the electrical safety requirements are respected.

6 CONCLUSIONS

The present work proposes the development of a

sensorial glove in 3D printing at low cost, easily

reproducible and replaceable, equipped with 10 flex

sensors. The glove design was based on hand photos,

thus allowing customization of the glove shape and

size, to fit the user hand. The design of the glove

includes the housings for the sensors, which can be

developed separately and then easily replaced, or

reused in case of damages of the glove material or

need of different glove sizes. The choice of drilling

shape was circular, but in a future work one might

think to check the influence of the drilling geometry

with respect to the performance of the glove in 3D

printing. In view of applications that require it, the

glove can be printed as a single fabric, without seams

or welding or use of glues to obtain waterproofing,

and then used in new applications, where the user is

in contact with solvents or water. In a future work, it

will be possible to insert the sensor during the

molding phase, so that it will be fixed by the printed

glove without the need for stitching.

The glove in 3D printing also allowed the control

of virtual or mechanical hands for surgical, military,

space and civil applications.

The performances of the first prototype, evaluated

with a standard test, showed the same degree of

accuracy of the compared devices, except when the

glove was removed and worn again, demonstrating

low reproducibility due to needed improvements in

glove realization, such as a more efficient extruder to

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obtain 0.5 mm thick substrate or more elastic

filaments.

REFERENCES

Liu, H., 2011. Exploring human hand capabilities into

embeeded multifingered object manipulation. IEEE

Transactions on Industrial Informatics. 7 (3), 389-398.

Dipietro, L., Sabatini, A., M., Dario, P., 2008. A survey of

glove-based systems and their applications. IEEE

Trans. on Systems, Man, and Cybernetics - Part C:

Applications and Reviews. 38 (4), 461-482.

Li, K., Chen, I-M., Yeo, S., H., Lim, C., K., 2011.

Development of finger-motion capturing device based

on optical linear encoder. Journal of Rehabilitation

Research and Development. 48 (11), 68-72.

Dipietro, L., Sabatini, A., M., Dario, P., 2003. Evaluation

of an instrumented glove for hand-movement

acquisition. Journal of Rehabilitation Research and

Development. 40 (2), 179-190.

Lisini, Baldi, T., Scheggi, S., Meli, L., Mohammadi, M.,

Prattichizzo, D., 2017. GESTO: a glove for enhanced

sensing and touching based on inertial and magnetic

sensors for hand tracking and cutaneous feedback.

IEEE Trans. on Human-Machine Systems. 47 (6), 1066-

1076.

Simone, L., K., Sundarrajan, N., Luoc, X., Jia, Y., Kamper,

D., G., 2007. A low cost instrumented glove for

extended monitoring and functional hand assessment.

Journal of Neuroscience Methods. Elsevier. 160, 335-

348.

Gentner, R., Classen, J., 2009. Development and evaluation

of a low-cost sensor glove for assessment of human

finger movements in neurophysiological settings.

Journal of Neuroscience Methods. Elsevier. 178, 138-

147.

Saggio, G., Pallotti, A., Sbernini, L., Errico, V., Di Paolo,

F., 2016. Feasibility of Commercial Resistive Flex

Sensors for Hand Tracking Applications. Sensors &

Transducers. IFSA. 201 (6), 17-26.

O'Flinn, B., Sanchez, J., T., Tedesco, S., Downes, B.,

Connolly, J., Condell, J., Curran, K., 2015. Novel smart

glove technology as a biomechanical monitoring tool.

Sensors & Transducers. IFSA. 193 (10), 23-32.

Li, M. X., Wen, R., Shen, Z., Wang, Z., Luk, K., D., K., Hu,

Y., 2018. A wearable detector for simultaneous finger

joint motion measurement. IEEE Trans. on Biomedical

Circuits and Systems. 12, (3), 644-654.

Sbernini, L., Quitadamo, L., R., Riillo, F., Di Lorenzo, N.,

Gaspari, A., L., Saggio, G., 2018. Sensory-glove-based

open surgery skill evaluation. IEEE Transactions on

Human-Machine Systems. 48 (2), 213-218.

Orengo, G., Lagati, A., Saggio, G., 2014. Modeling

wearable bend sensor behavior for human motion

capture. IEEE Sensors Journal. 14 (7), 2307-2316.

Orengo, G., Saggio, G., 2018. Flex sensor characterization

against shape and curvature changes. Sensors &

Actuators_A_Physical. 273, 221-231.

Saggio, G., Bizzarri, M., 2014. Feasibility of teleoperations

with multi-fingered robotic hand for safe

extravehicular manipulations. Aerospace Science and

Technology. Elsevier. 39, 666-674.

Saggio, G., Lazzaro, A., Sbernini, L., Carrano, F., M., Passi,

D., Corona, A., Panetta, V., Gaspari, A., L., Di Lorenzo,

N., 2015. Objective surgical skill assessment: an initial

experience by means of a sensory glove paving the way

to open surgery simulation?. Journal of Surgical

Education. Elsevier. 72 (5), 910-917.

Wise, S., Gardner, W., Sabelman, E., Valainis, E., Wong,

Y., Glass, K., Drace, J., Rosen, J., M., 1990. Evaluation

of a fiber optic glove for semi- automated goniometric

measurements. Journal of Rehabilitation Research and

Development. 27 (4), 411-424.

Kortier, H., G., Sluiter, V., I., Roetenberg, D., Veltink, P.,

H., 2014. Assessment of hand kinematics with inertial

and magnetic sensors. Journal of NeuroEngineering

and Rehabilitation. BioMed Central. 11:70.

Low Cost and Fast Development of 3D Printed Gloves for 10 Degrees of Freedom Gesture Recognition

247