Biomedical Sensors and Applications of Wearable Technologies on Arm and Hand

Abstract

Global difficulties, such as rising healthcare costs and staff shortages, have accelerated the transition to new wearable technologies in place of traditional healthcare services. With wearables, patients can be tracked regularly. In the study, wearable biomedical technologies were investigated according to their wearable structures as rigid, soft, and textile based. With the developing technology, it will be possible to get more comfortable, accurate, and better measurements by getting sensors from the textile surface. Resistive sensor, galvanic skin response, capacitive sensor, piezoelectric sensor, optical sensors, semiconductor sensors, and inertial measurement unit are the most used basic sensor types in health studies. In the study, information was given about the sensor types used in health applications. Biomedical applications are described only by classifying the upper-extremity region according to the sub-parts. When looking that are used on arm and hand are explained in the study. It is emphasized by researchers working in the textile field that to be presented as a final product, the wearable must meet natural clothing requirements. The trend toward commercialization is especially in adhesive wearables, integration of new sensor technologies, and miniaturization of wearables. Comfort and flexibility can be achieved using more textile techniques in future, which will greatly increase the usability and accuracy of the devices.

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Vol.:(0123456789)
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Biomedical Materials & Devices
https://doi.org/10.1007/s44174-022-00002-7
REVIEW
Biomedical Sensors andApplications ofWearable Technologies
onArm andHand
MineSeçkin1,2 · AhmetÇağdaşSeçkin3 · ÇetinGençer4
Received: 15 February 2022 / Accepted: 18 July 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2022
Abstract
Global difficulties, such as rising healthcare costs and staff shortages, have accelerated the transition to new wearable tech-
nologies in place of traditional healthcare services. With wearables, patients can be tracked regularly. In the study, wearable
biomedical technologies were investigated according to their wearable structures as rigid, soft, and textile based. With the
developing technology, it will be possible to get more comfortable, accurate, and better measurements by getting sensors
from the textile surface. Resistive sensor, galvanic skin response, capacitive sensor, piezoelectric sensor, optical sensors,
semiconductor sensors, and inertial measurement unit are the most used basic sensor types in health studies. In the study,
information was given about the sensor types used in health applications. Biomedical applications are described only by
classifying the upper-extremity region according to the sub-parts. When looking that are used on arm and hand are explained
in the study. It is emphasized by researchers working in the textile field that to be presented as a final product, the wearable
must meet natural clothing requirements. The trend toward commercialization is especially in adhesive wearables, integration
of new sensor technologies, and miniaturization of wearables. Comfort and flexibility can be achieved using more textile
techniques in future, which will greatly increase the usability and accuracy of the devices.
Keywords Biomedical· Healthcare· Wearables· Sensors· Textile
Introduction
The concept of Wearable Technology (WT) has long been
used in many fields, such as health, sports, electronics, tex-
tiles, and defense [1]. However, there is a perception that
wearable technologies are a new topic as we have not used
most of these technologies directly in our daily lives until
recently. WT has become a very popular topic in the last
decade, mainly due to the possibilities offered by the Inter-
net of Things technology. There are various definitions for
the term WT or wearable devices, and it is an application
area in which different disciplines are used together. Park
etal. focused on the term wearable and stated that these
devices are different from conventional clothing and provide
personalized mobile information processing [2]. Coyle and
Diamond stated that WTs should be soft, flexible, wash-
able, and meet people’s normal wearing expectations [3].
Godfrey etal. focused on smartwatches when explaining
the concept of WT and stated that it includes a large number
of devices that are directly or loosely attached to a person
[4]. Ye etal. defined the concept they studied WT as smart
textiles, fabrics with various integrated electronic compo-
nents and stated that this technology should meet the fun-
damental needs of clothing, such as comfort, lightness, air
permeability, and heat retention [5]. In these studies, the
main common point put forward outside direct definitions
is that wearable devices contain sensors, processing units,
and power sources. In addition, as pointed out by researchers
working in the field of textiles, they are expected to meet the
* Ahmet Çağdaş Seçkin
seckin.ac@gmail.com
Mine Seçkin
mine1seckin@gmail.com
Çetin Gençer
cetingencer@gmail.com
1 Textile Engineering Department, Uşak University, Uşak,
Turkey
2 Physiotherapy Department, Adnan Menderes University,
Aydın, Turkey
3 Computer Engineering Department, Adnan Menderes
University, Aydın, Turkey
4 Electrical andElectronics Department, Fırat University,
Elazığ, Turkey

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natural requirements of clothing, i.e., at least the require-
ments of comfort and appearance, to present the wearable as
a final product. WTs are non-invasive. Thus, no subcutane-
ous treatment is required. However, these devices do collect
information subcutaneously in nature. Devices that require
subcutaneous intervention, such as cochlear implants, pros-
thetics, and orthotics, are not covered by WT. They are worn
and removed without physically going under the skin.
When the topic WT is addressed in relation to busi-
nesses, the existence of a large market potential is an impor-
tant factor in the emergence and application of innovative
and creative ideas. The race of businesses to adapt to the
changing and evolving technology has a great impact on
the integration of WT products in areas, such as healthcare,
textile, education, entertainment, and tourism. According to
the 2020 forecast by Gartner, Inc., the WT devices market
will reach $81.5 billion in 2021, growing at a CAGR of
18.1% globally [6]. The same report highlighted that with
the increasing reliance on WT devices for sports and health,
the market has grown and today there is a strengthening of
the market for ear-worn devices and smartwatches.
There are several approaches to classify WT according to
how people wear them. In their study, Fang etal. examined
WTs as devices worn on the wrist and head [7]. Ometov
etal. classified them as worn on the upper body, on the lower
body, held on the wrist/hand-held, and head-mounted [8].
Dunn etal., on the other hand, divided WT into neurologi-
cal, mental health, cardiovascular, pulmonary, movement,
gastrointestinal, and metabolic based on sensors and func-
tions rather than location [9]. WTs differ in terms of loca-
tion, physical structure, and sensors and are specialized in
applications. Considering this situation, the WTs used in
this study on the arm and hand were examined according to
their anatomical placement, sensors, and wearable structure,
as shown in Fig.1. The organization of the next parts of the
study, respectively, WT placement according to the anatomi-
cal perspective, sensor technologies used in WT, the struc-
ture of wearables, WT applications, and then challenges and
open directions are presented.
Anatomical Placement
In anatomy, the parts of the human body are generally
divided into four: Caput-Collum (head-neck), truncus
(trunk), membrum superius (upper extremity), and mem-
brum inferius (lower extremity). The parts of the upper-
extremity parts which are the subject of this study are
shown in Fig.2. The upper extremity is divided into six
parts: Omos, Brachium, Cubitus, Antebrachium, Carpus,
and Manus. There are many muscles and bones that make
up the upper extremity. The upper extremity is made up of
many different bones, joints, muscles, and ligaments that
allow multiple degrees of freedom of motion. The segmenta-
tion of the upper-extremity regions is by bone, joint, muscle,
and ligament. In addition, the upper extremity also has tis-
sues, such as veins, nerves, skin, nails, and lymph.
The bones are dense, hard structures that support the soft
tissue. Joints, on the other hand, are the junctions of adjacent
bones that allow rotational movement in one or more axes,
like hinges. Ligaments are thick white bands of intact tissue
that hold joints together and allow joint motion. Muscles are
structures that contract and relax to move the joints. They
are all attached to a white cord-like structure called a tendon.
Thanks to the multi-degrees of freedom movement of the
musculoskeletal system of the upper-extremity region is the
most important body region in daily activities. Thus, it is
the region where both indirect physical activities and direct
Fig. 1 Anatomical parts of the
upper extremity

