Contactless Physiology Radars to Promote Healthy Ageing

via Remote Tracking: The Need for IoT Context

Miquel Alfaras

, Claus Vogelmeier

, Nurlan Dauletbaev

2,3,4 c

and Zouhair Haddi

NVISION Systems and Technologies, Gran Via Carles III, 124, ent.1a, Barcelona, Spain

Department of Internal, Respiratory and Critical Care Medicine, Philipps-Universität Marburg,

(Member of the German Center for Lung Research, DZL) Biegenstraße 10, Marburg, Germany

Department of Pediatrics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada

al-Farabi Kazakh National University, al-Farabi Avenue 71, Almaty, Kazakhstan

clausfranz.vogelmeier@uk-gm.de, nurlan.dauletbayev@uni-marburg.de

Keywords: Physiology, Contactless, Older Adults, Active and Assisted Living Technologies, Internet of Things,

Biosignals, Remote Digital Health, Radar, Machine Learning.

Abstract: In recent years, the use of high frequency radars to promote the contactless monitoring of physiology

parameters of cardiorespiratory nature has seen an impressive progress pushing for the creation of clinical

experiences beyond the telecommunications engineering field. In this position paper, we argue in favour of

combining the expertise of IoT and smart home research that paved the way for remote digital health in the

last decade, to foster a paradigm shift in healthy ageing an independent living promotion by means of

disruptive support technology that may overcome privacy invasiveness, discomfort, and usability issues.

1 INTRODUCTION

The promise of contactless physiology sensing for

health has been around for years. With an interest that

was revamped by the efforts of research teams around

the world, the private sector is starting to pave the

way for a productive market niche that is called to fill

an existing gap no other technology covers smoothly.

Radio Detection and Ranging (in other words,

RADAR) has been a long understood technology,

with industrial success in areas as vast as marine and

air navigation, military/defence, law enforcement,

autonomous driving or even in civil engineering. It is

only under the research initiatives of teams such as

Dina Katabi’s and their approach to wireless signals

that use cases for physiology metrics have been

starting to see the light in the last 8-9 years (Adib et

al., 2015). In fact, due to the nature of the technology,

counting on sensors that give insight on position and

peed is clearly aligned for the development of gait

assessment studies throughout the world (Gambi et

https://orcid.org/0000-0002-8942-5843

https://orcid.org/0000-0002-9798-2527

https://orcid.org/0000-0002-7114-1041

https://orcid.org/0000-0001-9835-4795

al., 2020; Abedi et al, 2022), of interest not only to

euromotor disorders, but also to a bigger frailty

perspective. When entering cardiorespiratory metrics,

an even more disruptive perspective beyond

movement was established, since contactless

physiology by means of radar is called to offer

significant cost reductions, use case adaptation and

signal quality features which could coexist with

ubiquitous health wearables established in the last

decade. Back in 2016, and focusing on contactless

only, reviews like that of Naziyok, in Germany

(Naziyok et al., 2016), started to highlight how radar

technology began to emerge as a consistent

competitor against other technologies such as

ballistocardiography (BCG), depicting pilots and

proofs-of-concept taking place in clinical domains

out of the lab. A transition to radar-based spin-offs

and successful use cases built for pathologies such as

Parkinson’s suggest a promising landscape (Kabelac

et al., 2019), for a far more versatile technology than

its vibrational-based counterparts. Works such as

Alfaras, M., Vogelmeier, C., Dauletbaev, N. and Haddi, Z.

Contactless Physiology Radars to Promote Healthy Ageing via Remote Tracking: The Need for IoT Context.

DOI: 10.5220/0012705500003699

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2024), pages 243-249

ISBN: 978-989-758-700-9; ISSN: 2184-4984

243

millimiter wave approach of (Antolinos et al., 2020)

