Are Sensors and Data Processing Paving the Way to Completely
Non-invasive and Not-painful Medical Tests for Widespread
Screening and Diagnosis Purposes?
Giovanni Saggio
Dept. of Electronic Engineering, University of Rome “Tor Vergata”, via del Politecnico 1, Rome, Italy
Keywords: Body Contact Sensors, Body Contactless Sensors, Machine Learning, Diagnosis.
Abstract: Effective medical tests are essential in supporting correct clinical decisions by medical doctors. But, have
medical tests to be necessarily invasive and painful to be effective? During last decades, new developments
of sensors and improvements of data analysis algorithms seem to paying the way to a (more or less near)
future with completely non-invasive and not painful medical tests. This work aims to furnish a survey on what
is going on within this frame, with an eye to new possibilities.
Validated medical tests are essential for medical
doctors’ decision-making processes effectiveness.
Medical tests can be highly, moderately, minimally,
or completely non-invasive. The invasiveness is due
to instruments and energy that physically enter or
interact with the patient’s body, and can be not
painful, relatively painful (e.g. blood sample taking),
painful (e.g. biopsy), and potentially dangerous (e.g.
x-ray radiation exposure).
Of course, ideally we look forward only to
medical tests which are effective, non-invasive and
not painful. In addition, the market demands also
affordability, safety, in-vivo monitoring, etc.
Answers can come from the rapid evolution of
electronics and data processing.
The electronics mainly rely on sensors (such as
inertial measurement units, optical sensors, electronic
nose, etc.), while data processing mainly rely on
pattern recognition (such as Principal Components
Analysis, Cluster Analysis, Support Vector Machine,
Artificial Neural Networks, etc.).
In this work, we aim at investigating the sensors
in supporting completely non-invasive and not-
painful medical diagnosis and screening, underlining
their advantages and their limits. Sensors can be of
two main categories: body contact and body
contactless ones.
Body contact sensors can be touch, clip, bandage,
adhesive patch, tattoo, wearable or a mix of them, for
a short-term or a long-term usage, and needle-free to
avoid pain and discomfort.
2.1 Touch, Clip, Bandage, Patch,
Let us start considering body contact sensors for
diabetes, which represents a global challenge disease
for more than 400 million people worldwide, and
requires as-frequently-as possible checks of blood
sugar levels. Current clinical/personal practice to
measure glycaemia is mainly by the discomfort finger
pricking. Conveniently, new non-invasive techniques
are ongoing based on touch, patch and clip adopting
solutions. An example comes from the DMT (by
DiaMonTech, Germany), which detects glucose
molecules by using a mid-infrared scanning of the
interstitial skin fluids. The shoebox-sized version has
the same accuracy as tests strips in preclinical tests, a
pocket-sized version will be available at the end of
2020, and a watch-like device will be presumably
available in 2024. GlucoWise™ (by MediWise, UK),
an under-developing non-invasive glucose monitor
solution, is based on low-power high-frequency radio
waves transmission through a thin body part (between
Saggio, G.
Are Sensors and Data Processing Paving the Way to Completely Non-invasive and Not-painful Medical Tests for Widespread Screening and Diagnosis Purposes?.
DOI: 10.5220/0009098002070214
In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020), pages 207-214
ISBN: 978-989-758-398-8
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
the thumb and forefinger, or earlobe). FreeStyle Libre
(by Abbott Diabetes Care, UK) is a sensor patch
useful to measures glucose levels in the interstitial
fluid between the cells under the subject’s skin.
GlucoTrack (by Integrity Applications, Israel) is a
blood sugar level sensor used with an ear clip, based
on a combination of ultrasonic, electromagnetic and
thermal waves.
Body contact-skin sensors can measure different
bio-parameters, such as pressure, heart-pulse,
respiration-quality, sweat and local body part
temperature too. As an example, a touch-type system
named SensoSCAN® (by Sensogram, USA) has a
built-in alerting system triggering blood pressure,
heart rate, and oxygen saturation. A Kapton-based
flexible sensor can be stuck over the face skin for
monitoring inhalations/exhalations moistures, aimed
at evidencing breath anomalies (Caccami et al.,
We can continuously measure pH-values from the
sweat by a tattoo potentiometric sensor (Dang et al.,
2018), or sodium concentration by a sensor belt
(Schazmann et al., 2010). Chloride amount measured
by an adhesive patch sensor can lead to an early cystic
fibrosis diagnosis (Gonzalo-Ruiz et al., 2009).
