Exploring Different User Interfaces for Velocity based Training using

Smart Gym Machines: Pilot Study

unter Alce

1 a

, Jakob H

akansson

2 b

and Andreas Espinoza

2 c

Design Sciences, Lund University, Sweden

Advagym, Sweden

Keywords:

Internet of Things, Training, Health, User Interface.

Abstract:

With the emerging technology called the Internet of Things (IoT), we can now connect computing devices

and sensors to the Internet. The IoT sensors serve to collect data, pushing it and sharing it with a whole

network of connected devices. We decided to explore how to utilize IoT data to increase the user experience

from a commercial gym application called Advagym. Advagym is a commercial solution already available in

the market, which aims to digitize the gym experience, where a retroﬁt solution is used to ﬁt IoT devices on

gym machines to track performance data from users’ workouts. The main goal of this paper is to utilize IoT

data from Advagym’s IoT sensors for velocity-based training (VBT) and conduct a comparative study of three

different user interfaces presenting VBT data to increase the user experience. The main contribution of this

paper is an analysis of user preferences regarding the user interface of VBT feedback during a gym workout.

1 INTRODUCTION

It is becoming more and more common to exercise

regularly. According to a large annual health sur-

vey for the USA (Stobbe, 2018), more people exer-

cise enough each week to meet the USA’s government

recommendations for both muscle strengthening and

aerobic exercise in the USA. This trend is reﬂected

in the amount of new mobile applications which is

being continuously added to AppStore and Google

Play. Several smartphone applications are attempting

to help people training at a gym to log their training

progress. Examples of such applications are “Fitness

Buddy” and “JeFit” which are available both for iOS

and Android. But, most of these applications force the

user to enter the results manually, meaning that after

each performed sequence of repetition (set), the user

has to pick-up the phone and manually write their re-

sult in an application or using pen and paper. This is

a problem since it interrupts the focus of the workout.

On the other hand, if you do not take note right away,

you will most likely forget or write a more positive

number. A more intuitive way would be to log this

data automatically with the help of sensors connected

https://orcid.org/0000-0001-9112-2414

https://orcid.org/0000-0002-8309-561X

https://orcid.org/0000-0001-6189-0121

to the phone and the workout machines. This could

be achieved with the emerging technology called the

Internet of Things (IoT).

With IoT, we can connect computing devices and

sensors to the Internet. IoT does not have a real aca-

demic deﬁnition, but Preece et al. (2011) deﬁne IoT

as “a system of connected computing devices, me-

chanical and digital machines, objects, animals, or

people that are provided with a unique identiﬁer and

the ability to transfer data over a network.” The IoT

sensors serve to collect data, push the data, and share

the data with a whole network of connected devices.

IoT sensors could be used to track the user’s perfor-

mance and movements (Gubbi et al., 2013), connect-

ing, and gathering all the data from and about the user.

Advagym (2015) is a commercial solution already

available in the market, which aims to use IoT de-

vices mounted on gym machines to track performance

data from user’s workouts and digitizes the gym ex-

perience. The interaction between the IoT units and

the users is mainly through its smartphone applica-

tion which runs on both iOS and Android platforms.

Currently, the IoT devices attached to the machines

are gathering many types of data, of which some, are

never used or shown for the user. For example, an

accelerometer is used to detect movement and a time-

of-ﬂight sensor is used to measure distance, the com-

bination is used to count the number of repetitions.

Alce, G., Håkansson, J. and Espinoza, A.

Exploring Different User Interfaces for Velocity based Training using Smart Gym Machines: Pilot Study.

DOI: 10.5220/0010271301130120

In Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021), pages 113-120

ISBN: 978-989-758-506-7

113

However, the actual data is not shown and could be

combined for other measurements, for example, Ve-

locity Based Training (VBT). VBT focus on the ve-

locity of the performed exercise movement rather than

the weight.

