BrainIn: A Data-driven Software System for Neurorehabilitation of

People with Acquired Brain Injuries

Roman Mou

cek

, Luk

s Va

reka

, Petr Br

uha

, Pavel

Snejdar

, Michal Hork

and Ivana Herejkov

New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia,

Univerzitn

ı 8, 306 14 Plze

n, Czech Republic

Keywords:

Acquired Brain Injury, BrainIn, Computer-based Neurorehabilitation, Online Software System.

Abstract:

The current model of neurorehabilitation provides comprehensive care (neurologist, physiotherapist, occu-

pational therapist, psychologist, speech therapist) in the acute phase of acquired brain injuries during hos-

pitalization in some countries. However, follow-up care is insufﬁcient or completely absent, especially for

disadvantaged people. Increasing the availability, effectiveness and efﬁciency of neurorehabilitation care is

beneﬁcial both socially and economically. The BrainIn project aims to improve the quality, effectiveness, and

efﬁciency of neurorehabilitation procedures in both the acute phase and long-term home rehabilitation phase.

Moreover, its goal is to facilitate and accelerate the return of the affected people to their families, social, and

working life. The BrainIn project is freely available as a web application (https://brainin.kiv.zcu.cz/). It is

adapted to the needs of therapists and patients who have experienced acquired brain injuries.

1 INTRODUCTION

This paper deals with the neurorehabilitation of peo-

ple suffering from acquired brain injuries (ABIs) and

opportunities to improve its availability, effectiveness

and efﬁciency. First, it presents an overview of stud-

ies, approaches, questions and results achieved in the

ABI neurorehabilitation and then introduces an online

data-driven software system BrainIn that provides

ABI people, their families and therapists with vari-

ous neurorehabilitation tasks. Moreover, the BrainIn

system enables the personalization of neurorehabilita-

tion tasks, facilitates remote cooperation among ABI

people, their families and therapists and collects be-

havioural data that can help to improve neurorehabil-

itation procedures.

The term acquired brain injury (ABI) refers to any

brain injury that occurs after birth, i.e. is not hered-

itary, congenital, degenerative, or induced by birth

trauma. Medical disciplines use various classiﬁca-

tions for ABIs, including traumatic brain injury (TBI)

and non-traumatic injuries such as infection, disease,

https://orcid.org/0000-0002-4665-8946

https://orcid.org/0000-0002-5998-3676

https://orcid.org/0000-0003-4031-8243

https://orcid.org/0000-0002-2800-3030

stroke, brain tumour, lack of oxygen, a blow to the

head, poisoning, and alcohol/drug abuse. ABI results

in a change to the brain’s neuronal activity, which can

lead to changes in the physical and sensory abilities,

behaviour and personality, metabolic activity, atten-

tion, memory, communication abilities, thinking and

learning or can cause other medical troubles such as

epilepsy.

It is generally difﬁcult to predict the long-term

effects and consequences of ABI. These are differ-

ent for each individual and range from mild to se-

vere. People usually suffer from increased mental

and physical fatigue and decreased general abilities

to plan and solve problems and process information.

These changes require a period of adjustment, both

physically and emotionally, and affect the individu-

als suffering from ABI and their families, friends, and

caregivers.

There is general agreement that the neurorehabil-

itation care of people with ABI is very demanding. It

involves an overall systematic and interdependent co-

operation of experts such as neurologists, physiother-

apists, occupational therapists, psychologists, speech

therapists, and above all, ABI people themselves and

their family members. The big questions (to which

we have only very limited answers) are how to target

and organize neurorehabilitation care to make it effec-

126

Mou

cek, R., Va

reka, L., Br˚uha, P., Šnejdar, P., Horký, M. and Herejková, I.

BrainIn: A Data-driven Software System for Neurorehabilitation of People with Acquired Brain Injuries.

