Tele-tDCS: A Novel Tele-neuromodulation Framework using

Internet of Medical Things

Samuel S. D. Herring

, M. A. Hannan Bin Azhar

and Mohamed Sakel

School of Engineering, Technology and Design, Canterbury Christ Church University, U.K.

East Kent Hospitals University, NHS Foundation Trust, Kent, U.K.

Keywords: tDCS, Neuromodulation, Parkinson’s, IoMT, Tele-health, Biomedical Device, e-Health.

Abstract: As part of the Internet of Medical Things (IoMT) within Biomedical Engineering, telehealth is an emerging

field. Due to the recent events surrounding COVID-19, it has become obvious that Telehealth treatments must

be developed as a means of protecting vulnerable patients in hospitals by reducing the need to visit and

therefore reducing risk to physicians. This paper investigates the feasibility of developing a non-invasive

remote neuro-stimulation system using internet-based transcranial Direct Current Stimulation (tDCS). A

hardware-based prototype tDCS device has been developed to be controlled using a remote command-line

interface over the internet. As a result, a physician can remotely set the parameters for the tDCS treatment

and monitor the treatment in real-time to ensure patient safety. In this study, the feasibility of a Tele-tDCS

system was investigated, as well as the capabilities a Tele-tDCS system should offer to patients.

1 INTRODUCTION

Telehealth is an evolving field, a part of the Internet

of Medical Things (IoMT) within Biomedical

Engineering. In current society, it is of growing

significance to develop such systems, as it allows

patients to be treated remotely by physicians.

However, models must be in place to ensure that the

treatments may be performed appropriately, taking

into consideration the security risks associated with

the IoMT (Hall et al., 2014). Recent events involving

COVID-19 make it clear that there is a need for

telehealth treatments to be developed because of the

benefits of protecting vulnerable patients by reducing

the need for visits to hospitals and other clinical

settings, and also reducing risk to physicians through

less physical patient contact (Smith et al., 2020).

Transcranial Direct Current Stimulation (tDCS)

has been used in neurorehabilitation for many years

to effectively increase or decrease mental function

and learning (Bucur et al., 2018) and it is considered

to safe and widely accepted (Bikson et al., 2016). The

use of tDCS for treating neurological disorders such

as Parkinson's disease and other movement-related

disorders has been considered in several studies

https://orcid.org/0000-0003-1190-6644

https://orcid.org/0000-0001-6749-5229

(Boggio et al., 2006; Lefaucheur et al., 2017). It has

been demonstrated that such treatments are effective

in a wide range of patients with neuro disorders,

where tDCS treatment has improved quality of life

(QoL) in patients who would otherwise suffer

significantly (Leite et al., 2014). In order to obtain the

desired results with tDCS systems, researchers

typically need to work with patients. This is because

these types of systems are required to precisely target

and focus on specific areas of the brain for stimulation

(Park et al., 2011). In addition, tDCS systems can be

very expensive, limiting their use to specialist units

with facilities where they exist (Zaghi et al., 2009).

Consequently, new approaches to performing tDCS

have been developed for improving patient outreach.

As an example, the methods mentioned by Sourav

(2017) are built on open source framework to provide

the same therapies, ultimately benefiting more

patients. However, such systems are still under

development and susceptible to certain limitations,

such as the accuracy of actual output currents and the

efficacy of the system.

As tDCS treatments require specialised clinician

supervision, treatment monitoring and delivery are

essential features of any novel tDCS system. By

Herring, S., Azhar, M. and Sakel, M.

Tele-tDCS: A Novel Tele-neuromodulation Framework using Internet of Medical Things.

DOI: 10.5220/0010882500003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineer ing Systems and Technologies (BIOSTEC 2022) - Volume 1: BIODEVICES, pages 84-93

ISBN: 978-989-758-552-4; ISSN: 2184-4305

providing remote and cloud-based services for such

treatment, more patients could be reached. This study

will explore how tDCS could be utilised by doctors

remotely using cloud-based applications. A system

like this must take into consideration legal and ethical

implications, including the need for safe testing and

development prior to clinical trials. As a result, these

automated remote solutions have to be secure and

must pose no risk to patient privacy or abuse or

misuse of tDCS treatments. In order to keep costs

down and make the device more affordable for

patients, this study will explore use of off-the-shelf

components in the hardware design.