The main goal of this paper is to utilize IoT data

from Advagym’s IoT sensors for VBT and conduct

a comparative study of three different user interfaces

presenting VBT data to increase the user experience.

The main contribution of this paper is to elucidate

knowledge about which UI feedback of VBT given

for the “gym-goer” was preferred.

The next section presents relevant related work.

Then the Advagym is described followed by the eval-

uation, results, discussion, and conclusions.

2 RELATED WORK

Over recent years, there is a growing awareness of the

potential power and relevance that interactive media

applications can have in inﬂuencing people’s motiva-

tion and subsequent behavior. Recent work in the area

of persuasive technology, that is, technology inten-

tionally designed to change a person’s attitude or be-

havior, testiﬁes to this effect (IJsselsteijn et al., 2006).

One can for instance use wearables and applications

such as RunKeeper, Fitbit, and Withings which helps

you to keep track of how much you exercise. How-

ever, more than half of the individuals who purchased

a wearable device stop using it and, one-third did so

before six months (Patel et al., 2015). One reason is

that current applications often hold a simple focus on

recording data and displaying it in statistical form to

the user for post-activity viewing. Moreover, they are

relatively well developed for running, walking, and

bicycling but they are not so useful for gym workout

nor for getting feedback during a workout.

Sankaran et al. (2016), evaluated HeartHab,

which is an application developed for Coronary

Artery Disease patients. The authors managed to in-

corporate behavior change techniques and design the-

ories into their mobile application. The application

focuses on presenting patient data of medication, pre-

scription, and exercise targets for walking or running,

no gym exercises.

Another example of an application tracking walk-

ing but tailored for a certain group of people is the

“WalkWithMe” application by Geurts et al. (2019).

WalkWithMe is a mobile application that supports

people with Multiple Sclerosis (pwMS) in walking.

The application can coach pwMS in achieving a per-

sonal goal over a period of 10 weeks. Geurts et

al. (2019) evaluated the application in a 10-week

ﬁeld study with 13 pwMS subjects. They found that

WalkWithMe supports pwMS in achieving their goal.

Moreover, it has been proven that physical activity has

a positive effect on most of these symptoms. How-

ever, we believe that this kind of application would

beneﬁt from utilizing gym exercises as well.

Yang (2015) investigated the use of real-time soni-

ﬁcation as a way to improve the quality and motiva-

tion of strength exercises. In the study’s case, a biceps

curl routine was investigated with a soniﬁcation sys-

tem and with the help of electromyography (EMG)

sensors and a Microsoft Kinect camera. When exer-

cising with the system, muscular and kinematic data

were collected and used to a custom-designed soni-

ﬁcation software which then generated real-time au-

ditory feedback. An initial pilot study showed that

providing real-time sonic feedback on a biceps curl

exercise can produce useful cues to a user and inﬂu-

ence the quality of the exercise (Yang, 2015). A lat-

itudinal experiment was later on conducted to com-

pare exercising quality between a soniﬁcation group

and a control group that does not get any feedback.

The study showed that users with soniﬁcation real-

time feedback performed consistently better in terms

of movement velocity and effort.

There are multiple beneﬁts to being ﬁt. Some

studies suggest physical exercise and ﬁtness are ben-

eﬁcial for both younger and older people (Malina,

2010). One of the ideas of the new user interface is to

add a new way of achieving ﬁtness through exercis-

ing. Consider exercise prescriptions for older adults.

The main objective of resistance training programs

for the majority of this population is to increase func-

tional performance (Vince, 2017). Muscle power is

also a superior determinant of physical function com-

pared to muscle strength. Therefore, utilizing VBT

as a strategy to improve functional abilities in older

adults appears logical. Performing resistance train-

ing with maximal concentric velocity has been shown

to be more effective at improving functional perfor-

mance in older adults (Vince, 2017).

