Internationalof Computational
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Systems
Journal ofJournal
Robotics,
Networking andArtificial Life
Vol. XX(Z);Month(Year),xx–yy6(4); March(2020),
pp.pp.
270–273
DOI: 10.1080/XXXXXXXXXXXXXX; ISSN
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DOI: https://doi.org/10.2991/jrnal.k.200222.007;
ISSNXXXX–XXXX
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Research Article

Big Data and AI Approach for Body and Brain Test for Seniors
Henrik Hautop Lund*, Yan-Xin Liu, Massimiliano Leggieri
Center for Playware, Technical University of Denmark, Building 326, 2800 Kgs., Lyngby, Denmark

ARTICLE INFOABSTRACT

Article History

We developed a Big Data and AI approach for the screening and early detection of health risks among seniors. The approach isbased on seniors performing playful activities on the Moto Tiles. The activities are organized in a Body & Brain Age Test, whichis composed on four games of 30 s each on the Moto Tiles. A whole population of individuals take the Body & Brain Age Test,and the performance data is collected for each game in the test. The Big Data approach allows the system to identify the nominalscore for each age. The system can automatically generate a personalized training protocol based on the score in the Body & Brain
Age Test. This is done by using the performance score to identify which physical and/or cognitive abilities are in need of training,
and then generate a protocol based on Moto Tiles games, which tend to increase those particular skills as verified in clinicaleffect studies. The suitability of the method was tested in a small effect test with seniors with mild dementia at a care institutionin Denmark. The results show that the seniors with dementia who were screened to be at high risk of falling, within the shortperiod of training with the automatically generated personalized protocol increased their skills to no longer be at risk of falling.

Received 25 November 2019
Accepted 05 December 2019
Keywords
Playware
Moto Tiles
Big DataAI© 2020 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
There is a demographic change ongoing in which most countries
worldwide is getting a population composition with more and moresenior citizens. In Japan in 2016, 27.3% of the population was aged
over 65 years old [1], and in the developed countries, in 2020 mostof the developed countries already have 19–27% of their population
being seniors [2]. The extended lifespan of the population is very
positive for the individual, especially if it results in expansion of thehealthy life years. On the other hand, the societies are met with thechallenge that the population as a whole face many more chronical disease cases, and this may become a major economical challenge in the near future as the demographic change is resulting ineven larger percentage of the population becoming seniors. Someof the most common health issues, which will increase with theincreased number of seniors include falls-related injuries, stroke,dementia, hypertension, coronary heart disease, osteoarthritis, andtype 2 diabetes. If we would be able to help seniors easily identify
health risks and act to minimize these health risks, we may be able
to ensure a longer healthy life period, and at the same time dramatically decrease the society expenses.
A new approach combining several disciplines may help confronting this challenge. These disciplines include playware, Big Data,and AI. Playware is defined as intelligent hardware and softwarethat mediates playful actions among users of all ages [3,4]. Play is afree and voluntary activity that people engage in for no other purpose than play itself and for the enjoyment that it brings. Hence,play is based on intrinsic motivation, and precisely the intrinsic
Corresponding author. Email: hhl@playware.dtu.dk*

motivation may be crucial in the area of health to bring individual
intrinsic motivation to perform actions of benefit for the individual health. As an example of such playware for health, the MotoTiles [5] were developed to allow people to perform playful healthactions. The Moto Tiles are proved to significantly improve the balance of older adults with less training period than ordinary training[6–8]. Also, Liu et al. [9] presented a Moto Tiles training with focus
on combined physical and cognitive gaming, which could enhancecognitive abilities such as the reaction speed of the seniors.
Further, Big Data was developed as an approach to collect massive
data and find patterns in the data. This may allow us to use Big Data
to develop an AI expert system, which can recognize patterns in thedata which we collect. For instance, this could be health patterns of
the population and the individual. Here, we will combine the playware as a health technology with Big Data and AI. This will providea system, which can screen for specific health related challenges
for the individual senior, i.e. which physical and cognitive skills areunderperforming, and at the same time automatically generate apersonalized playful training for the individual to improve on thoseidentified challenges.

