An IOT based Wearable Smart Glove for Remote Monitoring of

Rheumatoid Arthritis Patients

M. Raad, M. Deriche, A. Bin Hafeedh, H. Almasawa, K. Bin Jofan, H. Alsakkaf, A. Bahumran and

M. Salem

King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

Keywords: Arduino, Rheumatoid Arthritis, Therapy, Smart Glove, E-textile, IOT, Bluetooth.

Abstract: Rheumatoid Arthritis is a disabling and painful disease of finger joints affecting mainly the elderly people

and requiring continuous medications and physical therapy. Traditional arthritis measurements require labor

intensive examination by clinical staff. These manual measurements are inaccurate and subject to observer

variations. Nowadays the use of wearable technologies is spreading and rapidly becoming a trend

promising to offer key benefits in the management of chronic diseases especially at home. Here, we

propose an affordable Smart Glove instrument for assisting physiotherapists in remotely analysing patients

finger flexions when performing diverse activities at home. An E-textile based glove uses flex and force

sensors, and an Arduino platform to transmit motion data to the physiotherapists using a smart phone using

a dedicated App. The flex sensor on the index finger detects and estimates the motion, a BLE (Bluetooth

Low Energy) Nano is used for processing and wireless transmission. The wearable smart glove uses a

lithium ion 3.3V rechargeable 400mAH battery for consuming power. This Smart Glove also helps in

monitoring the patient’s response to either medication and/or diverse recommended movements. The data

collected can be used to analyse the status of the patient with time and also in assisting care givers to change

planned activities or exercises when needed.

1 INTRODUCTION

Improving the efficiency and quality of health care

services in both hospitals and homes, has always

been very important and challenging at the same

time. Among the different diseases affecting the

elderly people, in particular, is Rheumatoid Arthritis

(RA). This disease is an autoimmune disease, that

results in stiffness, swelling and possibly deformity.

Stiffness may arise from physical damage around the

joints, and the symptom of stiffness is quantified by

the degree of difficulty in joints movements.

Stiffness intensity varies between patients and

occurs most commonly in the hands (O'Flynn et al.,

2013).

Clinical manifestations of RA can be confused

with similar unrelated musculoskeletal and muscular

disorders. Outcome measures such as Disease

Activity Score (DAS) and Health Assessment

Questionnaire (HAQ), are used to asses the patient’s

disease severity. These measures are partly

subjective and can be influenced by other factors

such as depression or unrelated non-inflammatory

conditions. Patients suspected to suffer from RA are

at first examined by an Occupational Therapist (OT)

to quantify joint Range Of Motion (ROM) and hand

function. Traditional objective measures of RA

using the universal goniometer (UG) and visual

examination of the hands is labor intensive and open

to reliability problems and human bias.

Consequently, there is a crucial need to use

technology like wearables to detect stiffness of

fingers. Moreover, emerging technologies like

Internet of Things (IOT), is expected to offer

advanced connectivity of devices, systems, and

services that go beyond machine-to-machine

communications. As a part of reaching every sector

with the connectivity of IOT, eHealth services are

gaining a lot of grounds. Primarily, eHealth is a

healthcare practice supported by electronic processes

and wireless communications (

Chatterjee et al.,

2015

),(Holler et al., 2014) and (Connolly et al., 2018).

Such technology is helping doctors in remotely

monitoring their patients and treating them even

when they are not in hospital. One application of

224

Raad, M., Deriche, M., Hafeedh, A., Almasawa, H., Jofan, K., Alsakkaf, H., Bahumran, A. and Salem, M.

An IOT based Wearable Smart Glove for Remote Monitoring of Rheumatoid Arthritis Patients.

DOI: 10.5220/0007573302240228

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 224-228

ISBN: 978-989-758-353-7

major importance is home based motion sensing

specially for the elderly people. Wearable sensors

can be used to monitor patients at home and track

their movements (OQuigley et al., 2014).