For the ordinary gym-goer, the main focus of

their training progress usually has its focus on which

weight and amount of repetitions are being per-

formed. This way of training is well known and sim-

ple to understand as a beginner. But there are other

innovative ways to train and instead e.g. focus on the

velocity of the lift. As already mentioned VBT fo-

cuses on the velocity rather than the weight, which

can be useful to develop athletes that need explosive

movements (Mann, 2016).

This paper focuses on utilizing unused IoT data

for VBT and present it for the “gym-goer”, during a

workout by giving feedback on how well the exercise

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

114

Figure 1: Illustrates the Advagym’s IoT data ﬂow to differ-

ent users.

is performed, regarding the velocity.

3 Advagym

Advagym (2015) is a relatively new IoT solution to

digitize the gym experience, where a retroﬁt solution

is used to ﬁt IoT devices on gym machines to track

performance data from users’ workouts. The system

offers different services, and there are different kinds

of users for each service. Examples of users of Ad-

vagym are The application user; The personal trainer

(PT); and The gym responsible (Figure 1). However,

this paper will focus on the application user, who with

the help of an iOS or Android application can train

and log their training automatically with exercise ma-

chines.

3.1 Advagym Hardware

In general IoT solutions often consist of three basic

components device, gateway, and cloud. The Ad-

vagym solution has a similar architectural structure.

There are three components called: main unit and

puck, observer, and Advagym server. The main unit

and puck, and the observer are connected to the Ad-

vagym server which is equivalent to the cloud. While

the observer corresponds to the gateway component,

and the main unit and puck are the IoT devices.

The main unit is the IoT device which does the

actual tracking of performed repetitions on an exer-

cise machine. Different sensors are used in the main

unit. An example of an included sensor is the ac-

celerometer, which is used to awaken the main unit

from its “sleep mode” (sleep mode is a battery con-

servative mode). Once the main unit is awake, the

ﬁrmware is booted up and starts tracking the vertical

movement with the help of the time-of-ﬂight sensor.

By using a combination of these sensors and smart al-

gorithms, data packages for each repetition and their

movement will be broadcasted. The data package is

broadcasted multiple times through Bluetooth-Low-

Energy (BLE). The data packages sent are used both

for the user’s application to process, as well as for

the observer (Figure 1), to log. Each broadcasted

data package is dependent on two events of repeti-

tion, these events help to deﬁne the performance of a

repetition. Event 1: Occurs once the weight stack has

reached its max peak value of repetition. This means

that the max value of distance measured has been re-

ceived. Event 2: Occurs once the weight stack has

reached a lower value than the one measured in event

1 and is on its way up once again. The actual broad-

cast is also performed, at this event.

These events occur for each repetition of a set, ex-

cept for the very ﬁrst repetition, where not all param-

eters of the package are set. The reason why not all

parameters of the data package are set of the ﬁrst rep-

etition is due to the boot-up time of the main unit. It is

triggered by the accelerometer and it is done rapidly

but still, it will give some uncertainty on how far the

weight stack has traveled from its original position.

The movement of the main unit will only be on the

vertical axis since it is placed on the weight stack of

an exercise machine.

The puck has its main purpose to work as a con-

necting link between the main unit on the installed

machine to the user’s smartphone application. Mean-

ing that with the help of the puck, users can con-

nect their phone with the Advagym application, to the

chosen exercise machine which is connected to the

Advagym system. Depending on which smartphone

is used two different technologies are supported, For

iOS users, a BLE package is received from the puck

once the user “taps the puck”. For Android users

with near ﬁeld communication (NFC) capabilities on

their smartphone, the same interaction is done, but the

package is received through NFC instead of a BLE

package. For Android users without NFC capabili-

ties, BLE is used instead. Regardless of which tech-

nology is used to receive the data packages from the

puck, the same information is provided and processed

in the smartphone application.

The observer has its main purpose of monitoring

the IoT units and acting as a gateway i.e. connecting

the gym to the Internet. The observer is placed in the

same area as the exercise machines to “listen” to all

the broadcasts which are done by the main units and

keep track of all the connected gym machines.