set a general comparison between several contactless

physiology sensing technologies, covering not only

BCG, but also video-based recording and thermal-

based technology among others. Growth experienced

by the radar business has been also notable in the in-

cabin monitoring for the automotive field,

transitioning from lab experiments (Lázaro et al.,

2021) to full datasets sponsored by key automotive

industries (Yoo et al.n 2021) such as Hyundai. For

vital signs, the link between biomedical engineering

R&D groups capable of processing the data and

clinical assessment teams at healthcare institutions

narrowing down use cases applies more than ever

before, with sleep health and pneumology offering a

productive landscape for the years to come (Yen et

al., 2022). Respiratory health, sleep health

assessment and datasets set up for palliative care use

cases (Schellenberger et al., 2020; Michler et al.,

2019) are only a few examples of the possibilities that

radar physiology will bring in the short term. Current

reviews already illustrate engineering efforts

incursion into the clinial domain, underlining the

different frequencies and operation modalities even

within the field of radiofrequency sensing for

physiology (Paterniani et al., 2023), reinforcing the

message that contactless physiology radars are now

explored hand in hand with medical specialties

experts. In this paper, we build the case for radar

physiology that could suit healthy ageing paradigms.

In this paper, we illustrate physiology metrics that are

a reality in our contactless physiology approach,

already integrated into our IoT cloud services, and

outline how contactless physiology may support

clinical supervision of relevance to the older adult

user.

2 BACKGROUND

A physiology radar is a device that utilises the

transmission and detection of radio waves emitted

towards a person, inferring vital signs from the

reflected waves. Range, speed, and ultimately,

physiology waves are inferred by deploying one of

the main existing processing strategies available in

radar sensing: a) Impulse-Radio Ultra Wideband (IR-

UWB), b) Doppler Continuous Wavelength (CW)

and Frequency Modulated Continuous Wavelength

(FMCW). The recent review of Paterniani’s team

(Paterniani et al., 2023) illustrates how all these

paradigms, with their strengths and drawbacks, have

successfully started to implement use cases of interest

to the clinical domain.

The market shift experienced within electronics

since the 2000s, where chipsets, components and

electronic modules have steadily experienced

reduced mass production costs and dimensions, is no

exception to the radar domain. In practical terms, this

has meant that radar transmitters and receivers

(antennas) prepared for different operation regimes

have undergone a transformation that today feeds off-

the-shelf consumables ready for research exploration

with versatile characteristics (radar modality,

frequency of operation, bandwidth). Such a growth

has spurred lab efforts for long out of reach due to

high costs. Sensors (radars) are nowadays eligible for

customised component adaptation, use case scenario

definition, and important work on the underlying

algorithms to denoise, filter and convert demodulated

high frequency radar waves into actionable

physiology signals that can be fed into healthcare

monitoring pipelines.

In parallel, the advent of IoT cloud smart home

settings and businesses thriving in the domain of

remote health is a reality established to a point never

foreseen before the recent COVID-19 pandemic. IoT

services that are nowadays offered in the fields of

efficient energy management, domotics and smart

homes have steadily built a case for remote health that

is no longer science fiction, as seen in examples with

gait analysis, indoor position for ambulatory pattern

characterisation, sound and magnetic-based indoor

location (Guimaraes et al., 2016), gamified remote

physiotherapy, and connected telemedicine metrics

or smart home data to analyse health decline taken at

home (Alberdi Aramendi et al., 2018) , to name a few.

Undoubtedly, the ubiquitousness of mobile phones in

our society is also to blame, since the fact that

virtually any person can carry a powerful tiny

computer, anytime, within his or her pocket, has

redefined how technologies may support data

collection, data transmission, information access,

interpretation, health self-awareness and engagement

or ownership of the processes ingrained in the so

called 4P medicine (predictive, preventive,

personalised and participative). In essence, what an

IoT sensor infrastructure at home offers, is a means

of getting to monitor contextual information of

particular interest. And this is, of course, a monitoring

infrastructure that goes hand in hand with cloud

services and communication devices that enable the

storage, integration and processing of the remote

sensor’s data. Countless examples of IoT ecosystems

for Digital Health exist already in behavioural

metrics, mobile-supported health supervision and

ambient intelligence with or without the integration

of wearable sensors.

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The demographic challenge continues to be

present today in Europe and worldwide. Life

expectancy has significantly increased in recent

years, and it is a trend still observed, posing the need

to strengthen societies in which people not only live

longer, but more independently, inclusive and

prepared to cope with the health deterioration and

comorbidities that accompany the process of ageing.

The clear push obtained within projects under the

European Active and Assisted Living (AAL)

framework in the form of outcomes on tech

development has significantly interconnected the

private and public sectors, to which remote wearable

health played a key role in different implementations.

