IEEE Systems, Man and Cybernetics Magazine - January 2021 - 7

been widely adapted in existing aerobic exercise
machines. The use of radio-frequency identification
(RFID) tags, pressure sensors, or accelerometers on
dumbbells has been commercialized as well (https://
www.bowflex.com/selecttech/1090/710000.html).
Many researchers have developed systems that integrate proximity sensors. For example, Chaudhri et al.
[10] and Ding et al. [11] introduced a system that employs
RFID: they attached RFID tags to equipment such as
dumbbells and fixed resistance machines to allow users
to track anaerobic exercise by connecting a tag reader
with the tag located on the device. Sundholm et al. [12]
presented a mat with a resistive pressure sensor matrix
built inside it that can distinguish gym exercises, such as
push-ups, abdominal crunches, squats, and so on.
To provide visual feedback on workout quality, Nagarkoti et al. [13] proposed a system that can use a 2D camera
to detect users' errors in workout form. Another body of
work employs depth cameras for activity tracking.
Although Microsoft stopped the production of Kinect sensors in 2017, several in-depth camera approaches to indoor
exercise tracking inspired by the Kinect sensor have been
developed. For example, Khurana et al. [14] proposed a
Kinect sensor-based system that can detect, recognize, and
track exercise without user intervention. Lin et al. [15]
offered a method to monitor energy expenditure during
physical activity using Kinect sensors. Specifically, they
employed Kinect to collect skeletal data and then mapped
the data to a regression model. Another approach using
Kinect sensors, this one proposed by Reily et al. [16], can
evaluate users' performance on the pommel horse bench-
measuring the consistency of the user's timing and body
angle. Compared to a traditional gym-based exercise, a 3D
camera approach can enhance postural control and optimize user experience [17].
A salient advantage of proximity sensors is their unobtrusiveness. Users do not need to wear anything or bring
their own tracking devices. However, to archive their
information, they should transfer their data to personal
storage (e.g., a smartphone), which is not easy in proximity
to sensor devices. This is due to the fact that there is a burden for users to connect the proximity device into their
personal storage; they must perform actions such as logging into their account or establishing a Bluetooth connection with the device.
Wearable Sensors
About a decade ago, the success of activity tracking applications on smartphones [18]-[20] led to the introduction of
single-purpose wearable devices for activity tracking-fitness trackers. The wide availability of fitness trackers in
the consumer market and the variety of wearable and
mobile sensors make them popular tools for the automatic
data collection of aerobic exercise. Wearable sensors are
typically embedded with accelerometers, gyroscopes, and
electrical and optical heart rate sensors and can actively
	

track a user's heart rate along with instances of aerobic
exercise, such as cycling, running, and swimming [see Figure 1(a)]. Wearable sensors can communicate with ubiquitous personal devices such as smartphones and construct a
wireless-body sensor network (BSN). Studies have shown
that many human-centric applications such as m-Health
and fitness/wellness systems benefit from BSNs [21]-[23].
Wrist-worn wearables are the most common type in
this category. For example, smartwatches are commercialized and used widely. However, a recent study found
that ankle-worn wearables offer more reliable and sensitive results than do wrist-worn wearables [24]. Remarkable initial work from Nam and Park [25] proposed a
waist-worn system embedded with an accelerometer and
a barometric pressure sensor that can detect activities
such as climbing up and down and so forth. Iskandar et al.
[3] combined an electrocardiogram with a necklace to
determine a user's heart rate. Cruz et al. [26] suggested
adopting an earpiece that uses infrared thermometry to
detect heart rate. Akpa et al. [5] offered a glove that can
track fitness activities when the user touches exercise
equipment; 16 force-sensitive resistor sensors on the glove
allow it to track anaerobic exercises such as flexibility
training (e.g., side lunge stretches), dynamic strength
training (e.g., squats), static strength training (e.g.,
planks), and circuit training (e.g., push-ups, bench dips,
and lunges). Another force-sensitive approach proposed
by Zhou et al. [27] introduces a real-time assisted training
feedback system. Their system uses a smart textile on a
shirt as a fabric sensor [28] to track muscle activities by
measuring the pressure on the fabric. Using data acquired
from the shirt sensor, the system can classify anaerobic
exercise such as push-ups, bicep curls, handstands, and
so on. Textile wearables track anaerobic exercises by analyzing muscle responses via electromyography (EMG)
sensors during weight training activities. Another example utilizing EMG was offered by Taha et al. [29]. They suggested attaching an EMG sensor to the user's bicep to
monitor exercise intensity and muscle fatigue.
There are few reports about the feasibility of wearing
gloves or adapting smart shirts while exercising. In addition, in contrast to smartwatches and other wrist-worn
wearables, we have not observed a proliferation of smart
clothes in the wearable market. This might be due to their
high cost as well as hygiene concerns about the frequent
use of smart clothes. There is a need, however, for further
scientific investigation in this area to quantify the challenges that hinder acceptance of other wearables.
One adva ntage of using wea rables for activ ity
tracking is their personalized information collection
approach. In other words, no effort from users is needed
to collect or retain their own data, in contrast to proximity sensors, where data collection devices are shared
among users. Table 1 summarizes some common data collection approaches, based on the type of sensors, i.e.,
wearable and proximity settings.
Ja nu a r y 2021

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE	

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IEEE Systems, Man and Cybernetics Magazine - January 2021

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