DOI: 10.5220/0011086100003188

In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2022), pages 126-133

ISBN: 978-989-758-566-1; ISSN: 2184-4984

tive and efﬁcient for all involved individuals in their

daily routines, how to provide reasonable (sufﬁcient

and non-annoying) feedback among all involved peo-

ple and implement innovative procedures improving

a long-term therapy.

The paper itself is organized as follows. The

State of the Art section provides a short overview

of available resources: mainly review papers and

computer-based neurorehabilitation papers are pro-

vided. The third section introduces the core concepts

of the BrainIn software system. Then the fourth sec-

tion describes the system implementation. The ﬁfth

section gives an overview of groups of neurorehabili-

tation tasks available in the BrainIn system. The sixth

section provides a study on using the system by a

long-term ABI patient. The last section comes with

concluding remarks.

2 STATE OF THE ART

Although we might assume that the questions of ef-

fectiveness and efﬁciency of the neurorehabilitation

interventions should have already been resolved, this

is not the case. This section provides a state-of-the-art

overview of ABI rehabilitation, focusing mainly on its

effectiveness and efﬁciency. Since hundreds of stud-

ies were carried out and published to aim at different

ABI rehabilitation approaches, usually review papers

with their results and conclusions are presented.

The efﬁcacy of acquired brain injury rehabilita-

tion is investigated in the systematic review given

in (Cullen et al., 2007) to provide guidance for clini-

cal practice. The authors conclude that most interven-

tions have only been supported by limited evidence,

although positive outcomes are documented. These

include, e.g. moderate evidence that inpatient rehabil-

itation results in a successful return to work or that di-

rect patient involvement in neurorehabilitation goals

results in signiﬁcant improvements in reaching and

maintaining those goals. The authors conclude that

there is a need for studies of improved methodologi-

cal quality into ABI rehabilitation.

(Turner-Stokes and Wade, 2015) present a sys-

tematic review of 19 studies involving 3480 peo-

ple of working age. They claim that systematic re-

views demonstrate that multi-disciplinary rehabilita-

tion is effective in the stroke population, in which

older adults predominate. However, the evidence for

the effectiveness of rehabilitation following ABI in

younger adults has not been established. They con-

clude that different interventions and combinations of

interventions are required to meet the needs of pa-

tients with different problems.

Efﬁcacy of neurofeedback interventions for cog-

nitive rehabilitation following brain injury is system-

atically reviewed in (Ali et al., 2020) concluding that

’given the limited quantity and quality of the available

research, there appears to be insufﬁcient evidence to

comment on the efﬁcacy of neurofeedback therapies

within an ABI rehabilitation context’ and providing

recommendations for future research.

A scoping review (Stolwyk et al., 2021) exam-

ines the literature related to economic evaluations of

neuropsychological rehabilitation in individuals with

ABI. There were included studies published between

1995 and 2019 with a study population of adults aged

18 years or more with any ABI aetiology. The au-

thors state (among other conclusions) that most stud-

ies have documented cost savings from providing var-

ious models of multi-disciplinary inpatient or outpa-

tient rehabilitation. On the other hand, these beneﬁts

were estimated without a control group. Only a few

eligible studies included a cost-effectiveness analysis

that yielded mixed evidence for interventions consid-

ered cost-effective for ABI.

A systematic review of the use of computer-

ized cognitive rehabilitation of attention and execu-

tive function in ABI provided in (Bogdanova et al.,

2016) concludes that preliminary evidence suggests

improvements in cognitive functions following com-

puterized rehabilitation for ABI populations. How-

ever, it is also stated that further studies are needed

to address issues such as small sample sizes or inade-

quate control groups.

A systematic review of outcomes of computerized

cognitive training (CCT) in adults with ABI using

the International Classiﬁcation of Functioning, Dis-

ability and Health (ICF) is given in (Sigmundsdottir

et al., 2016). One of the conclusions is that there is

much research examining the efﬁcacy of CCT, but rel-

atively few Level 1 (randomized controlled trials with

a PEDro-P score ≥ 6/10) studies and evidence is lim-

ited mainly to body function outcomes.