Exploring Different User Interfaces for Velocity based Training using Smart Gym Machines: Pilot Study

115

3.2 The UI for Velocity based Training

One of the main goals was to design and evaluate a set

of user interfaces (UIs) that utilize IoT data to give

feedback of the velocity to the users during a work-

out. First, a brainstorming session was conducted to

ﬁnd out which IoT data was available and not used

akansson, 2019). During the brainstorming ses-

sion, it was noticed that the acceleration data from the

main unit was not used. This data could be used to

give feedback to the user during an exercise regarding

how fast or slow the user is performing the exercise.

The process of designing the feedback or the UIs was

performed in an iterative approach, starting with spe-

ciﬁc brainstorming sessions for the feedback and low-

ﬁdelity prototypes, which were tested and reﬁned. Fi-

nally, the UIs were developed and evaluated. The vi-

sual update is in the form of adding design patterns

with colors and forms which the Advagym applica-

tion uses. The reason for using resembling design

is to make the prototypes as realistic to the current

system as possible. Three different UIs were devel-

oped, referred to as “Text”, “Text and Guide Pendu-

lum” (TGP), and “Text and Guide Circle” (TGC), see

Figure 2.

3.2.1 Text

The ﬁrst UI is only text feedback and is presented dur-

ing the movement of the performed repetition (Figure

2a).

3.2.2 Text and Guide Pendulum (TGP)

For the pendulum prototype, a green dot was used as

a pendulum movement. In addition, discrete dots in

the backgrounds worked as the ﬁeld and outline of

the area within which the dot moved. The animation

of the movement also triggered the dots, creating a

more dynamic animation, making the pendulum feel

more like something moving with force in a direction

(Figure 2b). The repetition counter was modiﬁed to

match the design of the Advagym application. The

text feedback was positioned better in relation to the

surrounding elements as well as given an additional

animation for appearing and disappearing, where it

increases in scale and fades in, and after a predeﬁned

time to match the repetition time, fades out (Figure

2b).

3.2.3 Text and Guide Circle (TGC)

The circle prototypes went through several iterations

until current testable versions were completed. The

outer indicator circle now serves as the repetition

Figure 2: The three Hi-Fi UIs: a) Text, b) Text and Guide

Pendulum (TGP), c) Text and Guide Circle (TGC). The

number in the center shows the repetition.

counter as well, where a percentage of the circle

stroke is ﬁlled with green color for how many repe-

titions have been performed compared to how many

are aimed to be performed. It has the same structure

as the repetition counter for the pendulum. An extra

visual was added to the circle tempo indicator, which

is an outer stroke, that gives the user a sense of which

direction the circle is going. If the circle is expand-

ing or shrinking, this would represent a concentric

movement or an eccentric movement. An extra ani-

mation was added to the outer repetition circle which

“pops” once the indicator circle meets the outer circle.

Early user studies indicated that this effect improves

the user experience and the users felt as if the motion

was more natural (Figure 2c).

4 EVALUATION

A user study was conducted to evaluate the different

prototypes, by letting all participants perform the ca-

ble row exercise in the ofﬁce gym.

4.1 Setup

Both quantitative and qualitative data were col-

lected. Documentation of the active sessions was

done through video recordings from a Sony A6300

as an overview camera. Figure 3 shows an overview

of the setup.

4.2 Participants

Advagym is an application with a very broad user

group where young to old users are included. It can be

beginners as well as elite trainers. The one thing they

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

116

Figure 3: The setup.

have in common is that they are training at a gym.

However, we had some restrictions such as being able

to physically perform the test, i.e. the participant

should be able to perform the “cable row” exercise

on a low/moderate weight, without any pain. More-

over, having good enough sight with/without aids, to

see a 5.8” mobile screen, one meter away from the

participant.