New adhesive patches provide a continuous and
point-to-point body temperature mapping by means
of a radio-frequency identification (RFID) module,
thanks to a loop antenna and a transponder that
change their electromagnetic performance according
to the local skin temperature (Miozzi et al., 2017).
From an unusual point of view, we can consider
the smartphone as a sensor, for touch and “wearable”
passive health sensing (Cornet and Holden, 2018).
The digital phenotyping with the smartphone can be
used, for instance, to define mental health behavioural
patterns, which can be analysed with the purpose to
enhancing behaviour and mental health (Onnela and
Rauch, 2016).
2.2 Wearables
Wearable electronic sensors (wearables, hereafter)
can include sensor(s) embedded in wristbands,
headband/headwear, necklaces, gloves, rings and
bracelets, chest belts, stretchable clothing, elastic
bands, kneepads and socks, all complying with the
natural gestures and motions of the wearer.
Some well-known sensory wristbands, such as
Fitbit Flex, Mi Band, Garmin Vivoactive, for
instance, and are useful for activity recognition, step
detection, and distance estimation.
The sensory headwear (Piscitelli et al., 2019) can
monitor neck motion handicaps and help in
evaluating neck functionality rehabilitation.
Equipped with sensors, the sensory glove can
measure fingers’ movements (Saggio and Bizzarri,
2014). The sensors can be of different types,
including optical fibers (Wise et al., 1990), Hall-
effect based sensors (Portillo-Rodriguez et al., 2007),
inertial measurement units (IMUs) (G. Saggio et al.,
1995) (Hsiao et al., 2015), piezoelectric (Cha et al.,
2017), stretch (Sbernini et al., 2016), and resistive
flex sensors (Saggio et al., 2016). The sensory glove
has been adopted for the analysis of hand tremor in
Parkinson’s disease patients (Cavallo et al., 2013), for
determining the fingers’ range-of-motion and the
fingers’ deformity of arthritic patients (Condell et al.,
2011), for measuring handgrip capabilities (Grandez
et al., 2010), and for assessing rehabilitation in
surgery patients (O’Flynn et al., 2013). Moreover, the
sensory glove has been useful in evaluating hand
movement capabilities (Hsiao et al., 2015) (Saggio et
al., 2015), finger muscle therapy effectiveness
(Hidayat et al., 2015), functional recovery
improvements after stroke (Merians et al., 2006), and
hand rehabilitation after traumas (Hsiao et al., 2015).
SensoRing® (by Sensogram, USA) is a ring with
built-in biosensors and wireless connectivity, to
measure (among others) blood pressure, heart rate,
respiration rate, perfusion index, and oxygen
The stretchable sensory clothing can non-
invasively measure the 3D trunk movements for
biomedical applications (Saggio and Sbernini, 2011)
with an accuracy of the order of one degree
(Mokhlespour et al., 2017).
The sensory elastic band system equipped with
IMUs can be located in whichever human body
segments. This is to evaluate postural deficit in
vestibular failure (Alessandrini et al., 2017), enhance
body standing balance recovery (Costantini et al.,
2018), determine children’s motor impairments
(Ricci et al., 2019a), assess dyskinesia (Ricci et al.,
2018) and transcranial direct current stimulation
effectiveness (Ricci et al., 2019b) in Parkinson’s
disease patients, and gait harmony during walking
(Gnucci et al., 2018).
The sensory kneepad (Saggio et al., 2014)
furnishes useful data of knee motion capabilities to
trace on-going patients’ motor rehabilitation.
The Sensory Socks (by SensoRia, USA) can
provide ongoing monitoring of plantar pressure in
diabetic foot complications, so to early evidence
diabetic foot ulcers, aiming at reducing part of the
over 15 million of amputations in the world.
BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices
Gyrocardiography (GCG) is a new term coined
for recordings of electrocardiography (ECG) based
on heart motion assessment through a gyroscope.
This allows obtaining reliable information on systolic
and diastolic time intervals (Jafari et al., 2017).
As body contactless sensors, we refer to proximity
sensors and interacting with body’s fluids sensors.
3.1 Image Processing
Image acquisition and processing has been allowing
the development of non-contact and non-invasive
devices, for the evaluation of different health statuses
and the assessment of different clinical conditions.
We can start mentioning a work devoted to 2D
image acquisition for monitoring and evaluating
sleeping behavioural patterns (Papakostas et al.,
Imagine techniques, such as hyperspectral, plantar
and photographic ones, and data analysis, allow
detection of early developing feet and legs’ ulcers
(Toledo et al., 2014).
The image gathered by a webcam in front of a
subject during typing can be useful to extract
physiological signs of face-skin colour changes to
determine the heart rate (Ariyanti et al., 2016).
Heart and respiratory rates can be measured by
time-lapse imaging acquired from a camera, and data
processing of the images can result with rates with an
accuracy higher than 90% (Takano and Ohta, 2007).
Imaging methods were usefully exploited to
evidence the melanin pigment concentration
distribution map of a specific area of the subjects’
skin (Stamatas et al., 2004). Malignant melanoma can
be detected by smartphone-captured images: Lubax
( send pictures to a data lake for visual
inspection of dermatologists; an automatic solution is
based on algorithms for evaluation of colour variation
and border irregularity (Thanh-Toan et al., 2014).
Chronic fatigue syndrome can be revealed, 98%
in accuracy, by means of hybrid facial features
gathered from face pictures acquired by a camera
(Chen et al., 2015).
A non-invasive detection modality for breast
tumour relies on thermography, able to reveal heat
patterns and blood flow in tissues (Ng, 2009).
Camera-smartphone based picture acquisitions
can estimate wounds conditions considering sizes
and tissue classifications. Apps related to the wound
size are Wound Tracker, Wound Analysis, and
WoundMAP. Apps related to the assessment of
wound conditions are WoundMAP, MOWA, Wound
Analyzer, and AWAMS.
Elaboration of data gathered from digital photos
were used to quantifying conjunctival pallor useful as
screening test for anaemia (Collings et al., 2016)
The image processing can be related not only to
visible light-waves, but to microwaves too. So, a non-
invasive microwave head imaging system was
adopted to detect and localize intracranial
haemorrhage (Mobashsher et al., 2016). Microwave
Doppler radar images were useful for rapid detection
of fall events, so to alarm for interventions (Mercuri
et al., 2013).
3.2 e-nose and e-tongue
As bio-inspired sensors, the electronic nose (e-nose)
and the electronic tongue (e-tongue) sense the aroma
and the taste of different compounds. When those
compounds are related to human, e-nose and e-tongue
sensing combined with pattern recognition have been
used to assess pathologies.
Human skin emanations (odour, sweat) and
excreted materials (breath, saliva, urine, seminal
fluids, faeces), are the result of complex volatile
organic compounds (VOCs), which offer unique
insights into ongoing biochemical processes. VOCs
can be successfully analysed through spectroscopy,
chromatography and spectrometry, such as the gas
chromatography-mass spectrometry (gold reference),
the proton transfer reaction-mass spectrometry, the
selected ion flow tube-mass spectrometry, the ion
mobility spectrometry, and the laser spectrometry.
Inconveniently, those techniques are quite expensive,
time-consuming, cumbersome, and requires
specialized personnel, so that cannot represent
widespread procedures. Conversely, the e-nose joints
the non-invasive approach to an easy handling, low-
cost, rapid and mass procedure, well suited for its
high sensitivity, specificity, repeatability and
reproducibility (Wojnowski et al., 2019). The term e-
nose, coined in 1994 (Gardner and Bartlett, 1994),
refers to an array or a matrix of sensors, individually
sensitive to different VOCs thus providing multiple
detection, for a sort of “smell-signature”. The e-nose
can be made using different approaches, surface
acoustic wave (SAW) (Wang et al., 2008), chemi-
resistor (Peng et al., 2009), organically functionalized
gold nanoparticles (GNPs) (Peng et al., 2010), and
quartz microbalances (D’Amico et al., 2010), among
others. Then, pattern recognition algorithms relate the
“smell-signature” to a particular pathology.