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human–machine interactions can be performed most easily.
Since any abnormality such as nerve compression, fracture,
or injury in this region directly limits physical movement,
this is the most practical region to take measurements of
the muscular and nervous systems for normalization and
rehabilitation.
In the arm and hand, there is an extensive vascular network
that supplies blood to the tissues. These veins are located very
close to the skin, especially in the fingertips and wrist. They
are therefore very suitable for measuring the pulse and the
oxygen ratio in the blood. There are three nerves that supply
the arm and hand: median, radial, and ulnar nerves. Nerves
are like a kind of cable that carries electrical signals with ions.
The signal required for voluntary movement is first triggered in
the brain, then travels down the spine, and is transmitted to the
muscle via the nerves in the arm. The transmission of states,
such as heat, force, and pain, which are perceived in the arm
or hand, to the brain takes place in the opposite way.
Fig. 2 Anatomical parts of the upper extremity

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Sensors inWearable Biomedical Applications
onArm andHand
To perform biomedical applications using WT, it is neces-
sary to bring together many different sensors and collect
data from these sensors [10–12]. Resistive Sensor (RS),
Capacitive Sensor (CS), Piezoelectric sensor (PES), Opti-
cal Sensors (OS), Inertial Measurement Units, and Biopo-
tential Electrode Sensors (BES) are the most used basic
sensor types in health studies. Attaching the sensor for WT
means that the sensor is manufactured externally and later
applied to the wearable by methods, such as gluing, sew-
ing, or attaching. For WT, embedded sensor is the applica-
tion of the sensor with raw materials, such as yarn, elas-
tane, dye, fiber, and polymer, which make up the structure
of the wearable. Examples of attaching and embedding are
shown in Fig.3. In this section, the sensor types used on
arm and hand WT in health applications have been studied.
RS are resistive sensor systems that limit the applied
current and provide a physical measurement by dividing
the voltage or current. The most typical application of
these sensors is to knit conductive yarns and exploit the
change in resistance value caused by stretching and bend-
ing of the fabric from which these yarns are formed. In
systems with RS, the change in shape measures the move-
ment at a low level. In addition, situations such as liquid
contact and sweating affect the measurement and prevent
long-term use. Liquid-isolated systems are used to prevent
this situation. For this purpose, coatings are applied on
the Strain Gauge sensor and such a sensor is sold in the
market. Strain gauge sensors basically consist of only one
conductor. The factors that cause the length and width of
the conductor to change are measured by this sensor. The
typical resistance of the strain gauges is very low and the
resistance value is measured using bridge circuits. This
sensor must be surface mounted to detect hand move-
ment. Strain Gauge sensors are custom made and relatively
expensive. Consideration is being given to the use of a
conductive liquid in silicon (eGaIn) which is a new type
of insulated resistance sensor. These sensors are fabricated
by molding sealed, flexible microchannels using materials
such as rubber and silicone and injecting conductive liq-
uid into these molds. These sensors, which are difficult to
fabricate and design, are used to create high-performance
applications. However, puncture or rupture of the system
can easily disrupt the operation of the system as well as
transfer the harmful fluid to humans [13, 14]. One type of
RS is the ink RS. This kind of RS is made with conduc-
tive doped ink (silver nanoparticles or graphene doped).
Fig. 3 Sensor attachment and embedding

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The special conductive ink loaded into the inkjet printer is
applied to the target surface in the desired dimensions and
structures with a certain resistance are obtained. Changes
such as flexion, bending, and distortion on the surface on
which the ink is applied affect the length and width of the
conductor and hence a resistance that varies according to
the effects. Since the force exerted on the textile surface
due to stretching, bending, and distortion can be recorded,
studies such as pressure [15–17] and breathing rate [18,
19] can be performed. Compared with conventional RS
fastening methods, these systems can be more easily used
in mass production and offer low cost and more customi-
zation options. Galvanic skin response (GSR) is a type of
resistive sensor.
A capacitor is a passive circuit element consisting of
dielectric material between two conducting plates. Meth-
ods such as the fill-discharge method or frequency response
are used to measure the value of capacitive sensors. The
capacity changes by changing dielectric material between
the two plates and/or by changing the dimensions. As the
capacitance changes, so does the capacitive reactance, which
opposes the alternating current. Sensors designed to take
advantage of this property of capacitors are called CS. CS
is a commonly used sensor for finger movement, blink-
ing, wrist pulse measurement, prosthetic limbs, healthcare
applications [20], and especially for hand gesture recogni-
tion studies [21]. CS is particularly preferred because of
the insulating nature of biological materials and offers new
horizons for measuring biopotential energy in humans [22].
CS electrodes generally do not require skin contact, the use
of conductive fluid/gel, or fixation. Therefore, it is more
advantageous than other methods. In addition, capacitive
sensors can measure more accurately and consistently than
other methods. A soft electronic skin was formed with the
paint prepared using liquid metal. The capacitor sensor is
integrated with the geometry created on the electronic skin.
This sensor can detect changes in shape, so it is a system that
can provide data for biomechanical analysis in the glued area
of the body [23]. There are few systems with flexible tex-
tile surfaces for severe diseases, such as spinal cord injury,
stroke, and brain injury. It can also be made with a flexible
textile backing of CS. Thus, the perception of gestures and
movements can be realized with minimal disturbance in
patients with degenerative (Amyotrophic lateral sclerosis)
and magnetic (Guillian–Barre syndrome) diseases [17]. It
can be formed in CS with conductive ink, a technique used
in RS. It can be formed in CS with conductive ink, a tech-
nique used in RS. It is fabricated using a similar method to
RS and used in applications, such as respiration [24].
PES is a system that measures by providing a piezo crys-
talline electron flow as a function of the applied mechanical
force and scaling this load flow to the force [25]. Piezoelec-
tric sensors appear in various forms, e.g., load cell, buzzer,
and vibration sensor. There are examples of respiratory rate
imaging in biomedical studies using PES [26]. Piezoelectric
sensors are very accurate and fast, as well as very cheap.
Piezoelectric crystals are easy to break or crack. For this
reason, the force to be affected should be known in advance
and fabrication should be done accordingly. It is difficult
to use them on highly flexible surfaces and textures. These
surfaces are easily deformed even when a small force is
applied. Therefore, flexible fabrication processes of PES
have recently emerged, providing opportunities for future
developments [27, 28]. Organic piezoelectric biomateri-
als also have the advantage of environmental friendliness.
Organic piezo sensors are claimed to be beneficial because
they are more compatible with skin. Today, sensor devel-
opment and application studies are continuing this subject.
However, organic piezo sensors have not reached the com-
mercial application phase yet. [27, 29].
Optical Sensors (OS) work on emitting light of different
wavelengths and measuring the change caused by the reflec-
tion of this light. Today, mainly semiconductor and optical
base technologies are used to both emit light and measure
reflected light. Many structures such as LEDs, fiber optics,
photodiodes, and LDRs can be used as OS. However, as a
biomedical sensor, Fiber Optic Sensors (FOS) and InfraRed
Sensors (IRS) are more popular than other studies. Conven-
tional optical fibers are hollow, mirrored filaments. They
are made by drawing optical fibers. Optical fibers used as
sensors, namely FOS, have special regions that can bend
or curve. Light is refracted in these areas. The light coming
from the light source of the optic fiber sensor is bent accord-
ing to the change in the flexible region and when it reaches
the receiving end, changes in both light intensity and light
wavelength occur. These changes are related to the effect
acting on the specific site of the FOS and biomedical data
such as pressure [30], respiratory rate [31], heart rate [32,
33], and diabetes [34] can be measured [35]. In this way, dis-
eases such as sleep apnea, asthma, and sudden infant death
syndrome can be diagnosed and tracked. Optical fibers have
important advantages because they are inexpensive, insensi-
tive to interference, not affected by fluid contact, and com-
fortable to wear. FOS can be easily applied to textile surfaces
thanks to their ability to bend and twist. In this way, it adapts
to the human body much better than other sensor technolo-
gies. There are many examples developed by embedding
optical fibers in textiles using knitting, weaving, and embroi-
dery methods [36]. OS sensors are used to detect pressures
and textile displacements in applications where electrical
currents cannot pass through textile substrates [37]. IRS are
sensors that work on the principle of emitting infrared light
and measuring the reflected light intensity. The work done
with this sensor is also called Photoplethysmogram (PPG)
or oximeter. The infrared LEDs used in these sensors emit
between 700 and 900nm. The emitted light indicates the