To more easily recruit participants, an online ques-

tionnaire using Google Forms was used. The online

questionnaire served two purposes; to gather relevant

demographic data about the participant and book an

available time-slot. The sign-up questionnaire was

spread through different channels, both in digital and

physical form. The physical form were posters in-

cluding QR-code links that were placed in several

crowded areas within the campus of Lund Univer-

sity. The digital form was a link that was distributed

through Sony’s social media groups.

In total, 48 participants (20 female, 28 male) were

recruited. The age of the participants ranged from 18

to 55 years (M = 29.2, SD = 10.14). To estimate and

grade the training skill of the participants a sequence

of calculations were made, based on their sign-up

questionnaire answers. The following parameters:

weekly training frequency and time kept with current

training frequency. This was graded into a scale of

1 to 5 where the interval of 1 to 3 was graded as be-

ginner/novice training skill and 4 to 5 were graded as

advanced training skill.

4.3 Procedure

The test session was divided into three parts: Prepara-

tion stage, Test session, and Post-test (Figure 4). The

Preparation stage includes the stage of the recruitment

and initial meeting with the participants. As already

mentioned, Participants signed up for the test with the

help of an online questionnaire. The online question-

naire allowed us to have demographic data ready be-

fore the actual test. When the participant arrived at

the test location, they were welcomed and escorted

to a User Experience (UX) lab, where they signed a

Non-Disclosure-Agreement (NDA) and an informed

consent form.

Once the introduction was done, the participant

was taken to the test area which was the ofﬁce gym.

The reason for using an actual gym was to get the

most realistic test possible, as well as the fact that the

ofﬁce gym was already equipped with the Advagym

system. Once the participant was ready to start, the

test session continued in a certain order. There were

ﬁve different test cases for the test with the following

alphabetical labels:

- A. No System

- B. Personal Trainer (PT)

- C. System with Text feedback

- D. System with TGP feedback

- E. System with TGC feedback

Each test session started to test A i.e. no sys-

tem/no feedback in order to know how the partici-

pants’ would perform normally the exercise, followed

by B & C, but counterbalanced.

With 48 participants, each order will have 24

participants. The reason for this was to ﬁnd if

there are any relations between which order of

cases/prototypes is tested. Followed by the initial se-

quences of test cases, either D or E will be tested,

meaning only four test cases per test session. More-

over, cases D and E will be tested by 24 participants

each. We have a mixed design with two within-group

measurements (ABC and ACB) and between-group

measurements (DE and ED). The independent vari-

ables are the three user interfaces and the PT. The de-

pendent variable is the performance score. To avoid

sequence effects, the order in which the test sequences

were presented was fully counterbalanced, i.e. each of

the four possible orders was shown to equally many

participants.

For each case, the participant was asked to per-

form twelve repetitions on three different sets with a

low/moderate weight and a rest time of their choice in

between. The reason for this was to get sufﬁcient data

points to see any signiﬁcant patterns. When one test

case was complete, the next test case followed with

the same test structure, continuing throughout the test.

Example: A ⇒ B ⇒ C ⇒ E ⇒ Done!

After the test, the participant was taken back to the

UX lab and was asked to ﬁll out the System Usabil-

ity Scale (SUS) questionnaire (Brooke, 2014). In an

attempt to do a usability assessment of the user inter-

face, SUS was used. It attempts to measure cognitive

Exploring Different User Interfaces for Velocity based Training using Smart Gym Machines: Pilot Study

117

Figure 4: Test session procedure.

attributes such as learnability and perceived ease of

use (Brooke, 2014). The questionnaire was followed

by a short structured interview to see if the partici-

pants understood the user interface, and to see which

one they preferred.

Each session lasted about 30 min, and as a reward,

the participant was given a movie ticket. The whole

procedure of the test session is visualized in a block

diagram (Figure 4).