Are Sensors and Data Processing Paving the Way to Completely Non-invasive and Not-painful Medical Tests for Widespread Screening and
Diagnosis Purposes?
Some commercially available e-noses are (Fig. 1)
the Cyranose® 320 (by Sensigent LLC, USA), the
Aeonose™ (by The eNose Company, The
Netherlands), the PEN (Portable Electronic Nose, by
Airsense Analytics, Germany), the Lonestar VOC
Analyzer (by Owlstone, UK), the zNose® (by
Electronic Sensor Technology, USA).
(a) (b) (c)
Figure 1: (a) Cyranose® 320 by Sensigent LLC; (b)
Aeonose™ by The eNose Company; (c) zNose by
Electronic Sensor Technilogy Inc. Pictures are reprinted
with kind permissions.
The first work reporting breath analysis dates
1972 by the double Nobel Prize winner Linus
Pauling. Since then, the e-nose applied to the exhaled
breath has been discriminating a number of
pathologies. We can start mentioning the lung cancer,
which causes more than 1 million deaths per year
worldwide (Saalberg and Wolff, 2016), invasively
revealed by bronchoscopy or by spectrometry, with
the aforementioned drawbacks. The usage of the e-
nose allows discriminating 90% of patients from
controls (Dragonieri et al., 2009), a classification
accuracy as high as 80% (McWilliams et al., 2015), a
91% of specificity, and a sensitivity up to 92.8%
(D’Amico et al., 2010). Other e-nose applications
about tumour revelations were breast cancer (Peng et
al., 2010), skin cancer (Kwak et al., 2013), thyroid
cancer (Guo et al., 2015), ovarian cancer (Amal et al.,
2015), head-and-neck cancer (Hakim et al., 2011),
and bronchogenic carcinoma (Machado et al., 2005).
Colorectal cancer is a leading cause of cancer
death worldwide. Current gold standard test method
is the colonoscopy, but it is time consuming,
expensive and does not allow mass screening.
Another method is the faecal immunochemical blood
testing, but presents a high variation in sensitivity.
The e-nose was successfully adopted to reveal VOC
content of urine obtaining 78% of sensitivity
(Westenbrink et al., 2015), and promising results are
reported in a work reviewing analysis of VOC in the
faecal headspace (Di Lena et al., 2016). In addition,
e-nose has been successfully adopted for revealing
fungal infections (Acharige et al., 2018), tuberculosis
(Saktiawati et al., 2019), sclerosis multiplex (Ionescu
et al., 2011), allergic rhinitis (Saidi et al., 2015), and
wound odour quantification (Akhmetova et al., 2016).
The e-tongue operates in liquid mediums to
recognize a particular sample tasting it, similarly as it
occurs for the human taste buds. The first work
reporting a sensor matrix in liquid media dates 1985
(Otto and Thomas, 1985). Currently, the e-tongue is
mainly used in food industry for determining types,
quality, and freshness of olive, apples, spices, sauces,
honeys, water, wine, vinegar, tea, milk, oil, etc. More
rarely, the e-tongue is used for healthcare, for
obtaining the “taste fingerprint” of urine, or for
assessing prostate cancer “sensing” prostatic or
seminal fluids (Bax et al., 2018), or for the evaluation
of saliva metabolome for providing a sort of measure
of stress and anxiety (Fitzgerald and Fenniri, 2017).
3.3 Voice
It has been largely demonstrated that, if we purge the
voice sound from emotions, confidence and feelings,
what we get are parameters linked to the health
conditions of the speaker. The voice production
depends on four main parts: the lungs that provide air
with energy content; the vocal chords that produce
sound vibrating accordingly to the amount of air; the
cavities (mouth, nose, chest, ear) that produce
resonations; the articulators (lips, tongue, teeth) that
shape the sound. In turn, these parts depend on the
brain that coordinates. When one or more of these
parts are subjected to alterations or infections, the
resulting disease affects the voice production system
to a significant and measurable extent.