However, when it comes to technologies to support

Healthy Ageing, while the promise of gadgets

unlocking physiology monitoring insight is

remarkable, currently existing paradigms pose certain

concerns of usability, privacy-invasiveness and

activity disruption, long identified in research

literature.

2.1 Radar Principles

Conceptually speaking, any contactless radar

deployment can operate easily leveraging any

preexisting remote health sensor infrastructure

counting on an IoT cloud, communication protocols,

and a processing pipeline that can elaborate on raw

data to offer clinical insight by means of apps,

dashboards, and assessment interfaces. Figure 1

depicts a simple 3-step dataflow (RADAR

physiology, IoT cloud and Digital Health platforms

or user interfaces).

Figure 1: Radar physiology sensing (data flow depiction).

But in order for this to happen, radars need to be

understood. In general terms, radar physiology

principles, especially under the successful FMCW

approach, can be characterised in the following

manner:

1. High frequency waves are emitted from the

radar toward the targe subject, with an

intrinsic frequency and modulation pattern

defined at emission. The electromagnetic

wave is a complex signal that encodes

information in the form of an amplitude and a

phase

2. Signals that reach the target subject are

reflected back, establishing a dependency

between original waves and any changes in the

movement of the target. In particular,

physiology research will address the

dependency between radar signals and the

thoracic (or cardiothoracic) cavity

movements.

3. Analysis of the received waves combines

Doppler effect principles to extract range

(distance) and changes (speed) with complex

radiofrequency wave demodulation of the in-

phase (I) and quadrature-phase (Q)

components to infer changes with respect to

the emitted signal

4. Advanced spectral analysis techniques and

filtering proceed to tell apart cardiac,

respiratory signals and movement or noise

artifacts.

Figure 2 shows a representation of such signals

where inhale/exhale breathing cycles are captured

alongside cardiac activity.

Figure 2: Depiction of radar (phase) signals encoding

physiology data along time. Own work, inspired by (Adib

et al., 2015).

While the process seems overly simplistic,

antenna design, directionality, the control of

parameters such as field of view (FOV), maximum

range, frequency modulation paradigms, signal

filtering, multiple-input multiple-output for an

improved performance and spectral analysis are

engineering science fields in themselves.

In the FMCW characterisation outlined, for

instance, multiple reflections off target come into

play, posing the challenge of filtering those that come

from the target of interest even when at rest or in the

absence of any movement.

Contactless Physiology Radars to Promote Healthy Ageing via Remote Tracking: The Need for IoT Context

245

2.2 The Need for Context: IoT

To this date, the so claimed smart radars for

contactless physiology are not smart. In this work, we

argue in favour of the use of IoT sensors to make

contactless radar-based physiology a reality. Our

rationale does not try to invalidate any of the progress

done with embedded computation within radar host

boards, since computing at the edge (close to where

the sensors are, as opposed to cloud) is called to

transform the future of remote sensing by optimising

the use of computing or networking resources while

lowering the environmental impact of cloud

computing, often overlooked. On the contrary, our

perspective is that of leveraging the vast expertise in

IoT for remote tracking to provide contextual

information which could render radiofrequency

sensors for physiology smart. If so, such a challenge

calls for the coupling of radar technology with IoT

sensors that can already inform about subject’s

activities, subject’s position, presence of other

individuals in range, behavioural metrics, or

environmental conditions at measurement time.

2.2.1 IoT Examples

IoT encompasses a range of technologies that have

been around for more than two decades now. Some

examples illustrate how sensors deployed in a remote

environment complement existing tracking

paradigms. On the one hand, IoT could be in charge

of enabling communication between sensors and

processing infrastructure. On the other hand, IoT

facilitates an interface to which engage with the

tracking, or the resulting assessment. More

interesting, though, is the use of IoT sensors to add

context. Context supports such health tracking

paradigms offering information of relevance to the

core metric clinically targeted.

This is the case, for instance, of (Pang et al., 2023)

who in the context of COVID-19 were able to

combine wearables for cough monitoring with IoT

sensors to add an environmental dimension to the

assessment.

Other examples in chronic respiratory issues

exemplify how the IoT, either as seen statically in a

house or as a portable concept relying on mobile

phones and wearable devices (Escalona et al., 2023)

offer novel perspectives such as personalised asthma

symptom progression alongside individual exposure

to low air quality.

