Vedlegg til Leverandørkonferanse om roboter og automatisering i helsetjenester og helsebygg
Fil: sosiale-roboter-i-kommunale-helsetjenester-ntnu-leverandorkonferanse-robot-230925.pdf
Vedlegg til anbudet Leverandørkonferanse om roboter og automatisering i helsetjenester og helsebygg fra Levanger Kommune.
Sosiale roboter i kommunale helsetjenester – fra hype til reell verdi
23 September 2025, Trondheim
Associate Professor Solveig Beyza Evenstad, NTNU, Institutt for
industriell økonomi og teknologiledelse, Gjøvik
[navn skjult]
• MULTIDISCIPLINARY BACKGROUND:
– 1 year - Social sciences (METU, Ankara, Turkey, 1980-1981)
– BA in Management (Boğaziçi University, Istanbul, 1981-1985)
– BSc Informatics – Systems Analysis (UiO, Oslo, 1985-1988)
– Master in Management – Organizations and Leadership (NTNU, Trondheim, 2008-2011)
– PhD, Communication and Media Studies (Université Côte d'Azur, Nice, 2012-2016)
• ACADEMIC SINCE 2012
– Assoc. Professor in Organizations and Leadership at INN University, Lillehammer (2017-2020)
– Associate Professor at NTNU Gjøvik since August 2023, Organizations and Management, Org.
psychology and management, Quality management, Qualitative method, Sustainable organization
and management of innovation
• 20 YEARS OF INDUSTRY EXPERIENCE BEFORE ACADEMIA
– Hydro (aluminium, oil, fertilizers, petrochemicals, ICT); IBM, Cytec (now Solvay, chemical industry),
TINE, the Norwegian Food Safety Authority, Navico (electronics);
– Worked abroad in Belgium, Switzerland, France.
– Managed teams in Mexico, New Zealand, USA; worked in Nordic and UK teams.
2
NTNU Social Robots for Support Team is part of «Team Active Aging»
under the TSO Community, a strategic focus area for NTNU
• Human (Prof. Aud
Obstfelder, Associate
Prof. [navn skjult])
• Technology ( Prof.
Deepti Mishra, Associate
Prof. Akshara Pande)
• Organization (Associate
Prof. [navn skjult] –
project coordinator)
We have got some of NTNU's best social robot experts with us
Social robots research Health care research
Several EU
potential
partners were
interested
Innovation networks Municipalities
Social Robots in Municipal Healthcare
Norwegian municipalities face a growing elderly
population, staff shortages, and rising loneliness. And
the state wants people to live at home as long as
possible.
• Could social robots be part of the solution for
supporting the elderly and cover their (unmet)
needs?
• How can social robots contribute to optimize task
division / reallocation in carework?
• How can social robots be integrated in municipal
healthcare in an ethical, responsible and
sustainable way (trippel bunnlinje)?
• How can municipal decision makers build up the
necessary competence to make the good
procurement and implementation decisions of this
new technology?
Welfare technologies – the optimal home environment for "living at home
longer"(?) What place do social robots have in this environment? What
(unfilled) need can they meet for brukere? Hvordan kan de støtte helse-
omsorgsarbeidere i eldreomsorg?
8
Social robots – some definitions
• Robots in care environments are often categorized as either service robots
which perform physical tasks such as cleaning, lifting, or guiding or social
robots, which interact with humans through social behaviors and
communication (Dautenhahn, 2007).
• Socially assistive robots (SARs) are a subcategory of social robots defined
as “robots that assist through social rather than physical interaction” (Feil-
Seifer & Mataric, 2005). SARs provide cognitive support, motivation, and
companionship in domains like eldercare, rehabilitation, and special
education.
• A personal assistant robot may offer physical help and social
companionship simultaneously (Broekens et al., 2009; Naneva et al., 2020;
Salichs et al., 2020)
Social robots are robots that are able to interact with humans and display social behaviors such as
speech, gestures, and emotional expressions to facilitate social interaction (Dautenhahn, 2007)
ASIMO: A humanoid robot developed by Honda.
Pepper: A semi-humanoid robot developed by SoftBank Robotics
that can understand and respond to human emotions.
Jibo: A home assistant robot that can interact with users through
voice and visual displays.
Moxi: An animated household robot designed for children.
Kaspar: A robot designed by the University of Hertfordshire to
help children with autism.
PARO: A therapeutic robot baby harp seal that is used in care
institutions, especially for dementia patients.
hitchBOT: A social robot that attempted to hitchhike across the
United States.
Kismet: A robotic head that demonstrates emotion recognition
and expression.
Tico: A robot designed to improve children's motivation in the
classroom.
NAO: A humanoid robot used in educational settings and
healthcare.
Care-O-Bot: A robot designed to help with care tasks in
healthcare.
A.L.O.: A robotic butler used in hotels and shopping malls to
provide customer service.
Cozmo: An entertainment robot designed by Anki.