The remainder of the paper is organised as

follows: Section 2 discusses the optimal conditions

reported in the literature for tDCS treatments, Section

3 discusses the effects of tDCS on patients from

previous studies, Section 4 presents the proposed

framework and use cases for evaluating the system,

Section 5 explains the development of the remote

software interface, Section 6 describes the hardware

components of the system, Section 7 reports the

results of the evaluation of the system, and Section 8

concludes the paper and suggests directions for future

work.

2 TRANSCRANIAL DIRECT

CURRENT STIMULATION

The tDCS is a non-invasive brain stimulation

technique, used to modulate the excitability of the

central nervous system in humans (Woods et al.,

2016). The aim of stimulating the central nervous

system is to change the discharge of neurons in the

brain. The effects of the altered neurons have effects

that may be potentially positive or negative for a

patient. There have been a number of studies

investigating optimum testing parameters for patients

undergoing a tDCS treatment. These parameters

include session durations (minutes), current doses

(mA) and session timelines. The aim is to discover the

optimum conditions to produce the greatest long-term

cognitive plasticity improvement (Fertonani et al.,

2014).

A study conducted by Bikson et al. (2009)

established the safety limits for tDCS treatments and

suggested the average treatment time to be 20

minutes, at a range of 5-30 minutes. The duration of

treatment depends on the neurophysician's

prescription for each session, as confirmed by Thair

et al. (2017). Therefore, any tDCS system being

developed would need to have the capability of

performing optimally throughout the treatment

duration, potentially 30 minutes (Bikson et al., 2009;

Thair et al., 2017). Additionally, studies have been

conducted to determine whether current tDCS doses

are both safe for patients and provide a sufficient level

of stimulation to see positive results. Research from

Parazzini et al. (2014) found that 1 mA had no brain-

stem interference, so is an appropriate dose for

prolonged tDCS treatment up to 30 minutes. An

earlier study by Parazzini et al. (2013) found a dose

below 2 mA did not affect the heart, indicating a safe

current range of 1 to 2 mA. Once again, a doctor

would prescribe a precise amount for the patient

(Parazzini et al., 2014; Parazzini et al., 2013).

Finally, the number of sessions needed to achieve

the optimum neurological and cognitive

improvement is also an integral part of the treatment.

Studies from Castillo-Saavedra et al. (2015) showed

that five sessions per week were the optimal number.

These results were mirrored by Loo et al. (2010) with

treatments lasting between two and eight weeks.

However, no further improvements were observed

after week six. In cases where the number of sessions

were exceeded, there was a risk of minor negative

effects on the patients (Loo et al., 2010). Therefore,

the platform must support a scheduling or control

mechanism to protect the patient in accordance with

the physician's instructions.

In order to prove any tDCS system is successful

in treating a patient, there have been randomised

sham tDCS studies, where the device suggests to the

patient that the system is providing the current to the

patient. However, in reality no current is administered

– this is called a sham or placebo tDCS trial (Palm et

al., 2013; Palm et al., 2012). While such studies

describe methodologies to perform sham tDCS trials,

they don’t discuss a device specific method that

would allow the hardware platform to automate the

process by providing both fake and real treatments to

the patients. Previous studies only suggest a random

cross-over mechanism during the middle of the trial

by swapping patients between sham or real tDCS

treatments (Palm et al., 2012). Therefore, further

investigations are needed to automate the integration

of placebo and real treatments into the hardware

platform.