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O2 content in the blood and the value increasing with the
heartbeat increases the amount of directly reflected light.
Pulse detection is usually performed with the sensor operat-
ing in this way [38]. Patient monitoring applications are also
performed by adding temperature or pressure sensors [10]
alongside this sensor. In addition to typical cardiac applica-
tions, hand movements have also been detected using infra-
red light scattering in the body [39, 40].
Inertial Measurement Unit (IMU) is a microelectrome-
chanical type of sensor that consists of the combination of
more than one sensor. On the sensor IMU there are accel-
erometer, gyroscope, and compass sensors. Accelerometer
is used to measure the change in acceleration caused by the
force applied. Gyroscope can determine the amount of the
sensor’s angular rotation. The compass is used to measure
the sensor’s orientation with respect to the earth’s magnetic
field. The compass is mostly used to support the gyroscope
sensor. With IMU sensors, applications in body movements
are made very successfully, but the applications in hand
movements are at the level of perception of rude actions,
such as arm movements and hand waving or clapping [41].
The sensors from IMU can be used to evaluate indicators
of rehabilitation performance by therapists in gait analysis,
fall detection, sleep monitoring, and physical rehabilitation
[42, 43]. By combining IMU sensors with other sensors, it
is possible to collect more accurate data.
Biopotential electrode sensors are systems for measur-
ing electrical changes that occur on biological tissue. The
electrodes act as signal carriers between the biological tissue
and the measurement circuit. Generally, good conductors
such as gold and silver are preferred as electrodes to reduce
electrical resistance. However, for WT, it is important to
avoid corrosion and irritation when in contact with the skin,
so there are studies on carbon-based electrodes [44]. BES
circuit consists of various sections, such as amplifier, filter,
and converter. By measuring the flow of ionic current in the
body with BES, nerve impulses and muscle contractions can
be detected. Electromyography (EMG) systems are used for
muscle tissue contraction studies, and electrocardiography
(ECG) systems are used for heart activity.
The Electromyography (EMG) is a method used to
measure the natural electrical activity of muscles. EMG is a
method used in disease diagnosis and human–machine inter-
action. EMG is a method to detect dysfunction of nerves,
diagnose diseases that affect nerves and muscles, or deter-
mine the severity of damage. EMG works through a measur-
ing circuit and electrodes placed on or in the muscle. The
electrical signal formed in the muscle is transmitted to the
measurement circuit via the electrodes. The measurement
circuit amplifies, filters, quantizes, and samples the signal.
EMG electrodes must interact closely with the muscle. Nee-
dle electrodes are used for detailed information collection
because slippage and sweating can occur on the skin. Needle
electrodes used for medical purposes cannot be worn on the
body because penetrating the muscle requires expertise.
Wearable EMG devices are in a structure that attaches to the
skin or provides a firm hold on the muscle. When it comes to
EMG, people usually think of the invasive variety where the
subcutaneous EMG sensor is attached to the muscle bundles
with needles. Hand movements can be successfully discrimi-
nated with these studies [45, 46]. However, the major dis-
advantage of the wearables created with EMG is the bias of
the measurements due to the sensor slipping away from the
sites that receive the muscle signals. The fixed placement of
these sensors is done by sticking the electrodes to the skin
[47]. EMG applications include facial trigeminal neuropathy
in the wrist (ulnar, radial, peroneal), leg (femoral), and face
[48, 49]. Commercially available versions of EMG are also
on the market. In general, there are studies on physiotherapy
and gesture recognition [50, 51]. A data-logging study has
been done while the EMG and the IMU are together on the
elbow pivot when the hands are empty and full [52]. The
most used commercial product for IMUs are smartwatches.
Among these products, applications such as pedometer and
physical activity detection are widely used. However, they
are used for purposes such as treatment and motion control
rather than diagnosis of a disease [53, 54]. SCS are basically
negatively and positively doped crystalline materials and are
designed to respond to heat, force, light, or chemical factors.
They are used to convert many similar factors into electrical
energy and thus act as sensors.
The Electrocardiogram (ECG) is an electronic system that
allows you to check the rhythm and electrical activity of your
heart. Electrodes placed on your skin record the electrical
signals your heart produces with each beat. These signals are
amplified, filtered, and sampled by a measuring circuit and
converted into a digital signal record. The records obtained
are then examined for unusual conditions, such as cardiac
arrhythmias, coronary artery disease, heart attacks, and car-
diomyopathy. Medical ECG machines use many electrodes
attached to the arms, legs, and chest, but this system is very
cumbersome. Efforts have been made to mobilize the ECG
system and make it wearable for a long time. Brachium and
carpus regions have been investigated for upper-extremity
applications, and pulse and cardiac monitoring processes
have been performed in several studies [55–61].
The use of sensors alone produces biased information.
This situation is insufficient for many applications. There-
fore, applications are performed by collecting data from dif-
ferent sensors simultaneously, which is called sensor fusion
or multi-sensor. Seçkin’s multi-sensor study combined IMU,
PPG, and GSR sensors in one glove and tried to analyze dif-
ferent body activities [62]. Roberts etal. used more than one
wearable device at a time in his study. In this study, sleep
state detection and monitoring were performed using data
collected with two different watches and a ring [63].