Researchers have discussed E-textile devices for

data collecting to support work on Parkinson’s

disease. Parkinson Disease (PD) is a

neurodegenerative motor disorder that targets and

breaks down the nervous system. It occurs more

frequently with the elderly people. Affected

individuals can become unable to perform fine

motor movements of hands and arms. Collecting

objective movement data from a device such as a

smart textile can help in accurately monitoring the

patient state (Plant et al., 2014). Also, patients with

post-strokes can suffer from hand disabilities and

would benefit from Smart Gloves during

rehabilitation (Hidayat et al., 2015). Several smart

clothes were developed for tracking the activities of

users by using textile-based sensors for monitoring

deformation along textile, positions, angles, and

accelerations of body segments or joints during

motion (Goncu-Berk et al., 2017). In (Jung et al.,

2017), the researchers developed the RAPAEL smart

glove by involving video games to help patients in

their rehabilitation process at home. Various

sensitized gloves have been discussed in the

literature, for example, gloves that track hand and

finger motion for providing feedback to

rehabilitation systems (Escoto et al., 2017). For a

review of wearable sensors, the reader is directed to

read the survey by Duarte Dias in (Dias et al., 2018).

In this paper, we propose a novel Smart Glove

design that provides accurate readings and send

relevant information to doctors especially

physiotherapists enabling them to monitor patients

and provide them with the most suitable

prescriptions. The Smart Glove has several

therapeutic functions. One of these functions is to

provide doctors with flex related measurements

through simple smart phone applications. An

additional novel feature that is added is the ability to

measure hand gripping capabilities of patients by

holding house hold objects like a tea cup for

instance, while the patient is holding a cup of tea,

the therapist can monitor remotely the rehabilitation

process of the patient by looking at the data sent

from the gripper sensors through the smart glove.

The proposed smart glove can be used for several

other purposes as will be discussed in the next

sections. It is worth noting that the proposed system

has been implemented using off-the-shelf components

which were combined with our algorithms for data

analysis and information transmission.

2 METHODOLOGY

This work focuses on the development of a Smart

Glove system for helping the elderly people at home

suffering from joints movement disability. The main

objective is to design, implement and test a device

for remotely monitoring hand and fingers

movements. The system uses Smart Glove and a

multitude of E-textile sensors to measure the range

of motion (ROM) of fingers, and a microcontroller.

This system can collect and send rehabilitation

related data to physiotherapists. The microcontroller

allows the control of the activity of the Smart Glove

in an easy and effective way. The Smart Glove is

connected to a Bluetooth Module for observing the

state of patient`s palm and alerting the

physiotherapist if an error or an abnormality has

occurred. The main advantage of the proposed

solution is its simplicity, cost-efficiency, and

scalability with home based IOT systems. The whole

proposed system costed less than 100$ to build and

has low power requirements, compared to the

commercial Rapael Smart glove for arthritis Rehab,

which has a rental cost of 99$/month, and a total

cost for hospital amounting to 15,000$.

Nevertheless, the proposed smart glove is only

intended to be worn while collecting measurements.

Additional research is needed to make the system

more user friendly and non-invasive, in addition to

collecting patient’s data in a clinical setting with the

aid of a physiotherapist.

Figure1: System Design.

We display in Fig1 the overall proposed system. The

Smart Glove comprises two flex sensors and one

force sensor. Finger motion is measured by a flex

sensor while the force sensor measures the applied

pressure on each finger and transfers all these data to

the microcontroller. The Arduino Lilypad processes

the data and sends them to a physiotherapist by

using a Bluetooth module. In this research, we use

LilyPad Arduino, which is designed for E-textiles

and wearables e-health applications. It can be sewn

to fabric and similarly mounted power supplies,

sensors and actuators with conductive thread. Two

E-textile force sensors and one E-textile flex sensor

were used only due to the limited I/O ports on the

An IOT based Wearable Smart Glove for Remote Monitoring of Rheumatoid Arthritis Patients

