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.

1 INTRODUCTION

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.

https://orcid.org/0000-0002-9034-9921

2 CONTACT SENSORS

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,

Tattoo

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) - Volume 1: BIODEVICES, pages 207-214

ISBN: 978-989-758-398-8; ISSN: 2184-4305

207

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.,

2018).

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

saturation.

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

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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).

3 CONTACTLESS SENSORS

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.,

2015).

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

(lubax.com) 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?

209

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

210

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. (sondehealth.com) is

developing a voice-based technology platform to

monitoring and diagnosing physical health;

BeyondVerbal (beyondverbal.com) is developing

voice-enabled artificial intelligence to create vocal

biomarkers for healthcare screening; VoiceWise

(voicewise.eu) 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).

4 CONCLUSIONS

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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