Social Robots in Elderly Care
• Challenges of an aging population and caregiver shortages (Martinez-Martin et al., 2020).
• SARs can provide psychosocial benefits for older adults, such as reducing loneliness
and depression and improving cognitive functioning and overall well-being (Abdollahi et
al., 2017; Di Napoli et al., 2023; Góngora Alonso et al., 2019; Sawik et al., 2023).
• Salichs et al. (2020) identified four application areas where social robots can benefit the elderly
and their caregivers using cognitive and mental tasks: safety, entertainment, personal
assistance, and stimulation.
• Many existing eldercare robots do not yet fully meet users’ needs or expectations, and
real-world impacts on quality of care remain limited (Sawik et al., 2023).
• No reductions in staffing needs were observed in earlier studies, meaning the robots
supplemented rather than replaced human care (Fracasso et al., 2022).
• The need for long-term, real-world studies that consider not just the robot’s performance but
also organizational and contextual factors that affect success (Koh et al., 2021)
• High costs and fragmented solutions (Sawik et al., 2023).
Social Robots for Children with Special Needs
• Many children with autism spectrum disorder (ASD) respond positively to robot-
assisted interventions (Martinez-Martin et al., 2020)
• Long-term integration of a humanoid robot into autism therapy promote sustained
engagement, increased eye contact, and positive emotional responses in children
with ASD, particularly when sessions are personalized and repetitive (Rakhymbayeva et
al., 2021)
• SARs in special education improve therapy engagement and social outcomes for
learners with developmental disorders (Papakostas et al. 2021)
• Many children with autism appear to prefer simpler, less human-like robot designs, for
instance, devices with bright colors, cartoonish faces, or animal features over highly
realistic humanoid robots (Robins et al., 2006).
Needs are not «one-size-fits-all»
• Mrs. Hansen: Lives
alone, needs safety
checks and reminders
• Mr. Ali: In facility, craves
meaningful interaction
• Solution must adapt to
individual and evolving
needs
Human needs as design foundation in social robotics in
elderly care
14
Maslow → Users needs and social robot mapping
[navn skjult] User Need Robot Feature Existing Welfare Technology
Daily routine Medication & hydration
Physiological Pill dispensers with alarms
support prompts
Personal safety alarms, GPS
Safety Feeling secure Emergency alerts
trackers, fall detectors
Conversation, pet robot Video call platforms, digital
Belonging Social connection
behaviors photo frames
Positive feedback, Digital reminiscence tools,
Esteem Sense of purpose
shared tasks activity planning apps
Games, storytelling, Brain training apps, interactive
Self-actualization Mental challenge
memory cues tablets
Impact on caregivers and their needs
Healthcare professionals should engage, because social robots can support them
in their work (Bemelmans et al., 2013).
Royakkers and Van Est (2015) advocate for a value sensitive design in which
developers and researchers take into account wishes and needs of caregivers and
care recipients.
The design of the social robots would be influenced by acceptance and preference of
users to work with social robots. (Frennert & Östlund, 2014).
Social robots can relieve healthcare professionals from their workload (Royakkers &
Van Est, 2015).
Key questions when embarking upon social robotics projects
What do older adults actually want and need?
How do we include users and caregivers in design decisions?
How can social robots help preserve dignity and autonomy?
What are the risks, and how do we build trust?
“A good social robot is
not the most
advanced—it's the one
that meets real unmet
human needs.”
— Dr. [navn skjult]
Evenstad
What are the needs, impact and challenges?
A workshop on social robots
among researchers from
different disciplines at NTNU,
USHT, NR with own old or
deceased parents as reference
point (CAREBOT project,
NTNU& partners).
Current known limitations for social robots
• High acquisition costs
• Narrow functionality (Fracasso et al., 2022)
• Technical challenges such as battery capacity (Deshmukh et al., 2018)
• Mixed user acceptance (Sawik et al., 2023)
• Organizational resistance (Koh et al., 2021)
A large-scale review (Naneva et al., 2020) found that general attitudes towards
social robots in education and healthcare tend to be positive, but acceptance is
highly dependent on the robot's behavior and the context.
Digital security and privacy are important (Lutz et al. (2019).
20
Experiences from Japan
• Experiences with Hug, a lifting robot;
Paro, a robotic seal; and Pepper, a
humanoid robot.
• Hug was meant to prevent care workers
from having to manually lift residents,
• Paro offered a robotic form of animal
therapy (while also acting as a
distraction aid for some people with
dementia who made repeated demands
of staff throughout the day), and
• Pepper ran recreational exercise
sessions so that staff would be freed for
other duties.
Paro and Pepper in the nursing home Sakura
“Paro was received more favorably by staff and Social robots like Paro and Pepper were
residents alike. Shaped like a fluffy, soft toy seal, introduced precisely to address such
psychosocial and cognitive needs in
it can make noises, move its head, and wiggle its Japan. However, the outcomes were
tail when users pet and talk to it. At first, care mixed; robots sometimes provided
workers were quite happy with the robot. comfort but often introduced
complications or failed to sustain
However, difficulties soon emerged. One resident engagement. Wright observed:
kept trying to “skin” Paro by removing its outer
layer of synthetic fur, while another developed a
very close attachment, refusing to eat meals or
go to bed without having it by her side. Staff
ended up having to keep a close eye on Paro’s
interactions with residents, and it didn’t seem to
reduce the repetitive behavior patterns of those
with severe dementia.”