We can report how voice features were correlated
to upper respiratory diseases (Bothe, 2017), lung
tuberculosis (Saggio and Bothe, 2016) and chronic
obstructive pulmonary disease (Mohamed et al.,
2014). Benign thyroid disease (Pernambuco et al.,
2015) and level of asthma (Walia and Sharma, 2016)
were found to be related to some voice parameters.
For brain related diseases, data analysis of the
voice can lead to around 90% in accuracy for
Parkinson’s disease in early stages (Bocklet et al.,
2011) and in overt conditions too (Jeancolas et al.,
2017). Vocal parameters can be translated into
markers of Alzheimer's disease (Meilan et al., 2018),
and bipolar disease (Guidi et al., 2015).
Some voice features were related to diabetes
(Chitkara and Sharma, 2016), and some others
exceeded 97% of correlation with blood pressure
values (Sakai, 2015). From speech analysis, it was
observed the presence and severity of amyotrophic
lateral sclerosis with an accuracy of 92% in (Suhas et
al., 2019). By reflecting the loss of articulatory
BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices
processing, children with Down syndrome speak with
less distinction between vowels with respect to
individuals without (Moura et al., 2008). The work
(Alves et al, 2019) reviews at which extent the
dehydration conditions affect the voice performances.
According to (Manfredi et al., 2017), in a near
future, the possibilities of early detection of an
amount of pathologies via voice analysis can be
obtained directly via smartphones’ microphones,
leading to new tele-health-check possibilities.
Currently, Sonde Health Inc. ( is
developing a voice-based technology platform to
monitoring and diagnosing physical health;
BeyondVerbal ( is developing
voice-enabled artificial intelligence to create vocal
biomarkers for healthcare screening; VoiceWise
( processes voice samples by means of
machine learning algorithms for medical diagnosis
and health screening purposes.
Apart from the voice, the “sound” of the breath
furnishes elements too. SpiroSmart is a smartphone
app by which the user has to forceful exhaling the
breath in the direction of the phone’s microphone.
Audio data are analysed to calculate the exhaled flow
rate, with a mean error of 5.1% in comparison to
measure of lung functionality (Larson et al., 2013).
Data gathered by body contact and body contactless
sensors represent a more and more evolving tool for
non-invasive and not-painful medical tests. Data
analysis by means of machine learning algorithms
furnish a new paradigm for personalized medicine.
Since it is not possible to represent all the
pathologies and because more and more possibilities
enhance rapidly, this work cannot represent the entire
picture of the status-of-art of the completely non-
invasive medical tests, however a meaningful survey
was provided underlying the profitable aspects.
Nevertheless, the advantages have to be balanced
by issues due to confounding factors due to different
physiological aspects such as gender, age-range,
ethnicity, smoke-habits, diet, motor exercises, sleep
habits, taking medication, comorbidities, pregnancy,
etc. Considering the e-nose applications, for instance,
men have higher isoprene levels in breath with
respect to women (Lechner et al., 2006). Volatile
alkanes contents of the human breath (Phillips et al.,
2000) and lung cancer breath-print (Bikov et al.,
2014) differ for different age, as well as exhaled air
of healthy subjects with asthma depends on age
(Dragonieri et al., 2007). A gluten-free diet changes
the values of 12 volatile compounds excreted in
exhaled breath (Baranska et al., 2013). The exhaled
pentane levels differs after sleep in patients with
obstructive sleep apnoea (Olopade et al., 1997). The
tobacco smoking alters the breath VOC profile
(Gordon et al., 2002). Physiological hormonal
changes due to the ovarian cycle can alter the exhaled
VOCs (Dragonieri et al., 2018).
All considered, to date, data acquisition by
sensors and data analysis by machine learning
algorithms represent a new frontier for non-invasive
not-painful but accurate disease screening and
diagnosis, with all the credentials to become routinely
applied in medical practice.
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