Sensing the Body: Towards Best Practices for Integrating
Physiological Signals in HCI
Francesco Chiossi

Ekaterina R. Stepanova

Benjamin Tag

Simon Fraser University
Canada
katerina_stepanova@sfu.ca

Monash University
Melbourne, Australia
benjamin.tag@monash.edu

Arindam Dey

Sven Mayer

Alexandra Kitson

Simon Fraser University
Surrey, Canada
akitson@sfu.ca

The University of
Queensland
Brisbane, Australia
a.dey@uq.edu.au

LMU Munich
Munich, Germany
info@sven-mayer.com

Monica
Perusquía-Hernández
Nara Institute of Science
and Technology (NAIST)
Nara, Japan
perusquia@ieee.org

Abdallah El Ali

Centrum Wiskunde &
Informatica
Amsterdam, Netherlands
abdallah.el.ali@cwi.nl

Document

Repository

Reproducible Data Workflow

Protocol Methods &
Apparatus

Protocol Pipeline

Protocol versioned 

code, data dictionary,
metadata

Data Preprocessing

Data Curation

Link Materials in the
paper

Stage

arXiv:2312.04223v1 [cs.HC] 7 Dec 2023

LMU Munich
Germany
francesco.chiossi@lmu.de

Raw Data Collection

Data Storage

Publication

Maintenance & Legacy

Figure 1: Reproducibility Workflow for Physiological Signals in HCI. Icons at the bottom represent the key steps in the workflow
of a typical HCI user study, from inception to reporting. Book icons indicate components for reproducible best practices that
map onto this workflow. Repository icons on top provide potential open-source platforms to share documented protocols.

ABSTRACT
Recently, we saw a trend toward using physiological signals in
interactive systems. These signals, offering deep insights into users’
Permission to make digital or hard copies of part or all of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA
© 2024 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-0331-7/24/05.
https://doi.org/10.1145/3613905.3636286

internal states and health, herald a new era for HCI. However, as this
is an interdisciplinary approach, many challenges arise for HCI researchers, such as merging diverse disciplines, from understanding
physiological functions to design expertise. Also, isolated research
endeavors limit the scope and reach of findings. This workshop
aims to bridge these gaps, fostering cross-disciplinary discussions
on usability, open science, and ethics tied to physiological data in
HCI. In this workshop, we will discuss best practices for embedding
physiological signals in interactive systems. Through collective
efforts, we seek to craft a guiding document for best practices in

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

physiological HCI research, ensuring that it remains grounded in
shared principles and methodologies as the field advances.

CCS CONCEPTS
• Human-centered computing → Human computer interaction (HCI).

KEYWORDS
Physiological Computing, Affective Computing, Open Science, Physiological Signals, Replicability, Reproducibility, Transparency, Ethics
ACM Reference Format:
Francesco Chiossi, Ekaterina R. Stepanova, Benjamin Tag, Monica PerusquíaHernández, Alexandra Kitson, Arindam Dey, Sven Mayer, and Abdallah El
Ali. 2024. Sensing the Body: Towards Best Practices for Integrating Physiological Signals in HCI. In Extended Abstracts of the CHI Conference on Human
Factors in Computing Systems (CHI EA ’24), May 11–16, 2024, Honolulu, HI,
USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3613905.
3636286

1

MOTIVATION

In the human-computer interaction (HCI) community, we see a
growing interest in integrating physiological signals (e.g., heart
rate, breathing, electrodermal activity, brain activity, muscle tension) for usability evaluation and as input for interactive systems.
Physiological sensors are devices that measure our physiological
signals and, when analyzed, can provide insights into our physical and affective states [41]. Additionally, physiological sensors
can be employed in physiologically-adaptive systems for tailoring
interactions to users’ states or prompt changes in the system to
encourage a desirable user state [8–10]. Here, the field is developing traction, and we foresee more growth as sensor technology
becomes more viable and embedded in interactive systems [5, 6].
A recent increase in review papers mapping out sections of the
design domain with physiological sensors reveals the desire of the
community to structure and better understand the dimensions of
this emerging design space. For instance, Moge et al. [31] presented
a systematic review on interpersonal biofeedback outlining the use
of physiological signals in social interaction. Previously, Prpa et al.
[35] presented an analysis of theoretical frameworks underlying
the design of breath-responsive systems. In addition, Yu et al. [48]
reviewed biofeedback systems for stress management, identifying
several challenges in the interpretation of signals, scalability of
systems, and sparse evaluation. While all of these systematic revisions outlined biosignals’ existing and future opportunities, others
have identified methodological issues and a lack of shared best
practices [2, 36, 45]. Specifically, integrating physiological signals
in HCI necessitates a diverse skill set, from understanding physiological functioning and its associated psychological and cognitive
processes to expertise in signal acquisition and processing, machine
learning, and design, each a research area of its own.
The confluence of multiple disciplines presents a challenging
learning curve for researchers and designers looking to incorporate
physiological signals into studies and interactive systems. In contrast to building upon peers’ work, the current landscape of physiological signals in HCI sees most research groups independently
developing their datasets, analysis pipelines, and methodologies.