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Wearable Structures
WT products can be used as textiles and accessories as well
as hybrids, but in terms of their structure, they are basically
divided into two as hard or soft. Rigid wearable technologies
are made of hard materials and are mostly accessory or jew-
elry type ones. In wearable technologies used as accessories,
they collect data from the human body created by modifying
devices such as watches [7, 64], glasses [65], necklaces, and
rings [66] other than clothing. In rigid body structures, most
of the body is nonfunctional. It does not assume a sensor
function. It only provides mechanical support for the sen-
sor to be placed in the right place. These sensors measure
changes in living things. In rigid wearable technologies,
the entire product does not act as a sensor. As the rigidity
decreases, the sensor placement becomes fixed.
Soft WT structures are made of flexible and elastic
materials. Soft structures are divided into two categories:
breathable and nonbreathable. Nonbreathable structures are
membrane, sandwich, or coating like and do not allow air
or liquid to pass through. Breathable structures are manu-
factured as textile surfaces and allow air or liquid to pass
through. Nonbreathable WT are types that can be fabricated
by injecting conductive alloys into soft, silicone rubber-like
materials [13, 67, 68]. In these types, molds are prepared
according to the area to be measured. The soft material is
poured into the prepared molds. Thanks to its softness and
coverage of the measurement area, it is possible to perform
precise measurements. The sensor position is more stable
than in rigid wearable technology. In soft wearable tech-
nologies, the measurement method sensors are based on
deformation, i.e., the surface acts as a sensor. Soft sensors
are well suited for continuous monitoring of body move-
ments, human–machine interfaces, and measurement of
human body’s physiological parameters compared to their
traditional, more rigid counterparts. These sensors also make
the measurement very suitable for soft robotic applications
and exoskeletons [69]. Breathable WT generally consist of
conductors, sensors, special fibers, and other electronic ele-
ments embedded in textiles using techniques, such as weav-
ing [70], embroidery [71–73], and knitting [74]. Among
these systems, attaching sensors to the textile surface is the
most common and simplest. This method is done by sewing
or gluing a typical sensor directly onto the textile surface.
The transmission lines required for the operation of these
sensors are applied to the textile using conductive threads
or a cable pull method. Another method in textile-based sen-
sor applications is to impart sensor properties to the textile
surface itself by various modifications [75]. These modifi-
cations are made in two ways, internal and external. Inter-
nal modification is the sensitization of the textile surface to
external factors, thanks to a chemical or mechanical addition
made during the manufacture of the cation yarn or fiber. In
this way, the textile fiber or yarn itself becomes a sensor.
External modification refers to the conversion of the textile
surface into a sensor by applying processes such as dyeing,
various coating methods, or lamination after the textile sur-
face is manufactured. In this method, only the desired area
is sensed [75].Soft breathable, soft non-breathable and rigid
structure examples are shown in Fig 4.
Wearable Applications onUpper Extremity
The bones of the human upper extremities consist of
humerus (hip bone), antebrachium (radius and ulna), carpal
bones (wrist bone), metacarpus (crest bone), and phalanx
(finger bone). In this section, wearable technologies used
in the upper extremity are listed. Although there are many
studies on hand bones, they are mostly used for hand ges-
ture recognition [76]. As a result of the literature studies,
the carpal bones have been the most used area of wearable
technologies for health monitoring.
Tomoyuki etal. have provided the capability for wireless
transmission of vital signs, such as heartbeat, which can be
worn by the patient with a super flexible structure and ultra-
thin electronic skin, by connecting to a remote electrocardio-
gram [77, 78]. Alexander etal. developed a lightweight, wear-
able ECG device which is used for monitoring based on an
armband. The developed device is placed on an armband. It
can detect electrocardiogram potential through clothing using
capacitively attached electrodes. The resulting signals were
then transmitted to an ECG device for filtering and amplifica-
tion of the signals were processed by the microprocessor. The
acquired data were sent via Bluetooth to smart phones and
displayed in real time [79]. Yotha etal. designed a wristband
that delivers a hypoglycemic stimulus consisting of two parts.
When patients are treated with an insulin pump, they can wear
the bracelet and inject insulin into the body. The device has
humidity, temperature, and heart rate sensors. Heart rate can
be measured through the skin and sends the data to for process-
ing. The controller analyzes it on the screen while monitoring
[80]. Zhang etal. presented a paper about feasibility of using
ear and one-arm ECG for user recognition, which is expected
to contribute to ECG-based user identification in health appli-
cations [81]. Villegas etal. investigated the design, develop-
ment, and prototyping of an arm wearable ECG sensor system
for non-invasive long-term monitoring of cardiac rhythm [61].
Jha etal. developed an anger tracking unit in the form of a
wearable wristband that records the anger of individuals and
reports it to the monitoring unit using mobile phone (GSM)
technology. The person's anger is determined based on the
device, body temperature and heart rate. In the circuit, a ther-
mistor is used to detect the body temperature, a pulse sensor
is used to detect the heart rate, and an accelerometer is used

Biomedical Materials & Devices
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to detect the body movements [82]. One of the most common
studies performed with wristbands is heart rate measurement
[60, 83]. Lukowicz etal. recorded patients’ blood oxygen level,
heart rate, and body temperature and transmitted them to medi-
cal staff via a wireless data link [84]. Yu Fu etal. designed a
system that uses a wireless transmission module to provide
information about the heart rate and the amount of oxygen in
the blood of the athlete. The system also sends the obtained
data to the smartphones of athletes, coaches, or doctors for
processing and analysis [85]. Kaisti etal. designed a wristband
with MEMS pressure sensors to assess cardiovascular health
status and detect early onset of cardiovascular disease [10].
Kim etal. They designed a wristband to continuously monitor
epidermal pulse rate for blood pressure estimation and a wire-
less wearable device to monitor body pressure using a multiple
pressure sensor [86]. Escobedo etal. stated that determination
of pH in sweat can be an indicator of health and well-being
and can even be used to diagnose possible diseases. For this
purpose, they designed a wristband for continuous collection
of sweat on the skin with a color-based pH sensor range [87].
Acar etal. designed a wristband that can collect electrocar-
diogram with graphene fabric attached to an arm [55]. Tajitsu
etal. developed a wearable piezoelectric sensor for the wrist
to capture pressure signals from the heartbeat [88].
The data obtained from the patient’s daily life, without
being in the hospital, are of great importance for the correct
diagnosis, treatment, and follow-up of the diseases. There are
many applications for wristbands as the wristband is the most
widely used and convenient among wearable devices [89–93,
83]. Smart bracelets and watches are the application that is
considered the most commercial product in the wearable tech-
nologies [94–96]. Table1 provides a summary of the wear-
able application examples of the membrum superius (upper
extremity). More sensor types are used in this region than in
other regions. The commercialization of products used in this
region is more than other regions. The biggest advantage in
distribution is applications that provide comfort to the user.
Challenges andOpen Directions
Design andProduction
The most widely used WT is smart watches and wristbands.
From the literature review, there is not yet a mature and
Fig. 4 Wearable structures

Biomedical Materials & Devices
1 3
popular product on the market for other WT. However, there
is a great need for human motion sensing and its use as a
human–computer interface. Commercial EMG WTs that
excel in this regard are promising. It turns out that commer-
cial products are always made of rigid and nonbreathable
materials to withstand prolonged use, rather than being tex-
tile based. Focusing on textile-based wearable technologies
is more suitable in terms of comfort and does not restrict the
user. Currently, the main challenges for sensors installed on
the product are electronic connectivity, contact with liquids,
and washability. These challenges prevent commercializa-
tion as they directly affect the textile-based WT production
and design processes.
Safety andPrivacy
All commercial wearable devices collect data about your
daily activities and physical condition. This information is
processed, analyzed, stored, and even made publicly avail-
able on social media by the manufacturer or some applica-
tion providers. For reasons of patient rights and ethics, only
the patient and the doctor in question have access to the
biomedical data. In other cases, access may be granted with
specific ethical approval. However, with wearable technolo-
gies, many types of biomedical data are collected over a long
period of time. While data collected in health care facilities
are immediate and access is limited, long-term data collec-
tion in commercial products can be used to collect informa-
tion about higher-intensity activities. This simply involves
users signing an access text and granting access to their
data. Today, surprising information can be obtained with
Table 1 Application examples of upper-extremity
Sensor Aim Anatomical placement Sensor integration Wearable structure References
RS Pulse rate monitoring Carpus Attached Soft nonbreathable [86]
Sweat pH monitoring Carpus Attached Rigid [87]
Pulse Carpus Embedded Soft nonbreathable [16]
CS Health monitoring Carpus Attached Soft breathable [97]
Hand recognition Carpus Attached Soft nonbreathable [98]
Hand recognition Carpus Attached Soft nonbreathable [21]
PES Pulse rate Carpus Embedded Soft breathable [88]
Cardiac monitoring Carpus Embedded Soft breathable [99]
Finger gesture recognition Carpus Attached Soft breathable [100]
OS Heartbeat (PPG) Carpus Embedded Rigid [101]
Atrial fibrillation detection (PPG) Carpus Embedded Rigid [102]
Blood oxygen saturation and pulse, Hypogly-
cemia, and Anger monitoring
Carpus Attached Rigid [85, 82, 80]
Body joint motion Cubitus Embedded Rigid [103]
Pulse movement monitoring with additional
IMU
Carpus Attached Soft nonbreathable [104]
Health monitoring with additional TS Carpus Attached Rigid [83]
Health monitoring Carpus Attached Rigid [10, 93]
IMU Stroke rehabilitation Brachium Attached Rigid [105]
Motion monitoring Antebrachium Attached Soft breathable [106]
Shoulder rehabilitation Omos Attached Soft breathable [107]
Heart Cardiac monitoring Brachium Attached Rigid [79, 57, 59, 61]
ECG Pulse rate parametrization Carpus Attached Rigid [60]
Cardiac monitoring Brachium Embedded Soft breathable [55]
Cardiac monitoring Brachium Attached Soft breathable [58]
Biometric Authentication Carpus Attached Soft nonbreathable [108]
EMG Deaf people communication Brachium Attached Soft nonbreathable [109]
Emergency Detection Brachium Attached Soft nonbreathable [110]
Sensor fusion Blood pressure (PPG),
IMU, PPG, and GSR
Manus Attached Soft breathable [62]
Sleep detection with multiple wearable PPG
and IMU
Carpus-Manus Attached Rigid [63]