Computer-based cognitive interventions in ABI

are systematically reviewed in (Fern

andez L

opez and

Antol

ı, 2020). The authors conclude that such inter-

ventions might be beneﬁcial for ABI people to im-

prove their visual and verbal working memory, al-

though no effect was found in other cognitive do-

mains.

The efﬁcacy of ICT-Based neurocognitive rehabil-

itation programs for ABI was systematically reviewed

in (Geraldo et al., 2018). Most of the thirty-one stud-

ies use a pre-post methodological design, with few

performing assessment moments during intervention

or follow-up. Attention, memory, and executive func-

tions were the variables mainly considered by these

BrainIn: A Data-driven Software System for Neurorehabilitation of People with Acquired Brain Injuries

127

studies to assess neurocognitive rehabilitation pro-

grams efﬁcacy. The studies present a considerable

heterogeneity of the instruments and methods used,

even for the same assessment purpose; a lack of con-

sensus regarding assessment protocol is well visible.

A systematic review in (Resch et al., 2018) exam-

ines studies investigating cognitive rehabilitation in-

terventions for children with ABI, also focusing on

identifying effective (computerized) drill-based exer-

cises. Authors conclude (preliminarily, due to small

sizes and heterogeneity of included studies) that avail-

able evidence suggests that multi-component rehabil-

itation, including drill-based training, is most promis-

ing and can lead to improvements in children’s cogni-

tive and psychosocial functioning ABI.

A clinical review dealing with neurorehabilita-

tion of traumatic brain injuries (TBIs) is presented

in (Oberholzer and M

uri, 2019). The authors point

out speciﬁc characteristics of TBI individuals com-

pared to individuals with ABIs. They address ques-

tions on timing and existing evidence for various re-

habilitation programmes and their impact on the out-

comes in TBI rehabilitation. They also state that

there are currently no international guidelines regard-

ing treatment in the early rehabilitation phase for pa-

tients with severe TBI and that only a few studies

have investigated the effect of integrating rehabilita-

tion into acute TBI care.

A literature review of immersive virtual reality in

TBI rehabilitation is provided in (Aida et al., 2018)

concluding that ”while the current literature gener-

ally offers support for the use of VR in TBI recov-

ery, there is a paucity of strong evidence to support its

widespread use”.

A qualitative study aimed to explore the needs of

individuals with TBIs and their loved ones (Lefebvre

and Levert, 2012) point out that health care profes-

sionals should adopt a personalized approach to re-

spond to needs related to the evolution of informa-

tion, support, and relationships among them, individ-

uals with TBIs and their loved ones.

A systematic review (Coxe et al., 2020) deal-

ing with telebehavioral interventions for family care-

givers of individuals with TBI concludes that care-

givers generally express positive outcomes related to

telebehavioral interventions, but low diversity of sam-

ples prevents generalizing these outcomes.

A critical review of the literature (Fetta et al.,

2017) dealing with the efﬁcacy of computer-based

cognitive rehabilitation interventions on cognitive

performance after mild TBI and ABI concludes that

computer-based interventions seem promising when

improving working memory in individuals with ABI.

However, there is no evidence that currently available

interventions are speciﬁc to mild TBI.

To summarize the state-of-the-art section, we can

argue that many studies describe ABI/TBI rehabilita-

tion’s success. On the other hand, only a small part

of the studies was carried out so that the success of

the rehabilitation, or rather the rehabilitation proce-

dures used, was proven. This can also be stated in the

case of computer-based rehabilitation. All in all, the

evidence that targeted rehabilitation procedures lead

to signiﬁcantly better results than any rehabilitation,

even if based on everyday human activities, is not

convincing.

Finally, we can modestly state that various stim-

uli that motivate ABI people to be active and improve

their abilities and skills have a rehabilitative character.