3 EFFECTS OF tDCS ON

PATIENTS

Several studies have been conducted on the benefits

of tDCS for treating a variety of health conditions,

Tele-tDCS: A Novel Tele-neuromodulation Framework using Internet of Medical Things

from relatively simple cognitive improvements

(cognitive neuroplasticity) to treating depression,

Parkinson's disease, dyslexia, and fibromyalgia with

the goal to improve patient QoL (Fregni et al., 2006;

Boggio et al., 2006). Figure 1 illustrates how tDCS

significantly improved cognitive reaction times of

patients with Parkinson's disease. It shows

improvement in both 1 mA and 2 mA doses. With up

to 15% increase in response time to some patients,

tDCS has the potential to improve QoL for many

patients. While the studies show direct improvements

in patients, they do not examine in detail the exact

varying parameters of treatment, even if this is a

relatively minor variation in current, due to varying

load resistance (through the patient's head), device

output voltage or overall power output. From patient

to patient, the head size and skull thickness will vary,

which means that there is a wide variance in head

resistance, with the average being 7560 +/- 4130

Ohm-cm (Law, 1993). To account for physiological

differences, additional studies need to be undertaken

to explore more precise parameter values, including

the variations in load output during tDCS treatments

for a wide range of patients.

Figure 1: Response time improvements from patient's with

Parkinson's Disease (Boggio et al., 2006).

As discussed previously, research has been

conducted to explore the suggested parameter ranges

for patients and define treatment durations (Bikson et

al., 2009; Bikson et al., 2016). Yet little is known

about the design of tDCS devices. Specifically, the

design decisions that have been made to ensure that

patients receive their treatment safely. This may

involve safety-net systems that work both

autonomously or manual overrides to stop incorrect

treatment current doses or durations. To put such a

system into the hands of a large and possibly

unrestricted group of patients, it must be explored

how the tDCS devices can be controlled with a high

degree of precision.

4 PROPOSED Tele-tDCS

FRAMEWORK

The cloud communication is an important element of

a Tele-tDCS platform to protect both patients and

physicians. Studies have demonstrated the use of

Tele-tDCS devices where patients are treated

remotely after receiving the specialist device, and

doctors work remotely with patients via video

conferencing (Cucca, et al., 2019). Although this

framework provided a mechanism for delivering a

patient's required dose in line with current tDCS

safety regulations and guidelines (Bikson et al., 2009;

Bikson et al., 2016), it did not provide a mechanism

for collecting real-time data about individual

treatment parameters or details regarding patient’s

conditions. Additionally, it does not discuss further

safety mechanisms that allow the device to deliver the

correct dose to the patient or the ability for the doctor

to remotely control it. In this paper, a novel IoMT

device is presented that facilitates bi-directional

communication between a patient's tDCS device held

remotely (such as at their home) and a physician's

software interface.

Figure 2 demonstrates the treatment process in the

Tele-tDCS framework. Each element of the process

is indicated with a change of colour in the figure. The

first phase (blue), is checking the device is online. If

confirmed, the Command Line Interface (CLI) of the

system provides the option to set the tDCS treatment

parameters. Once set, the treatment is considered

authorised and waits for the patient starting the

treatment through a button press on the device. The

confirmation triggers a start signal to be sent from the

tDCS device to the CLI. Once received, the CLI

enters a loop for the duration of the treatment, where

it regularly polls the tDCS device to collect the real-

time treatment values. This looping mechanism also

contains a treatment abort loop, which checks for

connection loss between the CLI and the device, as

well as checking to see whether the physician or

patient has stopped the treatment.

For the prototype system a publicly available

secure platform, Particle Cloud (Particle, 2020), was

used as the data hub for the framework. However,

clinic's private cloud system would be the obvious

choice when the device is manufactured after the

validation stage for trust and security reasons. Particle

have released a white paper that contains a security

checklist for all applications on their network

(Particle, 2020). API requests sent between the device

and the remote software interface utilise a 2048-bit

TLS certificate which uses HTTPS as a required

protocol.

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

Figure 2: Tele-tDCS data flow diagram.

In addition to the secure API calls, the device uses

the OAuth 2.0 Standard for secure device login when

creating tokens for User to Client Communications.