Chiossi et al.

Thus, HCI contributions to physiological computing are challenging as they must adhere to high standards from multiple disciplines
(neuroscience, cognitive science, design). To meet such rigorous
standards, HCI researchers must develop expertise in each discipline. There is a need to provide a shared framework and guidelines
to support HCI research using physiological signals.
Stemming from the work in other disciplines [12, 20, 34], HCI
researchers have started to provide guidelines to support incorporating physiological signals. Here, Babaei et al. [2] evaluated
practices for the recording and preprocessing of electrodermal activity (EDA). Similarly, Putze et al. [36] have provided reporting
practices for electroencephalographic signals (EEG) to support reproducible and reusable results. Finally, Treacy Solovey et al. [45]
have evaluated experimental practices for fNIRS research in HCI
and highlighted principles and patterns for effective brain-based
adaptive interaction techniques. While there have been strides towards the establishment of reporting guidelines and an emphasis
on transparency within the HCI community, evidence suggests that
these efforts have not significantly influenced the methodological
detail in conventional journal publications [11, 15, 33, 38]. The lack
of methodological detail indicates a potential need for alternative
strategies to enhance transparency and reporting in the HCI field.
As a result, these recent surveys imply that understanding and
replicability issues persist in HCI. Therefore, it is time to establish more intensive cross-disciplinary conversations to synthesize
current practices in physiological HCI research. This workshop
will combine quantitative and qualitative researchers working with
biodata to address issues around the open science, use and interpretation, and ethics of physiological data in HCI. Ultimately, we will
deliver a living document formalizing best practices as in previous
work [2] (see, https://edaguidelines.github.io/) to provide the HCI
community with sound and robust guidelines.

1.1

Workshop Topics

We aim to structure group discussions during this workshop around
four key topics and challenges commonly faced by researchers
and designers working with physiological signals. The discussion
will allow attendees to share their experiences and help to identify
and articulate common challenges across diverse domains. Through
this communal effort, we will begin to map out future directions for
the field of physiological computing to address these challenges.
1.1.1 Topic 1: Replicability and Research Transparency. As HCI
continues integrating physiological signals, applying FAIR Principles1 becomes necessary for repeatability, reproducibility, and
replicability. Several processes and platforms, like the Open Science
Framework2 and Zenodo3 , have been developed to foster community building. Open data-sharing enables result validation, supports
data aggregation for meta-analysis, encourages creative re-analysis,
and adapts to evolving scientific methodologies. However, open
data alone does not guarantee replicability. Often, data from individual, small-scale research projects –termed the "long tail of science"–
is produced with specific contexts or constraints in mind [19]. To
make this data usable by the wider community, there is a need
1 https://www.go-fair.org/fair-principles/
2 https://osf.io/