4.4 Results

In the following section, the results from the objective

performance score, SUS scale, and the structured in-

terview are presented. All of the 48 participants man-

aged to accomplish the exercises.

We used an alpha level of .05 for all statistical

tests.

Performance Score. The performance for all 48

participant test cases was logged and summarized.

The performance data is based on the velocity of the

concentric (v

) and eccentric (v

) lift for a repetition,

which was summarized and made into an average (v

)

velocity of the lift.

+ v

= v

(1)

The velocity v

was then graded as either slow,

good or fast, based on the targeted velocity v

tar

0.335 m/s with a tolerance of v

tol

= 0.05 m/s, which

is a sensitivity of 15%, i.e. v

could be in the inter-

val of (0.330 m/s ≤ v

≤ 0.340 m/s) to be graded

as “good.” As mentioned, for each test case twelve

repetitions on three sets were performed. Because of

hardware constraints, the very ﬁrst repetition was ig-

nored since no data was given for that repetition by

the system. Meaning that for three sets eleven repeti-

tions give a total of 33 data points for every test case

on every participant. The performance is represented

in percentage of each test case, i.e. how many repe-

titions per data points were graded “good” out of the

performed repetition on this test case. The number

of performed repetitions should be 33, but in some

cases, the participants missed a repetition. This was

taken into account for the calculation of the percent-

age score.

A one-way ANOVA for dependent measures be-

tween personal trainer (PT), Text and TGP showed a

Figure 5: Performance score illustrated in a boxplot, rela-

tions between TGP, PT and Text. N = 24.

Figure 6: Performance score illustrated in a boxplot, rela-

tions between TGC, PT and Text. N = 24.

signiﬁcant relation: F(2,69) = 5.34, p = .0070. Multi-

ple pairwise-comparison showed a signiﬁcant differ-

ence between the PT and TGP with an adjusted p-

value of p = 0.0057 (Figure 5). Moreover, it was close

to the margin of statistical signiﬁcance between PT

and Text with an adjusted p-value of p = .089.

When it comes to the dependent measures be-

tween PT, Text and TGC, again one-way ANOVA

showed a signiﬁcant relation: F(2,69) = 3.50, p =

.036. Multiple pairwise-comparison showed close to

the margin of signiﬁcant difference between PT and

Text with an adjusted p-value of p = .052 (Figure 6).

Moreover, it was close to the margin of statistical sig-

niﬁcance between PT and TGC UI with an adjusted

p-value of p = .080.

Since the test sessions were divided into four parts

which followed in a sequence of sessions, it could be

seen as a learning curve of how to ﬁnd the targeted

velocity. This is presented in Figure 7, where every

test case is displayed from the ﬁrst test case to the

fourth and last test case.

Based on the demographic data, we analyzed the

participants split into two groups to see if we could

see any statistical differences based on their training

skill: beginner/novice and advanced. The difference

in the learning curve of these groups can be seen in

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

118

Figure 7: Learning score curve of the four different test

sequences, from 1

to 4

(last). N

ABCD

= 12, N

ACBD

12,N

ABCE

= 12,N

ACBE

= 12.

Figure 8: Learning score curve difference between partic-

ipants graded as beginners/novice and advanced in their

training skill. N

advanced

= 19,N

beginners/novice

= 29.

Figure 8.

SUS Score. The results obtained from the SUS

questionnaire for the TGP present a mean score of

M = 82.2, SD = 12.07 with a minimum score of 55

and a maximum score of 97.5. For the TGC, a mean

score of M = 80.7, SD = 8.42 with a minimum score

of 62.5 and a maximum score of 95. A paired t-test

was used to explore the difference between the TGP

versus TGC but no statistically signiﬁcant difference

was found t(41.1) = -.41, p = .68. There were no sta-

tistically signiﬁcant differences between beginner and

advanced participants either t(33.9) = -.43, p = .67.

Structured Interview. During the interview of

the participants, questions regarding each tested pro-

totype were asked.