These standards used for the proposed prototype

IoMT Tele-tDCS system ensure that all data on the

device, inbound and outbound are secure and users

are not at risk being compromised by unauthorised

actors during a treatment. In the future, as medical

needs evolve, this framework allows for more

functional safety-oriented Tele-treatments, in

contrast to closed proprietary systems that cannot be

modified (Cucca et al., 2019). In addition to

architectural descriptions for secure operational

features, following use cases were considered to

evaluate system’s behaviour from user’s point of

view.

4.1 Use Case 1: Set Treatment

Parameters

The first use case of the framework is the ability to set

treatment parameters in the tDCS device. There are

three variables of interest: session length, current

status, and placebo status. In this scenario, the doctor

will access the client and define these parameters

within the acceptable safety ranges for tDCS

treatments. In this case, they stand for 1-60 minutes,

1-2 mA, and True/False for duration, current state,

and placebo status, respectively. This range falls

within the standard practice guidelines for tDCS

treatments (Bikson et al., 2009; Bikson et al., 2016;

Thair et al., 2017). Consequently, only appropriate

treatment values will be allowed to be entered via the

physician's interface while parsing the inputs at the

point of data entry.

4.2 Use Case 2: Monitor Treatment

Progress in Real-time

Another use case of the framework is to record sensor

data in real-time during treatments. The objective is

to emulate the ability of a doctor to be part of a

patient's care in real-time. Monitoring can be

performed for all treatment parameters as well as

incoming parameters from sensors using the I2C

Protocol (SparkFun, 2020).

4.3 Use Case 3: Patient Treatment

Safety Mechanisms

Safeguarding patients is one of the most important

aspects of Telehealth devices. Tele-tDCS systems

must ensure patient safety throughout the treatment

period (Riggs et al., 2018). Although there are

systems that provide remote monitoring so that a

physician can monitor the patient before tDCS

delivers the dose, this remote monitoring does not

provide the ability to abort a treatment by physicians

remotely if necessary. Similarly, the patient or

caregiver may need to end the treatment at any time.

The proposed Tele-tDCS device will implement this

through a regular polling mechanism between the

physician's CLI and the user's tDCS device. This is a

passive safety mechanism that can be operated

manually; however, real safety mechanisms should be

a combination of active and passive safety

mechanisms. The proposed tDCS device overcomes

these flaws by providing active safety systems on-

board that enable tracking and monitoring of the

output current and other treatment parameters. A

parameter deviation will immediately stop the dose

Tele-tDCS: A Novel Tele-neuromodulation Framework using Internet of Medical Things

from being delivered to the patient, and an alert will

immediately be sent to the physician’s CLI.

5 REMOTE INTERFACE

Remote CLI was developed to provide a reliable

connection to the Particle Platform using secure API

requests and OAuth tokens. Among the variables

stored on this platform are the treatment parameters

(Current, Session Length, Placebo Status) as well as

the device status and a selection of other relevant

variables. At device start-up configuration, a particle

class specific to each device is called (as shown in

Figure 3), revealing its status. These variables are

made publicly accessible via the Particle Class.

Figure 3: Particle microcontroller setup code.

Figure 4: Tele-tDCS physician's CLI login.

Using the Click Library, the CLI was written in

Python 3.7. The library provides all the necessary

error handling for parameters (Pallets, 2020). In order

to run the CLI, the physician will need to install a

Python 3.7 emulator and Click, with all its

dependencies installed. To use the Tele-tDCS

System, the physician connects to the tDCS

Controller's CLI as shown in Figure 4. This interface

allows the physician to define the tDCS treatment

parameters within the expected safe ranges. The

system also checks input data to ensure the requested

parameters are within an acceptable range and

suggests help texts where incorrect parameters have

been entered (Figure 5).

Figure 5: System checks incorrect inputs.

Figure 6: Help page option for each of the treatment.