3 https://zenodo.org/

Sensing the Body

for standardized documentation or even formal models detailing
how the data should be shared and interpreted. For instance, OpenNeuro4 or ARTEM-IS [42] advocate for standardized datasets with
joint documentation models. Such efforts have demonstrated that
data sharing can amplify scientific studies’ impact, drawing contributions from various disciplines [46]. In the HCI domain, there are
notable examples like Babaei et al. [2] practices for EDA recording
and Bergström et al. [4] checklist for VR experiments. The Association for Computing Machinery (ACM) introduced badges5 to
reward research transparency, acknowledging the broader movement toward open science.
While there’s been a push towards reproducibility and transparency, e.g., the first HCI conference introduced an open science
track6 , or with changes even in the review processes of prominent
HCI conferences, studies suggest that many in the field still do not
openly publish their research artifacts [38]. This could stem from
misunderstandings about the process, the multifaceted nature of
HCI, or underestimating the importance of transparency. Balancing
privacy concerns with open science ambitions remains a complex
challenge, especially when dealing with sensitive physiological
data.
1.1.2 Topic 2: I/O of Physiological Signals. Integrating physiological signals in HCI presents many technical challenges [39]. At the
core, issues are related to the robustness, scalability, and adaptability of continuously sensing (wearable) physiological sensors [44].
Especially outside the controlled environment of a lab, these physiological sensors often demand calibration and are vulnerable to
motion artifacts [27]. This is further complicated when systems rely
on physiological data to infer users’ states or emotions, such as anxiety or task engagement, as discussed in our previous workshops
[17, 18]. Concerning scalability, researchers are increasingly adopting software-based physiological measurements, such as pulse and
vital sign measurement using remote PPG from facial data, and used
across mixed reality setups [30]. To advance the field, it is essential
to go beyond isolated use cases and establish foundational physiological principles. Further challenges include coupling physiological
signals with the appropriate body feedback modalities [1, 10] or
body augmentation, which can lead to questioning the sense of
agency [13].
1.1.3 Topic 3: Meaning-Making and User Experience of Physiological Signals. The various applications using biosignals reveal the
plurality of effects this technology can have on individuals and our
relationship to our own and others’ bodies. These effects can have
long-term consequences of reshaping these relationships beyond
the moment of the interaction. For instance, using biofeedback can
improve one’s interoceptive awareness (awareness of one’s internal
processes) [24], and receiving information about others’ internal
state can improve users’ ability to empathize with them, ultimately
enhancing mutual understanding [14, 21]. This could be particularly
useful when only limited affective cues are shared between users,
e.g., when people lack sensitivity to such cues, such as users with
autism. However, this enhanced access to others’ states through
4 https://openneuro.org/

5 https://www.acm.org/publications/policies/artifact-review-badging
6 https://iui.acm.org/2023/call_for_open_science.html

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

biosignals also carries the risk of drawing too much attention towards an externalized representation of biodata and away from the
human at the other end.
Biodata provides us with insights into the state and process of
our bodies. However, interpreting these signals is often ambiguous [22]. For example, what does it mean if my heart rate is 10 beats
faster than my partner’s, and how does this influence biofeedback
displays (cf., [16])? Considering a case from affective computing,
there is a growing debate arising from the constructionist models
of emotions [25] on how much could be possible to infer from the
physiological data about users’ emotions given the lack of universal expressions of emotions [3]. The influence of the context of
use presents another challenge for considering variant meaningmaking processes. Naturally, our social relationships significantly
affect our comfort level with sharing our intimate biodata. A heartbeat visualization that may be intimate for close relationships [26],
it might feel like oversharing in a professional relationship [37],
or can represent an abstract human unity when received from a
distant stranger [28].
1.1.4 Topic 4: Ethical and Privacy Concerns. Working with biosignals inevitably raises ethical concerns and privacy considerations.
Our physiology is inherently private, and by that nature, so should
biodata be considered sensitive and private, providing agency to
each individual to make an informed decision to share. In affective
computing, an ethics framework has been proposed where recommendations are given to developers, operators, and regulators [32].
This is because of the unexpected usages that arise beyond the
expectations of the initial system designer.
Biosignals often disclose insights into our internal states beyond our immediate awareness, complicating the issue of consent
for data sharing. Our limited grasp over such physiological processes underscores the need for establishing transparent, meaningful ground truths to prevent misinterpretations and ensure ethical
use in biosignal research. Hence, its interpretive potential when
making it accessible to others. Thus, we must consider users’ sensitivity and potential vulnerability when designing biodata-sharing
systems. Questions of privacy, agency over data, equity and power
relationships, data use, and storage are crucial in considering how
we design such technology. This issue is further amplified by the
lack of consistent data privacy and security standards across health,
academia, and private business domains, where the industry is often
not bounded by the same data security standards as academic and
medical fields. Could a privacy-by-design approach be an initial
solution? With this approach, we can embed privacy considerations
into every stage of adaptive and biofeedback systems’ design and
development process [7]. Alternatively, federated learning could
offer a solution by enabling model training without centralizing
sensitive physiological data, ensuring privacy and regulatory compliance [23].

2

ORGANIZERS

Francesco Chiossi (https://www.francesco-chiossi-hci.com/) is a
PhD researcher in the Media Informatics Group at LMU Munich.
With a background in cognitive neuroscience, he focuses on implicit
measures of human behavior, such as EDA and EEG, as an implicit
input to design physiologically-adaptive systems.

