IEEE Systems, Man and Cybernetics Magazine - January 2021 - 8

Motivational Approaches
There are motivational applications intended to increase
user participation in physical activities, reduce their sedentary behavior, or improve the quality of their physical
activities. Based on our analysis of the literature, we categorize motivational approaches into three groups:
coaching, socialization, and gamification/exergaming. To
our knowledge, there is little research done on motivational approaches. This limitation does not allow us to
generalize our findings and make a conclusive remark.
We believe that in the near future, more motivational
applications will enter the market because of artificial
intelligence (AI) advances.

Table 1. Some prominent examples of the
proximity and wearable sensors used for
indoor activity tracking.
Sensor
Type

Location

Sensor

Reference

Proximity

Gym
-equipment

RFID

[10]

-

Environment

Depth camera

[30]

-

Environment

Depth camera

[14]

-

Environment

Depth camera

[15]

-

Environment

Depth camera

[16]

-

Environment

Depth camera

[17]

-

Environment

Camera

[13]

-

Gym mat

Pressure sensor

[12]

Wearable

Gloves

Pressure sensor

[5]

-

Waist

Accelerometer,
-gyroscope sensor

[31]

-

Waist

Accelerometer,
- yroscope sensor,
g
Barometric pressure
sensor

[25]

-

Ankle, waist

Accelerometer,
-gyroscope sensor

[24]

-

Smart textile

Pressure sensor, EMG

[28]

-

Bicep

EMG

[29]

-

Necklace

Heart rate sensor

[32]

-

Ear

Heart rate sensor

[26]

-

Ear

Heart rate sensor

[33]

-

Leg, chest,
and hand

Accelerometer,
-gyroscope sensor

[34]

-

Leg, hand,
and arm

Accelerometer,
-gyroscope sensor

[35]

8	

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 2021

Coaching
In addition to activity data collection in fitness tracking
applications, users can interact with applications for analysis of the collected data. Early work in this area by Hiilloskorpi et al. [36] introduced a coaching approach widely
adopted by aerobic exercise devices: calculation of the
user's maximum heart rate. The field of coaching applications is slowly advancing. Rawassizadeh et al. [37] suggested a coaching approach in which textual recommendations
are provided once a day because the small screen of most
wearable devices cannot accommodate graphs and numbers. Their approach compares users' daily activities and
heart rate to their personal averages using a machine
learning algorithm and notifies them if their activity level
has decreased.
Another coaching approach proposed by Pessemier and
Martens [38] compares the data collected from heart rate
sensors mounted on exercise equipment or within wearable devices. Their use guides users in adjusting pace during an aerobic exercise to avoid overexercise injuries or
more effectively reach their fitness goals.
Adaptation AI could improve fitness coaching applications considerably. Li et al. [39] proposed an AI fitness system that can recognize fitness actions by applying
evaluation methods on image sequences. Furthermore, the
AI of Things (AIoT), which integrates AI, deep learning,
and the Internet of Things (IoT), is also advancing rapidly.
For example, Chu et al. [40] proposed an AIoT system that
consists of both biological IoT sensors on the body and
motion sensors on weight training equipment. Their system can detect incorrect movements and provide coaching
advice to the trainee. Coaching approaches are still in
their infancy, and there is plenty of room for improvement,
as we explain later in detail.
Socialization
Some other approaches focus on motivating users by sharing their activity data through social network applications,
that is, through socialization. Studies have pointed out that
sharing physical activities can increase user motivation
[41], [42] by enabling users to compare their performance
with each other.
In this context, Häkkilä et al. [43] introduced a virtual
city jogging concept that simulates the subject's movement on a treadmill in a 3D virtual city. This application
combines a map-based presentation of the user's jogging
distance and a 3D representation of the city. Users can
see previous joggers' results and compete with them.
Cassola et al. [44] built an online gym system that satisfies users' socialization and fitness needs from their
homes. The practice of employing socialization for motivation is mature enough that it has penetrated the
mobile health market [e.g., Runkeeper (https://play
.google.com/store/apps/details?id=com.fitnesskeeper
.-r unkeeper.pro), Nike Run Club (https://play.google
.com /store/apps/details?id=com.nike.plusgps), and


https://play.google.com/store/apps/details?id=com.fitnesskeeper.runkeeper.pro https://play.google.com/store/apps/details?id=com.fitnesskeeper.runkeeper.pro https://play.google.com/store/apps/details?id=com.fitnesskeeper.runkeeper.pro https://play.google.com/store/apps/details?id=com.nike.plusgps https://play.google.com/store/apps/details?id=com.nike.plusgps

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