Biomedical Materials & Devices
1 3
data mining and machine learning. Moreover, since today’s
commercial devices are connected to your smartphone via
wireless communication, hackers can exploit software vul-
nerabilities to gain access to your data. Therefore, in future,
it will be necessary to establish working and legal agree-
ments in wearable data privacy and security.
Comfort andUser Acceptance
The most important feature in the commercialization of
wearable devices is the customers accept to use the prod-
uct. This process consists of design, production, and testing
phases. The product, whose design and production tasks
have been completed, must be tested before it is presented
to the customer. Testing involves verifying that the system is
mature enough to be put into service, in terms of both physi-
cally and software. At this stage, a device worn for a long
period of time should not bother the user, i.e., it should be
comfortable. Many parameters, such as the materials from
which WT devices are made, the components they contain,
and the working time, directly affect comfort. Since the
problems at this stage lead to a re-planning of the design
and production processes, which are the first steps of product
development, the consideration of comfort at the first step
will also be the main problem of scientific studies.
Conclusion andFuture Challenges
As a result of literature searches, mainly on skin applications
attract attention in resistance and capacitive applications.
However, the long-term use of these studies raises concerns
about comfort, especially since they prevent sweating.
For this reason, there is still room for improvement. Opti-
cal sensors, on the other hand, offer new horizons as they
allow versatile physiological applications thanks to optical
applications such as blood pressure, heart rate, and glucose
measurement and are more resistant to situations such as
liquid contact. These sensors will play a key role in future
of wearable technologies, enabling highly accurate measure-
ments of otherwise invisible environmental information and
parameters. Furthermore, as optical applications can provide
visual effects, this also enables various fashion designs in
textile applications.
In embedded sensors, highly conductive metals such
as copper, silver, or gold are used directly as thin cables
or as nanoparticles in textiles both as surfaces and yarns.
In addition to these applications, flexible sensor yarns
can also be fabricated by depositing conductive liquid
metal alloys on nanotubes. It is expected that the use of
non-metallic fibers, which are already produced as fibers,
will become more widespread in future. The most preva-
lent technology in the field of wearable technologies on
arm and hand are smart watches and wristbands. These
wearable technologies do not limit comfort or freedom
of movement. It appears that there are many commercial
applications of wristbands in the orientation of the region
being worn. When the literature studies are examined, the
common points that show up directly or indirectly in all of
them are that wearable devices contain sensors, process-
ing units, and power sources. Moreover, as emphasized by
the researchers working in the textile field, the wearable
as a final product must meet the requirements of natural
clothing, that is, at least the requirements of comfort and
appearance. Comfort is an important parameter in wear-
able technologies. The technology should provide comfort
and convenience, especially in health applications.
In the coming days, the popularity of smart wearables
used in jewelry, watch necklaces, and the like will decrease.
The direction that will increase more will be to incorpo-
rate them in textiles. It will be about putting sensors and
electronic circuits inside the textile. In the next part, sub-
cutaneous applications will be popular. It is also possible
to get more intense information from them. However, since
it is a subcutaneous procedure, it will not be popular soon
because of concerns, such as people do not want to have
this procedure or privacy. This will be used in mandatory
health situations. It is already being used in these situations.
Another important point where improvement is expected is
that even if the conductor is applied to the textile surface
with embroidery or printing, the integrated parts need to
be soldered. Soldering materials are hard materials. It will
cause discomfort to the user. Making flexible conductive
adhesives instead of them is an important parameter. Solder-
ing application is applied with temperature. Therefore, the
textile surface also deteriorates due to heat treatment.
Funding The authors received no financial support for this article.
Declarations
Conflict of interest The authors state that there is no conflict of interest
between them.
References
1. S.D. Guler, M. Gannon, K. Sicchio, A brief history of wearables,
in Crafting Wearables: Blending Technology with Fashion. ed.
by S.D. Guler, M. Gannon, K. Sicchio (Apress, Berkeley, CA,
2016), pp. 3–10
2. S. Park, S. Jayaraman, Wearables: Fundamentals, Advancements,
and a Roadmap for the Future, in Wearable Sensors (Second Edi-
tion). (Academic Press, Oxford, 2021), pp. 3–27
3. S. Coyle, D. Diamond, Medical applications of smart textiles,
in Advances in Smart Medical Textiles. (Woodhead Publishing,
Oxford, 2016), pp. 215–237