3 USE CASE SCENARIOS

Our work in the framework of the Tecniospring

UPTAKE project, has made progress in the following

lines.

First, a protocol for the collection of a radar-based

physiology dataset alongside reference physiology

signals and a predefined set of actions measured by

IoT sensors was established. This was done in

collaboration with the Department of Computing

Engineering of a University campus, recording

sessions that lasted 20-30min each, in what we called

a smart office (contact sensors in doors, windows,

environmental metrics). Ongoing work aims at the

open access publication of the dataset and protocol

description to enable contactless physiology data

quality assessment (to be published 2024). Labelled

datasets promote the use of machine learning

techniques to improve signal filtering, detect context

or operation regimes, so that processing can be

optimised to the activity, position or movement being

performed by the subject.

Figure 3: Smart office environment for contactless

physiology acquisition and IoT activity tracking.

In turn, the usage of complementary sensors (with

data that could also be made openly accessible)

enables the understanding of how context awareness

fosters efficient radar measurement and subsequent

processing. How data from different sources (radar

and IoT) needs to be combined is not yet entirely

clear. However, the understanding of context

variables such as where the target subject is located,

whether he or she is standing or resting, and whether

there is activity with profuse movement, offers

valuable input. This input guides how different

processing algorithms should operate to optimally

retrieve the physiology data, or whether certain data

entries should be discarded altogether. IoT gateways

(i.e. devices that connect multiple IoT modules or

sensors) are capable of establishing a single access

point where data would count on synced clocks.

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Those data points can therefore be used in rules and

logic for processing dataflows, as triggers to activate

different sensors around the smart home context or

even with more in-depth analysis instructions that

would establish a link between an activity and a

monitoring regime.

The idea that multiple low-cost radar sensors can

accompany a smart environment (house, office, or

clinic) to characterise different situations and rooms

must progress in line with IoT support metrics that

will avoid noise, irrelevant data capture or even

inefficient operation regimes deployed continuously

on places without relevant target or action taking

place. From a smart office, it is easy to start

approaching protocols that would turn a doctor’s

office into a smart one equipped with intelligent

triage taking place in a seamless way, or adding

objective physiology metrics to the routine visit

acquired while in a sensor-equipped waiting room,

optimising time and resources. With validation only

seen as a first step beyond sensor manufacturer’s

claims, the potential of contactless physiology for

healthy ageing becomes only tangible when outlining

use cases tackling a specific pathology (or serving the

needs of given medical specialties).

Two of them are illustrated in the following

subsections.

3.1 Medical Equipment Supporting

Physiotherapy Sessions with

Contactless Physiology

Neuromotor disorders with higher incidence in

advanced ages, fractures compromising mobility,

traumatic brain injuries (not necessarily narrowed

down to the older adult) and post-surgery procedures

leading to prolonged hospitalisation, are paradigms

where rehabilitation plays already a crucial role in

the recovery of affected patients within the clinical

institutions.

Digital health has successfully demonstrated

success cases in which technology supports the

rehabilitation processes. This comes, for instance, in

the form of assessment metrics and logs throughout

the process, engaging platforms to distribute guidance

materials or even prescribe and monitor exercises,

gamification assets to spur adherence to treatment or

even visual based performance metrics on exercise

compliance. Physiology, however, is usually not

embedded in the process due to the need to allocate

extra resources to a dimension that may increase

protocol complexity and assessment. While stress

tests, exercise metrics or routine lung function

measurements or electrocardiography are resource-

consuming tests only applied upon request.

Figure 4: Smart medical equipment (bed) provided with

radar physiology sensors for hassle-free biosignal

acquisition. IoT sensors (pressure mat and contextual

metrics on the room) complement the data.

If radar physiology sensors demonstrate the

fulfilment of high data quality standards comparable

to gold standard medical equipment, the embedding

of radar sensors to medical beds (medical equipment)

offers a promising access to a physiological

dimension that takes place seamlessly for the patient.

All of a sudden, the clinician can access a range of

data that delivers cardiorespiratory insight, enabling

to establish baseline measurements and pre/post

exercise assessment that may unveil recovery

progress or underlying issues such as bad sleep health

or respiratory function concerns.