2. BIG DATA APPROACH FOR BODY &BRAIN AGE TESTWe developed a Moto Tiles Body & Brain Age Test based on fourMoto Tiles games, each evaluating some body or brain abilities(see Liu et al. [10] for further details). The games are Color Race,Special One, Final Countdown, and Remember (Simon Says in anewer version), and each lasts for 30 s making the test last for justH.H. Lund et al. / Journal of Robotics, Networking and Artificial Life 6(4) 270–273

271

2 min in total. The physical layout of the Moto Tiles is six tiles in ahorseshoe shape as shown in Figure 1.

Table 1 | Spearman’s coefficients between the physical tests and the Moto
Tile games

Expanding on the approach from Liu et al. [10], using a Big Data
approach, we collected data from 379 individuals aged 4–97 andanalyzed the data. As an example, the collected data from the Moto
Tiles Body & Brain Test can be visualized as in Figure 2 for the fourtest games.

Correlation

Color RaceSpecial One

Final Countdown

TUG
CSFSST−0.70611−0.51659−0.80849−0.60695−0.42619−0.70577−0.61745−0.45566−0.65266

We analyse the data to obtain the normative scores of different ages,
e.g. as the average score in a game for each particular age, or bypolynomial fitting using the procedure described in Liu et al. [10].
The line in Figure 2 shows the polynomial fitting for each of thefour games in the Moto Tiles Body & Brain Test.

3. FALLS RISK SCREENING
Previous work (Liu et al. [11]) has shown that there is a strong correlation between Moto Tiles game tests and standard health tests
such as Timed Up & Go (TUG) [12], Chair Stand (CS) [13], andFour Square Step Test (FSST) [14]. The TUG test measures the timethat a subject takes to rise from a chair, walk three meters, turnaround, walk back to the chair, and sit down. Typical cut point ofthe TUG test is 14 s [12]. The CS test measures the time that a subject takes to stand up from a chair and sit down five times. The cutpoint of the CS test for fall risk is 15 s [15]. In the FSST, subjects are
required to step as fast as possible into each square with both feetin a sequence clockwise and counterclockwise. The cut point of theFSST for fall risk is 15 s [14].
Based on the data of the 51 participants (avg. 80.1 years old) who
took both the Moto Tiles physical tests and Body & Brain Age Tests
in Liu et al. [10,11] tests, we analyzed the correlation between theirperformances of the two tests, which is shown in Table 1. It canbe seen that the Color Race game had strong correlations with theTable 2 | The cut points of the Moto Tile games
Cut point

Color RaceSpecial One

Final Countdown

TUG
CSFSST

19 (18.56)
19 (19.26)
19 (18.69)

13 (12.58)
13 (12.92)
13 (12.66)

20 (19.75)
20 (20.32)
20 (19.81)

TUG and FSST (|rs| > 0.7), and the Special One game also had astrong correlation with the FSST. All the rest cases were moderatecorrelation.
The cut points of the TUG, CS, and FSSTs for fall risk assessment
are 14, 15, and 15 s respectively. Linear regression models of thegames and tests were built in order to transfer the standardized cutpoints to the games. Table 2 shows the cut points of the three games
calculated from the three tests. The results calculated from different physical tests are consistent with each other, which indicates ahigh reliability of the games. By taking the average of the results, wecould recommend the following Moto Tiles game cut points: Color
Race = 19; Special One = 13; Final Countdown = 20.
With these correlations verified, the perspective of the Moto Tiles
Body & Brain Age Test is that this test can be used as a screening of the population. In this case, with the correlation with thewell-established cut-points for falls risk, the Moto Tiles Body &Brain Test is a valid detector of fall risks. In this case, the scoringin the Moto Tiles games Color Race < 19 points, or Special One< 13 points, or Final Countdown < 20 points indicates a high riskof multiple falls.

4. PHYSICAL AND COGNITIVE ABILITIESFigure 1 | The Moto Tiles layout for the Body & Brain Age Test.

After being able to perform test and risk analysis with the Moto
Tiles Body & Brain Age Test, it is possible to develop a system,
which automatically recommend and generates personalized training protocols.
Firstly, the age-score model is integrated into the Moto Tiles Body& Brain Age Test app, which allows users to compare their scoreswith the normative score at their age. The comparison result is presented as a percentage difference as follows:
PD =(Score - nv )* 100%nv

Where PD is the percentage difference and nv is the normativevalue.

Figure 2 | The age-score model.