In the CLI, parameters will be passed to the

device that are safe for the majority of patients,

although the final parameter values are determined by

the physician. The system also includes a doctor's

name, as well as a password access to the system to

be used for both security and for logging treatment

information. In the event of any ambiguities, or if the

physician is using the utility for the first time, they

may refer to the CLI's inbuilt help page which defines

the system's permitted parameters, as shown in Figure

6, to allow successful setting of the treatment

parameters

6 Tele-tDCS DEVICE

HARDWARE

6.1 Tele-tDCS Device Circuit

In Figure 7, the detailed breadboard schematic of the

proposed Tele-tDCS device circuit is shown. Power

for the current dose is supplied by a Li-ion battery,

which powers both the microcontroller of the Particle

Argon as well as the Adafruit Power Boost 1000B IC.

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

Figure 7: Tele-tDCS graphical breadboard schematic.

It is important to note that the microcontroller's

power supply and the power boost supply are fully

isolated. This ensures that as the load from the

microcontroller changes in usage, there will not be

any potential interference with the patient's DC load.

The current dose the patient requires is of low

amplitude, so an acceptable error range is very small,

with the prototype having a minimum resolution of ±

0.1mA. The microcontroller can control the Power

Boost by pulling the ‘EN’ pin low from the D6 pin;

this can be seen as a yellow line in Figure 7.

Therefore, in placebo scenarios, the power can be

turned off without the patient’s knowledge, removing

potential bias in the trials.

Upon powering up the Power Boost, its 5V output

is directly connected to the current sensor (via the Vin

pin), allowing the microcontroller to monitor the

current output from the Power Boost. Sensor readings

are then polled by the system through the SCL and

SDA pins. These allow sensor data to be shared via

an I

C Protocol connection between the two ICs. Vout

from the current sensor connects to Vin from the

current regulation prototype circuit, which provides a

set current value out to the patient.

In Figure 7, the purple connection indicates the

Vin pin connection. One of the circuit's active safety

mechanisms is implemented through a Non-Latching

Relay, which acts as a safety barrier between the

patient and the device. There are two features in

this,one controlled by the microcontroller and the

other controlled by the non-latching relay. First, if a

deviation occurs from the prescribed treatment

parameters, the microcontroller will immediately

cease administering the treatment. Also, the relay

shares Vcc (power lines) with the microcontroller. As

a result, if the microcontroller loses power, the relay

will automatically open, stopping any dose from

being delivered to the patient.

6.2 Current Regulator Circuit

In comparison to other tDCS systems (Sourav et al.,

2017), the current regulation circuit provides a

variable current that can be digitally controlled in

real-time. By knowing the precise current

measurement, the microcontroller dynamically

adjusts the digital potentiometers wiper values to

provide the required current to the patient. Through a

dynamic adjustment process the current sensor

calculates the required resistance needed from the

Digital Potentiometers (Digi Pots) for the LM344

Current Regulator. The logic to dynamically adjust

the wiper values is shown in Figure 8, where first the

sensor for current is being read and then compared

with the target value. If the actual current is greater

than the target value, then the Digi Pot wiper value is

increased.

Figure 9 shows the Digi Pots with the required

connections for LM344. The default values of the

Digi Pots were 64 and 32, respectively. After

experimentation, these values were found to provide

the required target current of 1mA. Using two Digi

pots in series, resistance values were varied for the

LM344 Circuit Regulator. The values of 64 and 32

Tele-tDCS: A Novel Tele-neuromodulation Framework using Internet of Medical Things

Figure 8: Dynamic adjustment of Wiper values.

Figure 9: Current regulator breadboard schematic.

are set to DS3502_0 and DS3502_1, respectively.

They are set within the 7-bit wiper register, seen in

the datasheet of the Digi Pots (Figure 10). This allows

the device an approximate current output of 1±

0.5mA, calculated through trials with the device.

However, these Digi Pots would also allow for a

higher current should the tDCS treatment require

alternative current values.

Figure 10: DS3502 datasheet (Maxim, 2009).

Once the device is turned on, during

configuration, it is possible to configure it to reach a

more precise current output within 0.1mA by

adjusting the Digi Pot Wiper Values. Changing wiper

values would take less than five seconds,

Alternatively, wiper settings can be adjusted for

specific current values during calibration, such that

during operation the device skips the wiper setting

time to deliver the same current.