225

Lilypad Arduino. It has an Analogue /Digital (A/D)

converter built on the chip and a clock speed of 8

Mhz. The Main Board is based on the

ATmega168V (the low-power version of the

ATmega168). The Arduino board has a USB

connector to enable it to be connected to a PC to

upload or retrieve data. The board exposes the

microcontrollers I/O (Input/output) pins to enable it

to connect those pins to other circuits or to sensors,

etc. The Bluetooth module used in this research is

called BlueSMiRF silver and it uses the RN-42

module. A Smart Glove system is developed in order

to effectively demonstrate the activity daily living of

a patient including the grasping of objects for a

certain task. The process involving transmitting

electrical signals from the sensors to the LilyPad

Arduino to analyse the data is the most challenging

task in the circuit design. As shown in Fig. 2

(Gloves), the flex and force sensors are attached on

the Smart Glove. The hand glove incorporates a

sensory system which can detect the force of

grasping any object and the finger degree of

bending.

3 EXPERIMENTAL RESULTS

Fig. 3 shows the Smart Glove sensor readings as

shown in the Smart Phone of the therapist for remote

monitoring of the patient’s hand joint movements.

The glove has been synthesized from normal

stretched clothe and the sensors were glued on this

layer. Another nylon layer covers and protects the

sensors. The flex and force sensors were calibrated

based on instructions provided in the datasheet.

Figure 2: Smart Glove Prototype.

The LilyPad Arduino microcontroller has been

used in this device to process and control signals

generated from sensors. The Arduino

microcontroller is powered by a lithium ion

rechargeable battery embedded in the gloves. The

Arduino microcontroller processes the raw data

collected from sensors and transmits it to the mobile

screen as shown in Fig. 3. In addition to monitoring

the progress of rehabilitation through improvement

of hand joint motion based on the prescribed

exercises, the therapist or the doctor can also

monitor the gripping capability of the patients. The

system was tested successfully in the lab. Further

extensive lab experimentation is planned to evaluate

the performance of the smart glove utilization in a

clinical setup in collaboration with a physiotherapist

in a nearby hospital. The open platform software for

Arduino was used compatible to C language. This

feature with the advent of IOT opens lots avenues as

this valuable data can be sent on the spot or later to a

therapist for analysis which saves lots of physical

interaction with the therapist and hence decreases

the cost. Fig. 4 shows the Analog data (ADC) is

directly proportional to the force (Newton) of the

force sensor. This is vital to detect the gripping force

needed based on the physiotherapist plan,

particularly in monitoring the activity of daily living

like making tea. Also, the experiment shows that the

flex's resistance is directly proportional to the

bending as shown in Figure5. The flex sensor is a

carbon strip that measures the resistance which is

directly proportional to the amount of bending and

deflection as shown in Fig. 5. The force sensor is

basically a variable resistance that changes its value

depending on the amount of pressure applied. The

experiment shows that when the flex sensor is bend

inward, resistance value increased significantly as

the angle of flex sensor is bend further. However,

when it is bent outward, the resistance value

decreased gradually. These preliminary findings

suggest that flex sensor is clearly suitable to detect

finger bending angle by utilizing inward bend of the

flex sensor.

Figure 3: Sensor readings as shown in the smart phone.

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Figure 4: Force sensor, Newton Vs ADC.

Figure 5: Flex sensor, Resistance vs Bending Degree.

4 CONCLUSION

We discussed in this paper a Smart Glove based

system for monitoring joints and fingers movements

for patients suffering from RA. The system is useful

from remote monitoring of patients staying home.

The system enables doctors to diagnose and identify

the states of mobility and joints stiffness of the

patients without requiring them to be present at the

hospital. The system provides the doctors with

accurate readings of the flexion and applied force

via a smart phone. With the advent of IOT &

communication technology like 5G, it opens lots of

avenues for E-health and results in cost saving. The

system was tested successfully in the lab both for the

force and flex sensors utilizing volunteer students.

Nevertheless, further research need to be done in the

future in collaboration with a physiotherapist in a

clinical setting. The main limitation of this research

was the limited input/output interface of the used

Arduino microcontroller which limited the number

of sensors used to detect symptoms of the disease or

immobility. Work is in progress to accommodate

more sensors and add machine learning algorithms

on top to give the proposed solution the unique

feature of learning and adapting with the patient

situation.

ACKNOWLEDGMENTS

The authors would acknowledge the support of

KFUPM for this research.

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