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

Ekaterina R. Stepanova is a Ph.D. Candidate at the School of
Interactive Arts and Technology at Simon Fraser University with a
background in cognitive science, developmental psychology, and
virtual reality. Her research employs somaesthetics and embodied
cognition to design mediated experiences with bioresponsive and
immersive technologies.
Benjamin Tag is a Lecturer at Monash University. He researches
Human-AI Interaction, Digital Emotion Regulation, and human
cognition with a focus on inferring mental state changes from
physiological data collected in the wild.
Monica Perusquia-Hernandez (https://www.monicaperusquia.
com/) is an assistant professor at the Nara Institute of Science and
Technology (NAIST), Japan, working in affective computing, signal
processing, and interoceptive awareness enhancement in cyberphysical systems. Her work relies on Computer Vision, EMG, EEG,
ECG, and EDA for congruence estimation between facial expressions and emotions.
Alexandra Kitson (https://www.alexandrakitson.com) is a postdoctoral fellow in the Tangible Embodied Child Computer Interaction Lab at Simon Fraser University whose research is focused on
designing, developing, and evaluating interactive systems such as
wearables and virtual reality to support both personal and social
transformation and emotional well-being.
Arindam Dey is a computer scientist on a mission to make Metaverse better for users in various ways. Currently, he is a Research
Scientist at Meta, focusing on health and safety in the metaverse.
He is also Honorary Research Fellow at the University of Queensland, Australia, primarily focusing on Mixed Reality and Empathic
Computing.
Sven Mayer (https://sven-mayer.com) is an assistant professor
at LMU Munich. His research sits at the intersection between HCI
and Artificial Intelligence, where he focuses on the next generation
of computing systems. He uses artificial intelligence to design, build,
and evaluate future human-centered interfaces.
Abdallah El Ali (https://abdoelali.com) is an HCI research scientist at Centrum Wiskunde & Informatica (CWI) in Amsterdam
within the Distributed & Interactive Systems group. He leads the research area on Affective Interactive Systems, combining advances in
HCI, eXtended Reality, and Artificial Intelligence to measure, infer,
and augment human cognitive, affective, and social interactions.

3

PLAN TO PUBLISH PROCEEDINGS

We will publish the workshop proceedings on CEUR-WS.org. Moreover, the workshop website will host papers accepted two weeks
before the conference upon the authors’ consent.

4

WEBSITE

The website will contain a call for papers, links to materials and
activities for asynchronous engagement, and links to accepted position papers. Finally, the most important contribution of the website
will be a living document that relates to best practices for biosignals
research in HCI concerning validity, reproducibility, and privacy.

5

PRE-WORKSHOP PLANS:

The plan for this workshop began at DIS [43] and at MobileHCI [40].
From those two workshops, we have established a community and

Chiossi et al.

joint effort on Slack7 . Organizing this workshop is the important
next step in the long-term plan discussed, and members of the new
micro-community on Slack were invited to contribute to the organization of this new workshop. A dedicated webpage will be hosted
on the first author website8 . We will promote the workshop on the
Slack server, in research groups, and at upcoming HCI conferences.
Standard CfP releases via mailing lists and social media channels
will also be used to increase the reach and inclusivity of the event.
To ensure a productive discussion, we will select participants
with relevant expertise working with biosignals from a physiological computing, design, and ethics perspective. Prospective participants will be invited to either submit a position paper (1-4 pages),
or a copy of their previously published paper exploring one of the
workshop topics. Authors will be encouraged to follow the accessibility guidelines for their submissions. Additionally, participants
will be asked to complete a short survey to help us better understand the distribution of participants and forms of engagement
(online or in-person) to optimize the planned workshop structure.
The survey will inquire on the planned form of attendance, the
primary field or work, ranking of the interest in proposed workshop topics with an option to suggest new ones, and consent to be
invited to a Slack group for communication with organizers and
other attendees before the workshop. We aim to attract and select
about 30-35 participants.
Review of Submissions. Our focus for reviewing will be on the
submissions’ potential to provoke relevant discussion at the workshop. We expect position papers that present provocative views of
the future, case studies, or extensions to existing work to discuss
interesting research outcomes. The workshop organizers will primarily be responsible for reviewing and determining acceptance. If
we receive unrelated submissions, this will be expanded to those in
the existing Slack community.