22
Paro and Pepper in the nursing home Sakura
Pepper was not a huge success either.
Wright observed: “…But care workers
quickly realized that to get residents to
participate in the exercise routine, they
had to stand next to the robot, copying
its movements and echoing its
instructions. Since there was a
relatively small set of songs and
exercise routines, boredom also started
to set in after a few weeks, and they
ended up using Pepper less often.”
23
Hug robot for lifting in the nursing home
Wright observed that social robots often do
not reduce caregiver burden. Robots add
work through maintenance, training,
troubleshooting, and supervision. “Staff
stopped using Hug after only a few days,
saying it was cumbersome and time
consuming to wheel from room to room—
cutting into the time they had to interact with
the residents. And only a small number of
them could be lifted comfortably using the
machine.”
24
Conversations with Tokyo Science, AIST, Shibaura Inst.
of Technology in Japan (5th September 2025)
• The talks indicate there are upsides and downsides with the experiences.
• The industry is fueling more research and innovation and there is literature
that shows opportunities and challenges.
• In Japan there is a political agenda to support to technology development,
with national policy, strategy and investments in social robotics.
• Robots are still too expensive and have too narrow functionalities.
• Human-centric design focused on needs and ethical, responsible, thrustworthy
approach can lead to good results.
• More interdisciplinary research and development needed. Today most projects
are technology dominated, with technologists who develop what they think
people need, rather than involving them in the development from the start
(participatory design).
25
26
Norwegian social robots experience summary
AV1 and Tessa: Connection and Reminders
AV1 (No Isolation): Remote Tessa: Voice assistant for
school and social participation reminders and dialogue for
for long-term sick children in elderly people living at home in
Fredrikstad, Oslo, Stavanger, Kristiansand. Low user
Bergen. Very positive effect on threshold and useful support in
children's attachment. everyday life.
28
Tessa in Norway
• A robot system connected to
sensors in the home
• Learns the user’s daily routines
• Helps maintain routines by giving
reminders, e.g., to eat
• Notifies relatives of abnormal
behavior (e.g., frequent toilet
visits, walking outside at night)
Source: Jin Kristian Hurum/[navn skjult], NTNU
29
Norwegian experiences
Opportunities Challenges
• Helps older adults with mild • Some users expected two-
cognitive decline maintain way communication, leading
daily routines via spoken to disappointment.
reminders. • Voice clarity, dialect
• Acts as a communication mismatch, and technical
bridge between older adults bugs affected
and informal caregivers. comprehension and trust.
• Symbolizes companionship, • Less effective for users with
reducing loneliness for some advanced dementia or little
users. motivation for self-care tasks.
Source: Jin Kristian Hurum/[navn skjult], NTNU
30
Existing pilots in Norway lacked personalization and
contextualization
• Personalize: It is important to identify real unmet needs and program a layer
of functionalities that will serve the needs of the elderly and be a support for
the caregivers.
• Contextualize through integrating the robot to the built environment in a
Norwegian elderly home which can have already a robot pill dispenser, fall
sensor, electronic lock. The functionalities should not duplicate and create en
environment of gadgets with contant reminders, which will “infantilize” and
irritate the elderly, jeopardizing adoption. Furthermore, all IoTs need to talk
together and connect to municipal nursing & IT. Can we integrate the robot to
the welfare technology environment of a typical home? What cybersecurity
risks are there?
• Urgent municipal need for guidelines and competency to handle the
pressure from a very dynamic social robot market.
31
Lessons learned from EU projects in a EU-Japan cooperation, which
we in CAREBOT follow and recommend:
• Importance of user-centric participatory design
• Fight ageism: older people are not ‘all the same’
• Need for trust and security
• Ensure interoperability of systems in the built environment
• Sustainability and accessibility
• Embrace personalization
• Multidisciplinary approach
• Educational programs and support
32
Implications for design & implementation: Employ Maslow’s framework as a
design roadmap for personalization and contextualization.
Start with human needs (of both users/patients and caregivers), not
features.
Prioritize basic needs first (safety, routines) but avoid duplication
among the welfare technologies in the built environment.
Build up towards psychological and self-fulfillment domains for the
elderly with TRUST as key.
Use modular designs to customize robots for different needs of different
personas (one size doesn’t fit all).
Think integrated healthcare, avoid fragmented, narrow solutions. Be
courageous to test and build competence.
33
Thank you for your
attention!
Questions?
[skjult]@ntnu.no
m.40319156
Tekst er automatisk ekstrahert fra originaldokumentet. For autoritativt innhold, se originalfilen.