Then, a computer-based system providing tasks tar-

geted to improve various skills and abilities can help

ABI people, their families, and therapists in the long

term.

3 BrainIn SYSTEM

BrainIn is an online software system (web applica-

tion) for the neurorehabilitation of patients with ABI.

It is designed for patients in institutional and home

care, their families, and therapists and enables the

computerized deﬁnition, execution, and basic evalua-

tion of personalized neurorehabilitation tasks of vary-

ing degrees of difﬁculty for each patient. The ther-

apist deﬁnes the sets of personalized neurorehabilita-

tion tasks, and the patient then performs them with the

help of the therapist, the family, or completely alone.

The main advantage of the BrainIn system is the pos-

sibility of personalization of the tasks by the therapist,

the creation of exercises of different difﬁculty lev-

els, the organization of activities in packages, and the

readiness of the system for partially automated evalu-

ation of the patient’s results and the subsequent use of

machine learning methods to make recommendations

for personalized therapies.

The BrainIn system is based on task templates

with input variables for adjusting each task for an in-

dividual patient. These input variables must be set

before execution. Their setting occurs in the task that

runs the program. These tasks are easily editable, and

it is easy to create a similar version (for example, with

a different number of rounds, other pictures, ques-

tions, etc.). The exercise for each patient typically

forms a package consisting of multiple tasks.

We have introduced the terms template, task, and

package.

• A template is both the program itself and a web

form deﬁning input and output variables. Input

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128

Figure 1: Vocabulary task. The patient is asked to select the caption corresponding to the picture shown with a mouse click.

It is possible to use help (e.g., ’50:50’ to hide two wrong options) by clicking on the question mark button.

variables deﬁne parameters of the task typically

used for modifying the difﬁculty level and person-

alization. For example, the therapist can select fa-

miliar images for each patient (e.g., towns in their

neighbourhood), the number of hints, or highlight

some letters related to the task. The output param-

eters describe the performance and recordings of

each task execution. For example, the success rate

of the answers, the number of hints used, or event

recordings, such as a history of mouse clicks. Ex-

amples of input and output parameters are in Ta-

ble 1. The code is shared for all tasks created by

the same template.

• A task is a web form that sets the input variables

deﬁned in the template, the setting of which is

necessary to run the template program.

• The package is a set of tasks. Our concept is a

web form that creates a list of tasks.

The BrainIn users can be assigned a total of ﬁve roles

that modify their capabilities and behaviour in the sys-

tem:

• Super-Administrator manages the rights for

other roles and provides application environment

settings.

• Administrator takes care of the operation of the

application. He/she creates new tasks, manages

users, assigns patients to therapists, etc.

• Therapist assigns patients and gives them neu-

rorehabilitation tasks.

• Super-Therapist creates custom tasks that other

therapists can use.

• Patient role is assigned to each user after regis-

tration. The patient performs tasks assigned to

him/her and can evaluate them.

4 BrainIn WEB APPLICATION

BrainIn is built on the ASP.NET MVC framework

using the Microsoft SQL Server database. Each

neurorehabilitation task is implemented in the game

framework Unity 3D, providing developers with rapid

exercise development and huge graphic variability.

All exercise templates are based on a minimal tem-

plate with basic functionality. It guarantees the same

user interface (such as the menu, icons, buttons)

through all exercise templates.

There is a landing page for non-registered users

with basic information about the project. It allows

users to try out sample tasks. For registered users,

functionality is different for each role in the system.

There is a dashboard control panel with statistics,

graphs, and shortcut buttons for quick and easy access

to essential features. An example of the dashboard

with results is shown in Figure 2.

The BrainIn system is available at https://brainin.

kiv.zcu.cz/. Users’ recommended requirements are

64bit OS, Mozilla Firefox or Google Chrome web

browsers, and a touch screen for ﬁne motor tasks.