6

WORKSHOP MODE

We will conduct a synchronous hybrid workshop. Our hybrid approach will facilitate sharing between virtual and in-person attendees, ensuring discussion grounded in equitable and diverse
participation, integrating a broad range of perspectives.
Hybridity and Asynchronous Engagement. During the workshop,
we will introduce discussion topics to attendees using a presentation
projected in the physical room and shared on the teleconference
platform for remote attendees. We will use a Miro board to note
discussion topics to make them available for in-person and remote
participants. One of the organizers will copy the topics from the
whiteboard into the Miro board and vice-versa. Remote participants will use the Miro Boards, which will also be projected on
the whiteboards in the conference room. This way, participants in
the conference room can add physical notes to the same discussion
space and see the contributions of remote participants. We will
use automated transcription in Zoom during large group discussions to improve accessibility. We will share all the materials on the
workshop website and through the Slack channel for access by participants who cannot attend synchronous sessions. These materials
7 Physiological Interaction Slack Server
8 https://www.hcilab.org/physiochi24

Sensing the Body

will include papers submitted by accepted participants, descriptions
of the provocation exercises that participants can experiment with
by themselves, prompt cards used for structuring the discussion,
and links to the Miro board summarizing our discussion.
Materials. We will bring sketching materials, such as paper,
sticky notes, markers, etc., for physically present participants to
note down their discussion and prepare a Miro board for online participants and the summary of everyone’s discussion. Website, Miro
board, and all other materials will adhere to accessibility criteria outlined by ACM. We will continue to consult with CHI Accessibility
Chairs to ensure accessibility before and during the workshop.
Accessibility and Inclusivity. Accepted authors will be expected to
enhance the accessibility of their submissions by including accurate
subtitles for videos and ensuring their PDFs are screen-readerfriendly. We will proactively contact our workshop attendees to
identify and address additional accessibility requirements for the
event day.

7

WORKSHOP STRUCTURE

The workshop spans a full day and will be built into four rounds
(arranged around the natural breaks in the conference). In all parts,
the aim is to encourage discussion, especially before breaks and
lunch, where the most natural discussions are likely to occur. One
organizer will lead the in-person workshop for each program section, ensuring a lively and interactive session. Simultaneously, a
second organizer will facilitate the online participation and discussion, ensuring that remote participants are equally engaged and
included in the proceedings.
Round 1: Kickoff and Speed Dating (40 minutes). The workshop will begin with an introduction by the organizers and a short
warm-up speed-dating activity. This speed-dating activity will help
attendees to focus on the workshop interest and engage with the
other participants. Participants will be asked to introduce themselves and explain their motivation to participate in the workshop.
They will also be asked to briefly introduce the work (e.g., method or
application). Due to the expected diversity in participants’ research
backgrounds, we will start by organizing into different groups based
on identifying statements (e.g., qualitative researcher or quantitative researcher) based on the survey we ask participants to fill in.
The ultimate aims of Round 1 are a) to explicate the scope of the
workshop and the expertise in the room, b) to highlight the variety
of expertise, and c) to end up with mixed groups around the tables.
Further, by doing so, we aim to avoid being on laptops and settling
into a passive form of listening to talks. Once in mixed groups, the
remainder of R1 will focus on important shared research questions
and create a physical post-it mindmap on an available large surface
and a Miro board for online attendants.
Round 2: Research Transparency for Biosignals in HCI (60
minutes). Once in mixed groups, in R2, we will focus on important
challenges for Open Science practices and create a physical post-it
mindmap or on a Miro Board. Chiossi will provide an introductory
talk (10 minutes) on open guiding principles [47], where data should
be Findable, Accessible, Interoperable, and Reusable (FAIR). This
allows participants to have a shared background in the topic. Then,