6.3 Tele-tDCS Device Interface

Upon turning on the Tele-tDCS device, it will

perform basic configuration and setup, which

includes connecting to the Wi-Fi network, or other

available web platforms in the vicinity. The system

will then enter an "Awaiting Physician

Configuration" mode, shown in Figure 11, when it

awaits for the treatment parameters to be sent from

the physician's CLI (as shown in Figure 12).

Figure 11: Tele-tDCS device interface.

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

Figure 12: CLI setting device's treatment parameters.

Figure 13: CLI prompting the user to confirm.

Figure 14: Device status during treatment.

The RGB Status LED is shown in Figure 11 as

being a blue colour, indicating that the device is

connected to the Particle Cloud. Once the parameters

have been pushed by the physician using CLI control

panel (Figure 12), the patient should have the final

confirmation to start the treatment when they are

ready. Figure 13 demonstrates device interface

prompting the user to

confirm the treatment

parameters and begin the treatment. Once confirmed,

Tele-tDCS Device Interface shows the user the

'TREATMENT IN PROGRESS' in Figure 14.

As the

treatment starts, the real-time monitoring also begins,

and the device will push the sensor readings to the

Particle Cloud API, ready to be received by the CLI.

7 EVALUATION OF THE

SYSTEM

Experiments were conducted to evaluate the system

for all three use cases outlined in the framework,

described in Section 4. A dummy load was used to

test the operation and functionality of the Tele-tDCS

system without any human presence. This was done

to verify the circuit functionality and safety. Upon

receiving ethical approval in the future, human trials

will be carried out.

In the first use case, framework's ability to

manage tDCS treatment parameters was tested. This

test was necessary to ensure usability of the system,

as well as simplicity and clarity of use. Furthermore,

it is important to verify that the communication

between the physician and the device is in real-time,

without any delays that could impair the safe

administration of doses or stop treatments in the event

of problems. Experiment was conducted to test

whether the parameters of a simulated treatment,

which are tDCS session length, current state and

placebo status, could be delivered to the tDCS device

successfully. The tDCS device screen showed

‘Awaiting Physician Configuration”. The three

parameters were then set, and once this was

confirmed the treatment was able to commence, and

the GUI stated, ‘Treatment in Progress’. Once the

current was administered over the prescribed time the

GUI stated, ‘Treatment Stopped’. The GUI then

confirmed ‘Treatment Completed.’ Each of these

stages required confirmation from the Physician’s

CLI. The physician’s interface ensured that only

appropriate treatment ranges were used by parsing the

parameters at the point of data entry.

Figure 15: CLI monitoring Live a simulated treatment.

Figure 15 shows demo of the treatment phase,

which also shows the status when the treatment was

Tele-tDCS: A Novel Tele-neuromodulation Framework using Internet of Medical Things

completed. The interface provided a clear and concise

information to the tDCS device remotely throughout

the experiment’s simulated treatment process.

Additionally, the ability to stop the treatment at any

time by the tDCS device was very simple through a

button push. In real use, some patients may find the

technology stressful, so it has been kept simple to

minimise difficulties and to aid physicians in

reassuring and guiding patients during remote

consultations.

In Use Case 2, the treatment progress was

monitored in real time. The device was tested to

ensure that the tDCS device’s treatment parameters

were continuously monitored, including any

additional parameters that can be sent to the device

from a sensor using the I

C Protocol. The

experimental use of the CLI and device indicated that

a doctor could have the same level of control that can

be achieved during face to face contact. The systems

provided constant feedback from the device regarding

its status both operationally and regarding its

treatment output.

Figure 16: Treatment being halted by the CLI.

Final experiment was conducted to testify the

third use case of the framework to examine the safety

of the device. The remote monitoring in this instance

would allow the physician to monitor the patient in

real time. It was possible to abort a treatment anytime

either by the remote CLI or by using tDCS device’s

own control. Figure 16 demonstrates how inputs

from the CLI (in real, use by a doctor) can halt the

treatment taking control of the device remotely. The

treatment can be also aborted from the device and this

was implemented using a regular polling mechanism

between the physician’s CLI and the tDCS device.