Biomedical Materials & Devices
1 3
4. A. Godfrey, V. Hetherington, H. Shum etal., From A to Z: wear-
able technology explained. Maturitas 113, 40–47 (2018). https://
doi. org/ 10. 1016/j. matur itas. 2018. 04. 012
5. C. Ye, J. Ren, Y. Wang etal., Design and Fabrication of Silk
Templated Electronic Yarns and Applications in Multifunctional
Textiles. Matter 1, 1411–1425 (2019). https:// doi. org/ 10. 1016/j.
matt. 2019. 07. 016
6. R. Atwal, A. Antin, A. Teng, Forecast: wearable electronic
devices, worldwide, 2021. Gartner Res. (2021)
7. B. Fang, F. Sun, H. Liu etal., Wearable Technology for Robotic
Manipulation and Learning (Springer, Berlin, 2020)
8. A. Ometov, V. Shubina, L. Klus etal., A survey on wearable
technology: history, state-of-the-art and current challenges. Com-
put. Netw. 193, 108074 (2021). https:// doi. org/ 10. 1016/j. comnet.
2021. 108074
9. J. Dunn, R. Runge, M. Snyder, Wearables and the medical revo-
lution. Pers. Med. 15, 429–448 (2018). https:// doi. org/ 10. 2217/
pme- 2018- 0044
10. M. Kaisti, J. Leppänen, O. Lahdenoja, etal. Wearable pressure
sensor array for health monitoring, in 2017 Computing in Car-
diology (CinC) (IEEE, 2017), pp 1–4
11. X. Liang, R. Ghannam, H. Heidari, Wrist-worn gesture sensing
with wearable intelligence. IEEE Sens. J. 19, 1082–1090 (2018)
12. P.P. Ray, D. Dash, N. Kumar, Sensors for internet of medical
things: state-of-the-art, security and privacy issues, challenges
and future directions. Comput. Commun. (2020)
13. Y. Mengüç, Y.-L. Park, H. Pei etal., Wearable soft sensing suit
for human gait measurement. Int. J. Robot. Res. 33, 1748–1764
(2014). https:// doi. org/ 10. 1177/ 02783 64914 543793
14. S. Yao, L. Vargas, X. Hu, Y. Zhu, A novel finger kinematic track-
ing method based on skin-like wearable strain sensors. IEEE
Sens. J. 18, 3010–3015 (2018)
15. C.-T. Huang, C.-L. Shen, C.-F. Tang, S.-H. Chang, A wearable
yarn-based piezo-resistive sensor. Sens. Actuators A 141, 396–
403 (2008). https:// doi. org/ 10. 1016/j. sna. 2007. 10. 069
16. L.-W. Lo, H. Shi, H. Wan etal., Inkjet-printed soft resistive pres-
sure sensor patch for wearable electronics applications. Adv.
Mater. Technol. 5, 1900717 (2020). https:// doi. org/ 10. 1002/ admt.
20190 0717
17. G. Singh, A. Nelson, R. Robucci, etal. Inviz: low-power person-
alized gesture recognition using wearable textile capacitive sen-
sor arrays, in 2015 IEEE International Conference on Pervasive
Computing and Communications (PerCom) (2015), pp 198–206
18. V. Balakrishnan, T. Dinh, A.R.M. Foisal etal., Based electronics
using graphite and silver nanoparticles for respiration monitor-
ing. IEEE Sens. J. 19, 11784–11790 (2019)
19. H. Lee, H. Cho, E. Lee etal., Fabrication of strain sensor based
on graphene/polyurethane nanoweb and respiration measure-
ment. Sci. Emot. Sensibil. 22, 15–22 (2019)
20. L. Xie, P. Chen, S. Chen etal., Low-cost and highly sensitive
wearable sensor based on napkin for health monitoring. Sensors
19, 3427 (2019). https:// doi. org/ 10. 3390/ s1915 3427
21. H. Truong, P. Nguyen, A. Nguyen, etal. Capacitive sensing
3d-printed wristband for enriched hand gesture recognition, in
Proceedings of the 2017 Workshop on Wearable Systems and
Applications (Association for Computing Machinery, New York,
2017), pp 11–15
22. Y. Sun, X.B. Yu, Capacitive biopotential measurement for elec-
trophysiological signal acquisition: a review. IEEE Sens. J. 16,
2832–2853 (2016). https:// doi. org/ 10. 1109/ JSEN. 2016. 25193 92
23. L. Sheng, S. Teo, J. Liu, Liquid-metal-painted stretchable capaci-
tor sensors for wearable healthcare electronics. J Med Biol Eng
36, 265–272 (2016). https:// doi. org/ 10. 1007/ s40846- 016- 0129-9
24. S. Malik, M. Ahmad, M. Punjiya, etal., Respiration monitoring
using a flexible paper-based capacitive sensor, in 2018 IEEE Sen-
sors (IEEE, 2018), pp 1–4
25. Y. Cha, J. Seo, J.-S. Kim, J.-M. Park, Human–computer interface
glove using flexible piezoelectric sensors. Smart Mater. Struct.
26, 057002 (2017)
26. Z. Liu, S. Zhang, Y.M. Jin etal., Flexible piezoelectric nano-
generator in wearable self-powered active sensor for respiration
and healthcare monitoring. Semicond. Sci. Technol. 32, 064004
(2017)
27. E.S. Hosseini, L. Manjakkal, D. Shakthivel, R. Dahiya, Glycine–
Chitosan-based flexible biodegradable piezoelectric pressure sen-
sor. ACS Appl. Mater. Interfaces 12, 9008–9016 (2020). https://
doi. org/ 10. 1021/ acsami. 9b210 52
28. H. Zhou, Y. Zhang, Y. Qiu etal., Stretchable piezoelectric energy
harvesters and self-powered sensors for wearable and implant-
able devices. Biosens. Bioelectron. 168, 112569 (2020). https://
doi. org/ 10. 1016/j. bios. 2020. 112569
29. D.-M. Shin, S.W. Hong, Y.-H. Hwang, Recent advances in
organic piezoelectric biomaterials for energy and biomedical
applications. Nanomaterials 10, 123 (2020). https:// doi. org/ 10.
3390/ nano1 00101 23
30. M. Rothmaier, M.P. Luong, F. Clemens, Textile pressure sen-
sor made of flexible plastic optical fibers. Sensors 8, 4318–4329
(2008)
31. J. Witt, F. Narbonneau, M. Schukar etal., Medical textiles with
embedded fiber optic sensors for monitoring of respiratory move-
ment. IEEE Sens. J. 12, 246–254 (2012). https:// doi. org/ 10. 1109/
JSEN. 2011. 21584 16
32. Y. Koyama, M. Nishiyama, K. Watanabe, Smart textile using
hetero-core optical fiber for heartbeat and respiration monitoring.
IEEE Sens. J. 18, 6175–6180 (2018). https:// doi. org/ 10. 1109/
JSEN. 2018. 28473 33
33. X. Yang, Z. Chen, C.S.M. Elvin etal., Textile fiber optic micro-
bend sensor used for heartbeat and respiration monitoring. IEEE
Sens. J. 15, 757–761 (2015). https:// doi. org/ 10. 1109/ JSEN. 2014.
23536 40
34. B. Najafi, H. Mohseni, G.S. Grewal etal., An optical-fiber-based
smart textile (smart socks) to manage biomechanical risk factors
associated with diabetic foot amputation. J. Diabetes Sci. Tech-
nol. 11, 668–677 (2017)
35. Z. Gong, Z. Xiang, X. OuYang etal., Wearable fiber optic tech-
nology based on smart textile: a review. Materials 12, 3311
(2019)
36. K. Bremer, F. Weigand, Y. Zheng etal., Structural health moni-
toring using textile reinforcement structures with integrated opti-
cal fiber sensors. Sensors 17, 345 (2017)
37. C. Gonçalves, A. Ferreira da Silva, J. Gomes, R. Simoes, Wear-
able e-textile technologies: a review on sensors. Actuators Con-
trol Elem. Invent. 3, 14 (2018). https:// doi. org/ 10. 3390/ inven
tions 30100 14
38. S. Zang, Q. Wang, Q. Mi etal., A facile, precise radial artery
pulse sensor based on stretchable graphene-coated fiber. Sens.
Actuators A 267, 532–537 (2017)
39. J. Knibbe, D.M. Plasencia, C. Bainbridge, etal., Extending
interaction for smart watches: enabling bimanual around device
control. in CHI ’14 Extended Abstracts on Human Factors in
Computing Systems (Association for Computing Machinery, New
York, 2014), pp 1891–1896
40. Y. Zhang, R. Xiao, C. Harrison, Advancing hand gesture recog-
nition with high resolution electrical impedance tomography, in
Proceedings of the 29th Annual Symposium on User Interface
Software and Technology (2016), pp 843–850
41. J. Ryu, J. Seo, H. Jebelli, S. Lee, Automated action recogni-
tion using an accelerometer-embedded wristband-type activity
tracker. J. Constr. Eng. Manag. 145, 04018114 (2019)
42. A. Buke, F. Gaoli, W. Yongcai etal., Healthcare algorithms by
wearable inertial sensors: a survey. China Commun. 12, 1–12
(2015)