Each participant was also asked which test case

they preferred in the sense of which test case they

would prefer to use in their daily training, when train-

ing with an exercise machine, regardless of external

inﬂuences, e.g. money for a PT. The answers for the

test cases with the TGP are summarized in Table 1

and for the test cases with the TGC in Table 2.

Table 1: Preferred UI, TGP, Text, PT and No System

(N=24).

UI Nbr of participants

TGP 8

Text 5

PT 7

No system 4

Table 2: Preferred UI, TGC, Text, PT and No System

(N=24).

UI Nbr of participants

TGC 5

Text 5

PT 10

No system 4

5 DISCUSSION

In this section, we will discuss the “take-aways” of

the user study and the limitations of the prototypes.

Performance Score. Compared with a personal

trainer, trying to give feedback on the speed of a user’s

lift, all the prototypes performed signiﬁcantly better

in the sense of being close to the targeted velocity of

the lift. It also seems as if the participants who tested

the Text UI ﬁrst, rather than receiving feedback from a

PT ﬁrst, found it easier to adjust to target velocity. It is

also shown that participants using any prototype with

a beginner level of training skill can perform as well

as participants with advanced training skills, where

the skill level is an indication of physical body con-

trol for the participant. However, the advanced par-

ticipants had better performance than the novice par-

ticipants during the ﬁrst test which did not have any

feedback at all. So the advanced participants started

with better performance but with the help of different

prototypes, the performance score was leveled up.

SUS Score. There was no signiﬁcant difference

regarding the SUS score. Both UIs had a SUS score

larger than 68, which is considered to be above av-

erage (Brooke, 2014). The TGP UI had a slightly

higher mean SUS score value M = 82.2 than the TGC

UI which had a mean SUS score value M = 80.7.

The SUS score measures cognitive attributes such as

learnability and perceived ease of use, the result indi-

cates that both UIs are considered to be easy to use,

and easy to learn.

The Structured Interview. One of the main rea-

sons why the participants preferred to train with a per-

sonal trainer rather than with an application, might

be because of the human connection. Another reason

can also be that the participant wants more feedback

Exploring Different User Interfaces for Velocity based Training using Smart Gym Machines: Pilot Study

119

on their performance than just the velocity of the lift.

Feedback such things as the user’s positioning in the

exercise, range of motion, movement, and other rel-

evant information that the prototypes could not cur-

rently provide.

Limitations. An example of a hardware limita-

tion or constraint is the fact that it only broadcasts two

events per repetition, having a continuous real-time

data stream would enable more alternatives of UI ele-

ments. Another limitation of this study is that we only

focused on the visual modality. However, there are

plans to continue the research and add other elements

such as audio feedback and gamiﬁcation. For exam-

ple, having the indicators being moved with matching

audio or even just audio feedback. Regarding gami-

ﬁcation, which according to Deterding et al. (2014),

is deﬁned as the use of game design elements in non-

game contexts. An example of gamiﬁcation can be

the use of points, badges, levels, and leaderboards.

In this case, every time the velocity of the exercise

was performed correctly could lead to some achieve-

ment. For example, the gym could offer something

from their shop.

6 CONCLUSION

The ﬁndings presented in this paper expand the ex-

isting knowledge-base of HCI research in the sphere

of using a mobile application to support VBT. The

result of the prototypes has been overall very impres-

sive. Especially in the objective sense that an applica-

tion can help a user perform a physical movement at a

particular speed. All of the prototypes have also per-

formed very well regarding to the usability scores. All

of the prototypes were above the average score of 68

for the SUS-based questionnaire, which indicates that

the proposed user interfaces are easy to understand

and use. The majority of participants would also pre-

fer to use one of the prototypes in their daily training

with exercise machines. This is a good indication that

the feature itself is interesting for users.

ACKNOWLEDGEMENTS

Advagym team for supporting this research.

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