When the treatment was halted, not only the power

boost was turned off, but also the relay was opened,

preventing any residual power within the circuit from

reaching to the simulated electrical load acting as a

patient’s head. In the case of deviations from a set

current value during the treatment, onboard tracking

systems could alert the remote CLI. Overall, this

experiment demonstrated a sound safety mechanism

of the hardware during simulated treatments.

8 CONCLUSIONS

While the device has been shown to function, further

testing and ethical approval must be obtained before

it can be used for human trials. At present, there is a

short time delay for the device to calculate the

resistance values needed for the Digi Pots to provide

the required current output. With enhanced PCB-

based prototypes, future research should aim for

faster current adjustments almost instantly. Current

ranges can be extended outside the 1-2mA range and

preliminary investigations have shown that this is

possible using the prototype model.

The prototype should be further refined in the

future, so that it can be used successfully in clinical

trials and can also provide a more seamless

experience for physicians. An NFC smart card system

should be supported on the device interface so that

physicians can access the device using an institution's

smart card system to configure treatment parameters

for patients. Moreover, the CLI should be extended

to automatically log the treatment data. These logs

can then be used to generate patient reports that can

be uploaded to a healthcare system, such as the

advanced Patient Administration System (SystemC,

2020). In the long run, the CLI should be developed

into a graphical mobile or web-based application,

enabling users to engage in tele-neurorehabilitation in

a more convenient, efficient, and secure manner.

REFERENCES

Bikson, M., Datta, A., Elwassif, M. (2009). Establishing

safety limits for transcranial direct current stimulation.

Clinical Neurophysiology, 120(6), pp. 1033-1034.

Bikson M, Grossman P, Thomas C, Zannou AL, Jiang J,

Adnan T, et al. (2016). Safety of transcranial direct

current stimulation: evidence based update 2016. Brain

Stimulation; 9: 641 – 61. https://doi.org/10.

1016/j.brs.2016.06.004.

Boggio, P. S., et al. (2006). Effects of transcranial direct

current stimulation on working memory in patients with

Parkinson's Disease. Journal of Neurological Sciences,

Volume 249, pp. 31-38.

Bucur M, Papagno C. (2018). A systematic review of

noninvasive brain stimulation for post-stroke

depression. J Affect Disord. 238:69–78.

https://doi.org/10.1016/j.jad.2018.05.026.

Castillo-Saavedra, L., et al. (2015). Clinically Effective

Treatment of Fibromyalgia Pain With High-Definition

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

Transcranial Direct Current Stimulation: Phase II

Open-Label Dose Optimization. The Journal of Pain,

17(1), pp. 14-26.

Cucca, A., Sharma, K., Agarwal, S., Feigin, A. S. (2019).

Tele-monitored tDCS rehabilitation: feasibility,

challenges and future perspectives in Parkinson’s

disease. Journal of NeuroEngineering and

Rehabilitation, 16(20).

Fertonani, A., Brambilla, M., Cotelli, M., Miniussi, C.

(2014). The timing of cognitive plasticity in

physiological aging: a tDCS study of naming. Frontiers

in Aging Neuroscience, Volume 6, p. 131.

Fregni, F., Boggio, P. S., Nitsche, M. A., Rigonatti, S. P.

(2006). Cognitive Effects of Repeated Sessions of

Transcranial Direct Current Stimulation in Patients

with Depression. Depression and Anxiety, Volume 23,

pp. 482-484.

Hall, J. L., McGraw, D. (2014). For Telehealth to Succeed,

Privary and Security Risks Must Be Identified and

Addressed. Health Affairs, 33(2).

Law, S. K. (1993). Thickness and resistivity variations over

the upper surface of the human skull. Brain

Topography, Volume 6, pp. 99-109.

Lefaucheur, JP., et al. (2017). Evidence-based guidelines

on the therapeutic use of transcranial direct current

stimulation (tDCS). Clinical Neurophysiology, 128(1),

pp. 56-92.

Leite, J., Gonçalves, O. F., Carvalho, S. (2014). Facilitative

effects of bi-hemispheric tDCS in cognitive deficits of

Parkinson disease patients. Medical Hypotheses, 82(2),

pp. 138-140.