Biomedical Materials & Devices
1 3
43. A. Kos, A. Umek, Wearable sensor devices for prevention and
rehabilitation in healthcare: swimming exercise with real-time
therapist feedback. IEEE Internet Things J. 6, 1331–1341
(2019). https:// doi. org/ 10. 1109/ JIOT. 2018. 28506 64
44. S. Ha, C. Kim, H. Wang etal., Low-power integrated circuits
for wearable electrophysiology, in Wearable Sensors, 2nd
edn., ed. by E. Sazonov (Academic Press, Oxford, 2021), pp.
163–199
45. W. Geng, Y. Du, W. Jin etal., Gesture recognition by instanta-
neous surface EMG images. Sci. Rep. 6, 36571 (2016)
46. S. Tam, M. Boukadoum, A. Campeau-Lecours, B. Gosselin,
A fully embedded adaptive real-time hand gesture classifier
leveraging HD-sEMG and deep learning. IEEE Trans. Biomed.
Circuits Syst. 14, 232–243 (2019)
47. G. Tröster, The agenda of wearable healthcare. Yearb. Med.
Inform. 14, 125–138 (2005). https:// doi. org/ 10. 1055/s- 0038-
16384 46
48. R. Merletti, P.J. Parker, Electromyography: Physiology, Engi-
neering, and Non-invasive Applications (Wiley, 2004)
49. F. Sadikoglu, C. Kavalcioglu, B. Dagman, Electromyogram
(EMG) signal detection, classification of EMG signals and
diagnosis of neuropathy muscle disease. Procedia Comput.
Sci. 120, 422–429 (2017)
50. J. Ding, R.-Z. Lin, Z.-Y. Lin, Service robot system with inte-
gration of wearable Myo armband for specialized hand gesture
human–computer interfaces for people with disabilities with
mobility problems. Comput. Electr. Eng. 69, 815–827 (2018)
51. M. Sathiyanarayanan, S. Rajan, MYO Armband for physiother-
apy healthcare: a case study using gesture recognition applica-
tion, in 2016 8th International Conference on Communication
Systems and Networks (COMSNETS) (IEEE, 2016), pp 1–6
52. M. Tanweer, K.A.I. Halonen, Development of wearable hard-
ware platform to measure the ECG and EMG with IMU to
detect motion artifacts, in 2019 IEEE 22nd International Sym-
posium on Design and Diagnostics of Electronic Circuits Sys-
tems (DDECS) (2019), pp 1–4
53. S. Ashry, T. Ogawa, W. Gomaa, CHARM-deep: continuous
human activity recognition model based on deep neural net-
work using IMU sensors of smartwatch. IEEE Sens. J. 20,
8757–8770 (2020)
54. H.-S. Yeo, W. Feng, M.X. Huang, WATouCH: enabling direct
input on non-touchscreen using smartwatch’s photoplethysmo-
gram and IMU sensor fusion, in Proceedings of the 2020 CHI
Conference on Human Factors in Computing Systems (2020),
pp 1–10
55. G. Acar, O. Ozturk, M.K. Yapici, Wearable graphene nanotex-
tile embedded smart armband for cardiac monitoring, in 2018
IEEE Sensors (IEEE, 2018), pp 1–4
56. C.S. Arun, A. Alexander, Mobile ECG monitoring device
using wearable non contact armband, in 2017 International
Conference on Circuit ,Power and Computing Technologies
(ICCPCT) (2017a), pp 1–4
57. J. Lázaro, N. Reljin, M.-B. Hossain etal., Wearable armband
device for daily life electrocardiogram monitoring. IEEE
Trans. Biomed. Eng. 67, 3464–3473 (2020)
58. B.M. Li, A.C. Mills, T.J. Flewwellin etal., Influence of Arm-
band Form Factors on Wearable ECG Monitoring Performance.
IEEE Sens. J. 21, 11046–11060 (2021)
59. V.P. Rachim, W. Chung, Wearable noncontact armband for
mobile ECG monitoring system. IEEE Trans. Biomed. Circuits
Syst. 10, 1112–1118 (2016). https:// doi. org/ 10. 1109/ TBCAS.
2016. 25195 23
60. A. Rapalis, A. Petrėnas, M. Šimaitytė etal., Towards pulse rate
parametrization during free-living activities using smart wrist-
band. Physiol. Meas. 39, 055007 (2018)
61. A. Villegas, D. McEneaney, O. Escalona, Arm-ECG wireless
sensor system for wearable long-term surveillance of heart
arrhythmias. Electronics 8, 1300 (2019). https:// doi. org/ 10. 3390/
elect ronic s8111 300
62. A.Ç. Seçkin, Multi-sensor glove design and bio-signal data col-
lection. Nat. Appl. Sci. J. 3, 87–93 (2021)
63. D.M. Roberts, M.M. Schade, G.M. Mathew etal., Detecting sleep
using heart rate and motion data from multisensor consumer-
grade wearables, relative to wrist actigraphy and polysomnog-
raphy. Sleep 43, zsaa045 (2020). https:// doi. org/ 10. 1093/ sleep/
zsaa0 45
64. C.E. King, M. Sarrafzadeh, A survey of smartwatches in remote
health monitoring. J. Healthc. Inform. Res. 2, 1–24 (2018)
65. N.J. Wei, B. Dougherty, A. Myers, S.M. Badawy, Using google
glass in surgical settings: systematic review. JMIR Mhealth
Uhealth 6, e54 (2018)
66. H.J. Baek, J. Cho, Development of a ring-type wearable health-
care device. Trans. Korean Inst. Electr. Eng. 67, 892–897 (2018).
https:// doi. org/ 10. 5370/ KIEE. 2018. 67.7. 892
67. Y. Mengüç, Y.-L. Park, E. Martinez-Villalpando, etal., Soft
wearable motion sensing suit for lower limb biomechanics meas-
urements, in 2013 IEEE International Conference on Robotics
and Automation (IEEE, 2013), pp 5309–5316
68. J.T. Muth, D.M. Vogt, R.L. Truby etal., Embedded 3D print-
ing of strain sensors within highly stretchable elastomers. Adv.
Mater. 26, 6307–6312 (2014)
69. A. Atalay, V. Sanchez, O. Atalay etal., Batch fabrication of cus-
tomizable silicone-textile composite capacitive strain sensors for
human motion tracking. Adv. Mater. Technol. 2, 1700136 (2017)
70. X. Zhao, Y. Long, T. Yang etal., Simultaneous high sensitiv-
ity sensing of temperature and humidity with graphene woven
fabrics. ACS Appl. Mater. Interfaces 9, 30171–30176 (2017).
https:// doi. org/ 10. 1021/ acsami. 7b091 84
71. M. Martínez-Estrada, B. Moradi, R. Fernández-Garcia, I. Gil,
Impact of conductive yarns on an embroidery textile moisture
sensor. Sensors 19, 1004 (2019)
72. R. Polanskỳ, R. Soukup, J. Řeboun etal., A novel large-area
embroidered temperature sensor based on an innovative hybrid
resistive thread. Sens. Actuators A 265, 111–119 (2017)
73. J.-H. Yang, H.-S. Cho, J.H. Lee, An analysis on the luminance
efficiency of the machine embroidery method applied to flexible
plastic optical fiber for realization of the textile display. Text.
Res. J. 88, 1466–1478 (2018). https:// doi. org/ 10. 1177/ 00405
17517 703197
74. D. van der Valk, Knitted smart textile sensors: integrating technl-
ogy into garments by using knitting. Master Thesis, Delft Uni-
versity of Technology (2020)
75. A.D. Erol, S. Çetiner, Elektronik Tekstillere Yönelik Akıllı
Kumaş Sensörleri. J. Text Eng. 24, 305–320 (2017). https:// doi.
org/ 10. 7216/ 13007 59920 17241 0810
76. A. Moin, A. Zhou, A. Rahimi etal., A wearable biosensing sys-
tem with in-sensor adaptive machine learning for hand gesture
recognition. Nat. Electron. 4, 54–63 (2021). https:// doi. org/ 10.
1038/ s41928- 020- 00510-8
77. T. Someya, T. Sekitani, S. Iba etal., A large-area, flexible pres-
sure sensor matrix with organic field-effect transistors for arti-
ficial skin applications. Proc. Natl. Acad. Sci. 101, 9966–9970
(2004). https:// doi. org/ 10. 1073/ pnas. 04019 18101
78. T. Yokota, P. Zalar, M. Kaltenbrunner etal., Ultraflexible organic
photonic skin. Science. Advances 2, e1501856 (2016). https://
doi. org/ 10. 1126/ sciadv. 15018 56
79. C.S. Arun, A. Alexander, Mobile ECG monitoring device using
wearable non contact armband, in 2017 International Confer-
ence on Circuit, Power and Computing Technologies (ICCPCT)
(IEEE, 2017b), pp 1–4