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

participants will engage in 3 rounds of medium to small group
discussions (4-8 participants per group) in the form of roundtables.
The main goal of each group is to identify how each group member
follows or does not follow FAIR guidelines in their work. One moderator per group will be chosen to report to all other participants
on the discussion outcomes. Each group, online and offline, will
be joined by one organizer, who will focus on timekeeping and
deliverables to ensure that the groups are ready to contribute to
the large group discussion at the end. We expect to discuss all FAIR
principle statements before lunch. This exercise will flow naturally
into the first coffee break, where discussions can be continued.
Round 3: Keynote. (60 minutes). We plan to invite one keynote
speaker to the workshop. The keynote will be an established researcher in the physiological computing area, and the talk will
cover all four topics of the workshop in 30 minutes, followed by an
extensive Q & A and a discussion session. This round will have an
expected duration of one hour.
Discussion Lunch (90 minutes). Depending on the arrangements
available for lunch in the area, we aim to send groups (arranged
in R1) to lunch to continue discussions. We will ask each group to
return to the afternoon session with a considered set of biosignal
measures that they employ in their research and which challenges
they face regarding the construct validity. The lunch session is
expected to last 90 minutes.
Round 4: Meaning-Making and UX of Biosignals (45 minutes). To avoid a post-lunch slump, this session will first bring
the ideas back to the room from lunch and start with grouping
researchers by biosignal of interest: brain (EEG and fNIRS), peripheral (EDA, ECG, electrogastrography), biosignals with implicit and
explicit control (eye-tracking and electromyography, i.e., EMG).
Each group will develop a shared definition of how each biosignal
is physiologically interpreted, and to ensure a breadth of discussion,
list what each signal is mapped in their HCI research to which construct and as input for interaction. The definition and application
areas will be presented and discussed by a representative of each
group.
Round 5: Sharing Biosignals (45 minutes). Given the now
shared theoretical background and alignment on definitions across
participants, we will switch the focus from physiological evaluation to a multi-user, social perspective of sharing biosignals. Here,
participants who selected the topic of sharing biosignals will pitch
their presentations on their submitted position paper - case study.
Groups previously formed in Round 4 will discuss how biodata can
be represented, either individually [21] or as an aggregate from multiple users, offering potential privacy solutions [37]. Second, groups
will discuss the centrality of visual representations in biofeedback
designs [29] and explore the potential of other sensory modalities.
Participants will highlight the challenges and opportunities in mapping and representing signals for multiple users, presenting the
outcome of discussions by emphasizing the need for symmetrical
data representation and its impact on user interpretation.
Round 6: Privacy and Ethics (60 minutes). The final part
of the afternoon block activity will focus on responsible research,
ethics, and privacy. As a community, we expect this to be a key focus

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

issue for the future, with physiological interaction and technology
presenting several potentially invasive challenges. We intend to
use the Legal and Moral-IT cards on tables to provoke discussion9 .
Either submitting authors or invited speakers will be invited to cofacilitate this activity, where they have already considered aspects
of this theme.

8

POST-WORKSHOP PLANS

We will publish the guidelines summary on the workshop website.
Ideally, this could result in a joint publication between organizers
and participants. Finally, we will prepare a proposal for a special
issue at TOCHI on integrating biosignals in HCI, potentially including a Dagstuhl proposal and further workshops at SIGCHI
conferences (IUI and UbiComp). Additionally, involved authors
will mentor early-career researchers via the Slack Server and future events. The Slack Server will serve as a platform to engage
participants, continuing the evolving discussion of challenges and
opportunities presented by designing with biosignals: post questions, share posts about their work, recruit participants, and form
collaborations.

9

CALL FOR PARTICIPATION

Biosensing technologies are increasingly more widely integrated
in HCI. Biosignals provide novel opportunities for interaction, offering valuable insights into ordinarily hidden processes inside our
bodies, revealing somatic information about our and other’s bodies,
emotions, health, and cognitive processes. However, integrating
biosignals in HCI presents many challenges about transparency,
UX, I/O, interpretation of biodata, and broader ethical concerns. To
map out the landscape of existing challenges and future research
directions, we invite participants working with biosignals to join a
one-day hybrid workshop held at the 2024 ACM SIGCHI Conference on Human Factors in Computing Systems following a hybrid
format. We welcome participants from HCI, Psychology, Neuroscience, Data Science, Mixed Reality, and Digital Ethics. We invite
submissions of 2-4 page position papers or case studies, presenting a
project, or articulating a challenge related to this call. Alternatively,
authors may submit their previously published papers raising relevant questions. Submissions should be sent to EasyChair 10 , must
adhere to accessibility guidelines outlined by ACM, and use a CEUR
Workshop Proceedings template. The organizing committee will
select submissions based on the quality and contribution of the
work relating to the workshop themes, with a special focus on
biosignal integration. At least one author of each accepted submission must attend (in-person or remote) and register for the
workshop and the CHI’24 conference. For more information, please
visit https://www.hcilab.org/physiochi24/.

ACKNOWLEDGMENTS
Francesco Chiossi was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 251654672TRR 161. Katerina Stepanova was supported by the Social Sciences and Humanities Research Council of Canada. M. PerusquiaHernandez was supported by JSPS Kakenhi Grant Number 22K21309.
9 https://lachlansresearch.com/the-moral-it-legal-it-decks/

10 https://easychair.org/conferences/?conf=physiochi24

Chiossi et al.

Sven Mayer was supported by National Research Data Infrastructure for and with Computer Science (NFDIxCS).

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