BrainIn: A Data-driven Software System for Neurorehabilitation of People with Acquired Brain Injuries

129

Figure 2: Visualization of results from the Vocabulary task.

4.1 Data Collection and Annotation

The BrainIn system collects anonymized data from

the neurorehabilitation process. The aim of the data

collection is:

• to provide continuous neurorehabilitation results

(i.e., output parameters of tasks) to therapists to

personalize therapy,

• to allow export of multiple datasets in JSON for-

mat for statistical analysis.

All patients sign the informed consent about anony-

mous data collection and further processing using a

web form. We process the following personal data in

the system: name and surname, e-mail address, user-

name, and password.

For statistical processing during system valida-

tion and development, we collect and process addi-

tional data in Google Forms: Patient ID, gender, age,

neurorehabilitation start date, duration of the BrainIn

neurorehabilitation in weeks, assigned tasks, patient

education, dominant hand, initial patient condition,

the patient’s ﬁnal condition, the reason for ending

therapy, and the patient’s behavioural data.

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130

Table 1: Examples of Default input and output parameters from the Vocabulary template.

Input parameters

Parameter Description

Number of rounds Number of repetitive parts of the task

Number of options Number of multiple choice ﬁelds (one correct)

Shufﬂe Whether to show options in random order

Help How many times help can be used during a task

Showing the right solution Whether to display the correct solution

Output parameters

Parameter Description

Total time Total run time of the task in seconds

Round time Run time of each round in task in seconds

Success Percentage success of the task

Help used If the help was used in the round

5 NEUROREHABILITATION

TASKS

5.1 Categorization of Tasks

The BrainIn system contains samples of publicly ac-

cessible neurorehabilitation tasks. Now the system

has six task categories (examples of tasks are given

in parentheses):

• Speech functions (Text Completion 1, Word Se-

ries, Vocabulary, Anagrams, Charade, Characters

search, Interview)

• Memory (Ascending Numbers)

• Logical thinking (Lamps, Pyramid, Orion)

• Concentration (Lettuce Harvest, Test Tubes, Ad-

dition and Subtraction)

• Spatial orientation (Grid)

• Light motor skills (Finish the Half Example,

Painting with Hands, Joining Points, Connecting

Pairs)

5.2 Speech Therapy Use Case

Patients with speech disorders, such as aphasia, need

speech therapy in addition to classical rehabilitation.

This rehabilitation should always be targeted and

should reliably lead to speech adjustment. If the pa-

tient is hospitalized, they receive this care daily in

the hospital. After their release, the patients have to

train at home according to the instructions of a clini-

cal speech therapist and a regular speech therapist.

The BrainIn system offers daily targeted speech

therapy tasks – tailor-made brain training. These are

mainly exercises focused on naming and syntax disor-

ders, disorders of language emotion, memory, atten-

tion, reading, analysis, word synthesis, and exercises

in text and ﬁeld of view.

The tasks Vocabulary and Word series allow the

patients to strengthen lexical semantics, vocabulary,

and equipment of concepts. Skills such as reading,

text orientation, perception of phrases, and under-

standing of meanings are supported by the task Text

completion. The Anagrams and Word Charade tasks

help the patients to analyze and synthesize letters (or

sounds), syllables, and words and thus facilitate writ-

ing. The Ascending Numbers task strengthens visual

memory and trains arranging consecutive elements.

All tasks require attention and concentration; they

can be considered cognitive tasks. The speech tasks

are ranked from the lowest difﬁculty (level 1) to the

average difﬁculty (level 5).

As an example, the task Vocabulary (see Fig. 1)

is provided. The patient is asked to select the caption

corresponding to the picture shown. Similarly to other

tasks, there are yellow control buttons on the right of

the screen. The therapist can give the patient systemic

help in text form or by hiding 50% of the options. The

question mark button is used for this option. The pa-

tient can turn on/off all audio stimuli of the task using

the speaker button. The vocabulary contains over 400

words, which fall into 16 categories (food, fruit, veg-

etables, personal things, colours, profession, musical

instruments, animals, cities, ﬂowers, trees, transport

facilities, buildings, furniture, home appliances, and

home accessory).