Biomedical Materials & Devices
1 3
80. D. Yotha, C. Pidthalek, S. Yimman, S. Niramitmahapanya,
Design and construction of the hypoglycemia monito wireless
system for diabetic, in 2016 9th Biomedical Engineering Inter-
national Conference (BMEiCON) (IEEE, 2016), pp 1–4
81. Q. Zhang, D. Zhou, Deep Arm/Ear-ECG image learning
for highly wearable biometric human identification. Ann.
Biomed. Eng. 46, 122–134 (2018). https:// doi. org/ 10. 1007/
s10439- 017- 1944-z
82. V. Jha, N. Prakash, S. Sagar, Wearable anger-monitoring system.
ICT Express 4, 194–198 (2018)
83. A.R. Khalid, Design of wearable prototype smart wristband for
remote health monitoring using internet of things, in Intelligent
Technologies and Applications: Second International Conference
(INTAP 2019) (Springer Nature, Bahawalpur, Pakistan, 2019), p.
3
84. P. Lukowicz, U. Anliker, J. Ward, etal. AMON: a wearable medi-
cal computer for high risk patients, in Proceedings. Sixth Inter-
national Symposium on Wearable Computers (IEEE, 2002), pp
133–134
85. Y. Fu, J. Liu, System design for wearable blood oxygen saturation
and pulse measurement device. Procedia Manuf. 3, 1187–1194
(2015). https:// doi. org/ 10. 1016/j. promfg. 2015. 07. 197
86. K. Kim, J. Choi, Y. Jeong etal., Highly sensitive and wearable
liquid metal-based pressure sensor for health monitoring appli-
cations: integration of a 3D-printed microbump array with the
microchannel. Adv. Healthc. Mater. 8, 1900978 (2019)
87. P. Escobedo, C.E. Ramos-Lorente, A. Martínez-Olmos etal.,
Wireless wearable wristband for continuous sweat pH monitor-
ing. Sens. Actuators B Chem. 327, 128948 (2021)
88. Y. Tajitsu, Piezoelectret sensor made from an electro-spun fluo-
ropolymer and its use in a wristband for detecting heart-beat sig-
nals. IEEE Trans. Dielectr. Electr. Insul. 22, 1355–1359 (2015)
89. K. Grym, H. Niela-Vilén, E. Ekholm etal., Feasibility of smart
wristbands for continuous monitoring during pregnancy and one
month after birth. BMC Pregnancy Childbirth 19, 1–9 (2019)
90. P. Reali, G. Tacchino, G. Rocco, etal. Heart rate variability from
wearables: a comparative analysis among standard ECG, a smart
shirt and a wristband, in pHealth (2019), pp 128–133
91. Y.M. Roshan, E.J. Park, Design approach for a wireless power
transfer system for wristband wearable devices. IET Power Elec-
tron. 10, 931–937 (2017)
92. X. Yang, X. Wang, X. Li etal., Exploring emerging IoT technolo-
gies in smart health research: a knowledge graph analysis. BMC
Med. Inform. Decis. Mak. 20, 1–12 (2020)
93. H. Zhao, S. Wang, G. Zhou, D. Zhang, Ultigesture: a wristband-
based platform for continuous gesture control in healthcare.
Smart Health 11, 45–65 (2019)
94. B. Pardamean, H. Soeparno, B. Mahesworo etal., Comparing the
accuracy of multiple commercial wearable devices: a method.
Procedia Comput. Sci. 157, 567–572 (2019). https:// doi. org/ 10.
1016/j. procs. 2019. 09. 015
95. M.M. Rodgers, G. Alon, V.M. Pai, R.S. Conroy, Wearable tech-
nologies for active living and rehabilitation: current research
challenges and future opportunities. J. Rehabil. Assist. Technol.
Eng. 6, 2055668319839607 (2019). https:// doi. org/ 10. 1177/
20556 68319 839607
96. Q. Zhou, B. Fang, J. Shan, etal., A survey of the development
of wearable devices, in 2020 5th International Conference on
Advanced Robotics and Mechatronics (ICARM) (2020b), pp
198–203
97. Y.-N. Zheng, Z. Yu, G. Mao etal., A wearable capacitive sen-
sor based on ring/disk-shaped electrode and porous dielectric
for noncontact healthcare monitoring. Global Chall. 4, 1900079
(2020)
98. X. Liang, H. Heidari, R. Dahiya, Wearable capacitive-based
wrist-worn gesture sensing system, in 2017 New Generation of
CAS (NGCAS) (IEEE, 2017), pp 181–184
99. L. Maiolo, F. Maita, A. Castiello, etal. Highly wearable wireless
wristband for monitoring pilot cardiac activity and muscle fine
movements, in 2017 IEEE International Workshop on Metrology
for AeroSpace (MetroAeroSpace) (IEEE, 2017b), pp 271–275
100. R. Booth, P. Goldsmith, Detecting finger gestures with a wrist
worn piezoelectric sensor array. In: 2017 IEEE International
Conference on Systems, Man, and Cybernetics (SMC). (IEEE,
2017), pp 3665–3670
101. A. Oliveira, J. Aguiar, E. Silva, etal. Assessing daily activi-
ties using a PPG sensor embedded in a wristband-type activ-
ity tracker, in Trends and Innovations in Information Systems
and Technologies (Springer International Publishing, 2020), pp
108–119
102. V.D. Corino, R. Laureanti, L. Ferranti etal., Detection of atrial
fibrillation episodes using a wristband device. Physiol. Meas. 38,
787 (2017)
103. H. Cheng-Yu, Z. Ahmed Abro, Z. Yi-Fan, R. Ahmed Lakho, An
FBG-based smart wearable ring fabricated using FDM for moni-
toring body joint motion. J. Ind. Text. 50, 1660–1673 (2021).
https:// doi. org/ 10. 1177/ 15280 83719 870204
104. Y. Zhang, J. Cui, K. Ma etal., A wristband device for detecting
human pulse and motion based on the internet of things. Meas-
urement 163, 108036 (2020)
105. M. Panwar, D. Biswas, H. Bajaj etal., Rehab-net: deep learn-
ing framework for arm movement classification using wearable
sensors for stroke rehabilitation. IEEE Trans. Biomed. Eng. 66,
3026–3037 (2019). https:// doi. org/ 10. 1109/ TBME. 2019. 28999
27
106. X. Gao, Y. Yin, Design on a wearable armband device for assess-
ing the motion function of upper limbs. Comput. Commun. 153,
135–144 (2020)
107. I. Galiana, F.L. Hammond, R.D. Howe, M.B. Popovic, Wear-
able soft robotic device for post-stroke shoulder rehabilitation:
identifying misalignments, in 2012 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IEEE, 2012), pp
317–322
108. S. Shin, M. Kang, J. Jung, Y.T. Kim, Development of miniatur-
ized wearable wristband type surface EMG measurement system
for biometric authentication. Electronics 10, 923 (2021). https://
doi. org/ 10. 3390/ elect ronic s1008 0923
109. S. Kim, J. Kim, S. Ahn, Y. Kim, Finger language recognition
based on ensemble artificial neural network learning using arm-
band EMG sensors. Technol. Health Care 26, 249–258 (2018).
https:// doi. org/ 10. 3233/ THC- 174602
110. S. Said, S. AlKork, T. Beyrouthy, M.F. Abdrabbo, Wearable
bio-sensors bracelet for driveras health emergency detection, in
2017 2nd International Conference on Bio-engineering for Smart
Technologies (BioSMART) (2017), pp 1–4
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