In the Moto Tiles Body & Brain Age Test, four games are used andthe test results are presented as a spider web with seven abilities(physical and cognitive abilities). Based on the relation between

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H.H. Lund et al. / Journal of Robotics, Networking and Artificial Life 6(4) 270–273

the abilities and the games, the ability scores (0–100) are calculated
as follows:
Speed = PDFC + 80
Agility = PDCR + 80
Endurance = 100 - 10 * (FCscore2 - 9 - FCscore23 - 30 )(PDCR + PDSO + PDFC )Balance =3 + 80
Memory = PDRemember + 80
Concentration = PDSO + 80
Orientation = PDCR + PDSO + 80
The algorithm maps the normative values to 80 points. Note thatthe endurance score is not calculated from the age-score model.
Instead it measures if a player performs worse at the end of thegame (23–30 s) than at the beginning (2–9 s) of the game.

5. AUTOMATICALLY GENERATEDPERSONALIZED PROTOCOL
The personalized training protocol is generated based on the spider
web (see Figure 3). Seven games are chosen for the protocol: Color
Race, Special One, Final Countdown, Reach, Hit the Target, Simon
Says, and Concentration. In order to choose appropriate games forthe training protocol, each game is firstly assigned with a weightaccording to the spider web:
wcr = 400 − (speed + agility + balance + orientation)
w fc = 200 − (speed + endurance)
whtt = 200 − (speed + agility )wreach = 200 − (agility + balance)
wso = 300 − (orientation + concentration + balance)
wss = 200 − (memory * 2)wconc = 200 − (memory + concentration)
The weights of most games are associated with the sum of two
abilities or a double of one ability, except for Color Race with fourand Special One with three. This is because Color Race and Special
One are two fundamental games which are relatively easy to beunderstood, and they therefore have more chance to appear in thetraining protocol.
Then the number of games N and total training time T aredefined. In the first trial, N = 2 and T = 7 min. In later weeks

Figure 3 | Screen-shot of the spider web showing the user’s score on theseven physical and cognitive abilities.

of the training, N is raised to 3. The first N games with greatest
weights are selected. The training lengths of the selected games
are calculated as below:
Ti =wi*TN∑ w
j =1

j

6. DISCUSSION
It is interesting that the Moto Tiles game can be a consistent androbust health test for the seniors as is shown with the cut-points
for falls risks. Consistent with standard health tests correlation, it
is observed that scoring Color Race < 19 points, or Special One <13 points, or Final Countdown < 20 points indicates a high risk ofmultiple falls.
This means that just by seniors playing and having fun on the MotoTiles, it is possible to automatically at the same time obtain an indication of their health status and their risk of falls. Further, otherscientific effect studies show how balancing skills are improved
dramatically while playing on the Moto Tiles, so the Moto Tiles
system also presents a tool to lower such risk of falls by playing
certain games on the Moto Tiles.
Additionally, the health tests may show other indications. For
instance, FSST times of 11 s are able to differentiate between persons with chronic stroke and healthy adults older than 50 years, so
this can also be recognized simply by playing on the Moto Tiles.
Also, TUG is a predictor for global mortality (also after presence of
cardiovascular disease was taken into account).
This suggests that different cut-off points in the Moto Tiles games
can be used to show various health related issues (diagnoses) whenpeople are simply playing the games and are having fun.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

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AUTHORS INTRODUCTION
Prof. Henrik Hautop Lund
He is a professor of Center for Playware,Technical University of Denmark. He isWorld Champion in RoboCup Humanoids
Freestyle 2002 and has more than 200 scientific publications and 5000 citations.
He was awarded the Most Outstanding Healthcare Innovatorin the World in 2019. Over the last year, he received international awards in Tokyo, Singapore, and London. He has developed shape-shifting modular robots, presented to the emperorof Japan, and has collaborated closely on robotics and AI withcompanies like LEGO, Kompan, BandaiNamco, Microsoft,Mizuno. He is the inventor of the Moto Tiles (www.moto-tiles.
com), which are used by seniors all around the world.

Mr. Yan-Xin Liu
He is a research assistant at Technical
University of Denmark. He received hisMaster Engineering degree from Technical
University of Denmark in 2018. He is currently pursuing his PhD at Department ofPhysics, Technical University of Denmark.

Mr. Massimiliano Leggieri
He is an Engineering Researcher at Centerfor Playware, Technical University ofDenmark. He is specialized in Software
Engineering and playware. He has developed apps such as the Music Tiles andMoto Tiles app, and has won several
awards with the Moto Tiles development.