6 THERAPEUTIC PRACTICE

AND RESULTS

Since early 2021 the BrainIn system has been used in

hospitals and care homes in the Pilsen region. Four

BrainIn: A Data-driven Software System for Neurorehabilitation of People with Acquired Brain Injuries

131

0th Week 2nd Week 3rd Week 4th Week 5th Week 6th Week 7th Week 8th Week1st Week

Stroke

Start of

rehabilitation

with Brainin

Hospitalization

Practicing of exercises for fine

motor skills and name things

Start of

rehabilitation

Leaving the

hospital

9th Week

Vocabulary 1, 2

Vocabulary 3

Vocabulary 4

Text

completion 1

Vocabulary 5

Text

completion 2

Brainin motoric exercises

Text

completion 3

Vocabulary 5

Anagrams 1

Day 0 - Disabilities

- No ability to name things

- No ability to writing

- No ability to speaking

6 weeks rehabilitation

progress

- Ability to name things

- Ability to Writing

- Ability to Speaking

Figure 3: The timeline of patient progress when performing neurorehabilitation tasks with the BrainIn system.

Table 2: Basic patient data.

Institution Sex Age Education Laterality Total

M F - Primary Secondary Tertiary L R

University Hospital 3 3 37-64 0 4 2 0 6 6

uch Centre 8 2 27-64 4 6 0 6 4 10

Total 11 5 0 4 10 2 6 10 16

patients (aged 37-49) have been actively perform-

ing logopedic tasks, and two patients (aged 49-64)

have been actively performing light motor tasks in

the University Hospital. In the centre of residen-

tial and ﬁeld social services in Zb

uch, ten patients

(aged 27-64) have been using the BrainIn system

for various tasks. While in this care home patients

are generally stable and signiﬁcant improvements are

not expected; in the hospital, the patients’ improve-

ments are monitored using various screening meth-

ods (e.g., MAST, The Mississippi Aphasia Screening

Test (Nakase-Thompson et al., 2002) for logopedic

patients and BI for patients with cognitive problems).

The basic data about the patients is given in Table 2.

A woman, 48 years old, was hospitalized in March

2021 for a cerebral haemorrhage caused by a brain

tumour and underwent urgent surgery. Her initial

MAST score was 64/100. She could not write and

express what she wanted to say and name one-third

of the objects shown. She started to perform neu-

rorehabilitation with the BrainIn system consistently

and regularly about one month later. Simultaneously

but less frequently, she has been given traditional neu-

rorehabilitation tasks on paper. Her condition, includ-

ing speech, has gradually improved. Now (December

2021), she remembered 50/50 objects shown, some-

times with a delay. Moreover, her MAST score im-

proved to 83/100 (May 2021) and 88/100 (December

2021). Her progress when performing neurorehabili-

tation with the BrainIn system is depicted in Figure 3.

7 CONCLUSION

This paper presents a novel online system for person-

alized neurorehabilitation. BrainIn is based on a close

collaboration between therapists and patients and al-

lows a wide range of task parameter modiﬁcations.

These modiﬁcations are individually selected for each

patient and are gradually adjusted during the neurore-

habilitation process. These adjustments mean that it is

difﬁcult to evaluate the beneﬁts of the BrainIn system

statistically. Instead, we presented a speciﬁc case of

the neurorehabilitation process. For future work, once

more anonymized data are collected, it is planned to

apply machine learning methods to help to personal-

ize the course of neurorehabilitation.

ACKNOWLEDGEMENTS

This work was supported by ERDF and the Min-

istry of Regional Development of the Czech Republic

within the project INTERREG V-A 191.

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132

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