3.2 A Radar-Equipped Smart Home to

Support Independent Living

A smart home is a residence that utilises technology

(sensors, actuator and network infrastructure) to

enable a set of control and action features over house

facilities or services. For instance, energy efficiency

and consumption management, smart curtains and

blinds, surveillance technology, intelligent water

supply or consumer electronics equipped with

monitored usage (fridge, water dispenser) are market

services that have flourished under the domain of

smart homes. In turn, from a research perspective,

more disruptive IoT sensing approaches are tackling

activity recognition which could contribute to a better

understanding of behavioural metrics. When

approaching remote physiology metrics for digital

health, radiofrequency sensing technology embedded

in furniture, walls or ceiling emerges as a great step

forward.

Contactless Physiology Radars to Promote Healthy Ageing via Remote Tracking: The Need for IoT Context

247

Figure 5: Smart home IoT environment where IoT sensors

(blue) and contactless physiology radiofrequency

monitoring (orange) devices coexist to enhance context

awareness and cover a full picture of remote health tracking

at home.

This technology offers sensors that would enrich

existing behavioural monitoring paradigms,

complement wearable-based studies already tackling

biosignals or, even better, overcome some of the

limitations identified in established technologies. In

other words, the contactless hassle-free viewpoint of

physiology radars, could end up superseding

audiovisual behavioural surveillance in terms of

privacy-invasiveness, or overcoming concerns raised

by wearable usage on discomfort, daily activity

disruption and device charge/discharge routines, both

contributing to end user engagement, usability and

adherence. In Figure 5, a smart home schematic is

shown in which multiple radar physiology sensors

(orange) are deployed throughout various dwelling

spaces, while they coexist with IoT sensors offering

action context or activity metrics.

4 CONCLUSIONS

This position paper aimed to illustrate how our

current research work on contactless radars for

physiology can pave the way for healthy ageing.

Discussion within our partner healthcare networks

unveiled straightforward applications of radar

physiology that are intrinsic to the contactless nature

of the proposed sensors, e.g. for neonatal intensive

care units, burn units and wards, and first responder

emergencies. In the face of a significant range of new

businesses emerging in the radiofrequency sensing

domain, unresolved issues on measuring technique,

filtering procedures and hardware design constitute

main research topics where progress is called to set

the future of the sensing paradigm in the following

years. A trend of significant cost reduction and

promising results have helped radars secure their

share of Digital Health technology solutions research.

The fact that people are nowadays surrounded by

WiFi and 5G radiofrequency communication

antennas (2.4GHz, 5GHz frequencies respectively)

have built the case for an unexplored remote sensing

opportunity working at operating regimes that pose

no health concern to the user. Those are devices that

have made it into our homes, not necessarily posing

significant maintenance or setup concerns other than

first-time connectivity service arranged by telecom

service providers. Continuous exposure to

radiofrequency power is not to be taken lightly,

though, when a shift of paradigm is implied going

from broad internet/mobile service coverage to a

targeted remote health tracking where people are

expected to spend a considerable amount of time

under radiofrequency signal power (with frequencies

operating 1-2 orders of magnitude above

communication standards). With respect to

monitoring, unresolved issues still remain on the

hardware to be adapted for different uses (bed, chair

and low range vs high range environments),

processing issues on multi-person environments and

other agents (e.g. pets), and generalisation concerns

when moving far from quiet scenarios (bed, seated)

where great disturbances are not foreseen.

While the technology struggles to find its place

outside the telecommunications engineering labs and

signal demodulation procedures are further improved,

we advocate for a shift in perspective that

acknowledges the advances observed in IoT

ecosystems for remote health. The IoT market is

prepared to embark into the process of integrating a

contactless physiology technology while offering the

support infrastructure and context awareness that will

make radar vital signs clinically viable.

ACKNOWLEDGEMENTS

Authors would like to thank discussion and all the

feedback provided by the geriatricians within

NVISION’s healthcare network.

This research has received funding from the

European Union’s Horizon 2020 research and

innovation programme under the Marie Skłodowska-

Curie grant agreement No. 801342 (Tecniospring

INDUSTRY) UPTAKE and the Government of

Catalonia’s Agency for Business Competitiveness.

Researcher Zouhair Haddi received support under

Marie Skłodowska-Curie grant agreement No.

101029808 (EARLYCARE).

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248

Conflict of Interest: Authors Miquel Alfaras and

Zouhair Haddi are employed by NVISION, an R&D

company developing IoT and Digital Health services.

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