Loo, C. K., et al. (2010). A double-blind, sham-controlled

trial of transcranial direct current stimulation for

the treatment of depression. Journal of

Neuropsychopharmacology, Volume 13, pp. 61-69.

Maxim (2009). Maxim High-Voltage, NV, I2C POT

(DS3502). [Online] Available at:

https://web.archive.org/web/20190610042255/https://d

atasheets.maximintegrated.com/en/ds/DS3502.pdf

[Accessed 9/12/2021].

Pallets (2020). Click. [Online] Available at:

https://web.archive.org/web/20200519204909/https://c

lick.palletsprojects.com/en/7.x/ [Accessed 9/12/2021].

Palm, U., et al. (2013). Evaluation of Sham Transcranial

Direct Current Stimulation for Randomized, Placebo-

Controlled Clinical Trials. Brain Stimulation, 6(4), pp.

690-695.

Palm, U., Schiller, C., Fintescu, Z., Obermeier, M. (2012).

Transcranial direct current stimulation in treatment

resistant depression: A randomized double-blind,

placebo-controlled study. Brain Stimulation, Volume 5,

pp. 242-251.

Parazzini, M., et al. (2014). Modelling the electric field and

the current density generated by cerebellar transcranial

DC stimulation in humans. Clinical Neurophysiology,

125(3), pp. 577-584.

Parazzini, M. et al.

(2013). Numerical Estimation of the

Current Density in the Heart During Transcranial Direct

Current Stimulation. Brain Stimulation, 6(3), p. 2013.

Park, J.-H.et al. (2011). A Novel Array-Type Transcranial

Direct Current Stimulation (tDCS) System for Accurate

Focusing on Targeted Brain Areas. IEEE Transactions

on Magnetics, 47(5), pp. 882-885.

Particle (2020). Security Checklist for The Internet of

Things. [Online] Available at: https://cdn2.hubspot.net/

hubfs/2833873/White%20Papers/security-3.pdf

[Accessed 9/12/2021].

Riggs, A., Patel, V., Paneri, B., Portenoy, R. K. (2018). At-

Home Transcranial Direct Current Stimulation (tDCS)

With Telehealth Support for Symptom Control in

Chronically-Ill Patients With Multiple Symptoms.

Frontiers in Behavioral Neuroscience, Volume 12, p.

93.

Smith, A. C. et al. (2020). Telehealth for global

emergencies: Implications for coronavirus disease 2019

(COVID-19). Journal of Telemedicine and Telecare,

0(0), pp. 1-5.

Sourav, S., Rahman, A., Mamun, A. A., Alamgir, F. M.

(2017). Standard Transcranial Direct Current

Stimulation (tDCS) Model. Int. Journal of Computer

Networks and Communications Security, 5(12), pp.

264-270.

SparkFun, (2020). I2C. [Online] Available at:

https://web.archive.org/web/20200515151719/https://l

earn.sparkfun.com/tutorials/i2c/all [Accessed

9/12/2021].

SystemC, (2020). PAS. [Online] Available at:

https://web.archive.org/web/20200515205709/https://

www.systemc.com/solutions/epr/pas/ [Accessed

9/12/2021].

Thair, H., Holloway, A. L., Newport, R., Smith, A. D.

(2017). Transcranial Direct Current Stimulation

(tDCS): A Beginner's Guide for Design and

Implementation. Front Neurosci, Volume 11, p. 641.

Woods, A. J. et al. (2016). A technical guide to tDCS, and

related non-invasive brain stimulation tools. Clinical

Neurophysiology, 127(2), pp. 1031-1048.

Zaghi, S., Heine, N., Fregni, F. (2009). Brain stimulation

for the treatment of pain: A review of costs, clinical

effects, and mechanisms of treatment for three different

central neuromodulatory approaches. Journal of Pain

Management, 2(3), pp. 339-352.

Tele-tDCS: A Novel Tele-neuromodulation Framework using Internet of Medical Things