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REVIEW 3 major objections 7 minor 41 references

Effects of Robotic Touch on Older Users During Walking Guidance by a Humanoid Robot

T0 review · 3 major / 7 minor · reviewed 2026-07-13 · grok-4.5

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Pith's one-line read Older adults prefer gentle, stable robotic touch over contactless walking guidance, with higher trust and comfort.

desk verdict Solid multimodal HRI study: older adults prefer stable contact (HH/FC) over arm-link or none; the trust-from-distance claim is soft but the preference pattern does not need it. read the letter →

arxiv 2607.09323 v1 pith:6NZXXD4P submitted 2026-07-10 cs.RO

Leonie Leven , Marko Ackermann , Christian Werner , Melina Schmetterer , Theresa Buchner , Monika Eckstein , Katja Mombaur This is my paper · ORCID
classification cs.RO
keywords physicalhuman–robotinteractionrobotictouchwalkingguidancemobilityassistiverobotsolderadultstrustcontactforcegeriatriccare
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This study tests how healthy older adults respond to four ways of walking with a humanoid robot guide: no contact, holding the robot's wrist, linking arms, and resting the forearm on the robot's forearm. Using force, distance, heart and skin measures, and questionnaires with 24 people aged 68–88, it finds that the two firmer but still light-touch modes produce larger contact forces, closer relative standing distances, and higher ratings of safety, trust, and comfort. Physiology shows only a mild, mostly non-significant rise in stress. The practical aim is to give designers of care robots a clear preference order: stable, gentle contact is accepted and preferred to walking without touch or to arm-linking, which felt rigid and least trusted.

What carries the argument

Relative (normalized) lateral distance: absolute distance scaled by the pose-specific feasible range for each contact mode, treated as a behavioral proxy for trust and confidence, combined with estimated contact force magnitude and post-trial Likert and PANAS ratings.

What would settle it

A replication in which participants rate trust and comfort higher for the no-contact or linking-arms conditions while also choosing larger relative distances in the wrist-hold and forearm-rest conditions, or in which force and questionnaire rankings reverse under the same four poses.

Watch

Extended reading notes

Core claim

Gentle, stable robotic touch—holding the robot wrist or resting the forearm on the robot forearm—is preferred by older adults over contactless guidance and arm-linking. These two modes yield larger interaction forces, lower relative human–robot distances interpreted as higher trust and confidence, and better questionnaire scores for safety, trust, and comfort, while overall physiological stress rises only slightly.

Load-bearing premise

That a smaller relative distance between person and robot reliably means higher trust rather than simply reflecting arm pose, path width, or how the contact is kinematically forced.

Editorial extensions

If this is right

  • Walking-guidance robots for geriatric settings should default to stable forearm or wrist contact rather than purely contactless pacing.
  • Arm-linking should be avoided or redesigned because it produces the lowest forces, largest relative distances, and poorest subjective scores.
  • Mild physiological arousal need not block acceptance if subjective safety and trust remain high.
  • Force and relative-distance feedback can be used as online signals of comfort for adaptive robot control.
  • Designers can treat light, distributed contact forces (roughly 9–14 N median) as companion-level rather than load-bearing support for independently walking older adults.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If relative distance is only a partial trust proxy, future trials could add free choice of contact mode mid-walk to test whether people spontaneously select the higher-force poses.
  • The same preference order may not hold for frail or fall-risk users who need actual weight support rather than companionship.
  • Warmth from motor heat, noted positively by participants, suggests passive thermal cues could be deliberately engineered to increase acceptance of hard plastic surfaces.
  • Autonomous speed and path adaptation driven by force and distance could convert the laboratory preference into usable hospital-corridor behavior.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. This empirical user study examines how 24 healthy older adults (68–88 yrs) perceive and respond to four contact modes during teleoperated walking guidance by the TIAGo Pro humanoid: no contact (NC), hand-holding the wrist (HH), linking arms (LA), and full forearm contact (FC). A multimodal protocol records ECG, EDA, estimated contact force from joint torques, laser-based human–robot distance, and post-trial questionnaires (safety/trust/comfort items, short PANAS, NARS). Results show non-significant ECG shifts consistent with mild arousal, modest EDA elevation under contact (significant only for SCR amplitude under sitting baseline for LA/FC vs NC), larger forces and more favorable Likert scores for HH and FC, and higher relative (normalized) lateral distance under LA. The authors conclude that gentle, stable touch (HH, FC) is preferred over contactless guidance and arm-linking for design of robotic walking assistance.

Significance. The work addresses a practically relevant gap: physical human–robot interaction for walking guidance in geriatric care, where navigation decline and staff shortages motivate assistive robots. Strengths include a within-subject randomized design, multimodal sensing (physiology + force + proximity + subjective scales), an age-appropriate platform (TIAGo Pro with adjustable height and series-elastic arms), and transparent reporting of non-parametric tests when normality fails. The force and questionnaire patterns coherently favor HH/FC over LA and provide actionable design guidance (prefer stable, larger-area contact; avoid rigid arm-linking with hard covers). The contribution is incremental rather than foundational, but it is one of the few studies that actually implements multiple contact modes with older adults rather than video or free-choice designs with n≈4. If the trust interpretation of relative distance is appropriately caveated, the paper is a useful empirical reference for physical HRI in aging.

major comments (3)
  1. §3.6.3, Table 1, Figs. 6–7, Abstract and §5.1: The central behavioral claim that lower relative distance indicates higher trust/confidence is under-validated and partly confounded by kinematics. Distance bounds were obtained from only two individuals’ extreme poses; the NC upper bound (1.5 m) is an arbitrary hallway-tape limit not on the same scale as the contact conditions; LA’s absolute range is extremely narrow (0.62–0.75 m), so a high relative distance (median ~0.7) may simply reflect discomfort with the rigid plastic arm rather than a general distrust of robotic touch. Absolute distances already show NC ≈ HH while LA is forced close. The Abstract and conclusion treat the relative-distance result as evidence of trust that is then “supported” by questionnaires. Soften the interpretation: present relative distance as an exploratory interaction-intensity metric, report absolute distance
  2. §3.7 and Results: Incomplete data for load-bearing metrics (ECG n=14, force n=16, distance n=15 out of 24) are acknowledged but not analyzed for systematic missingness or bias. Force and distance are central to the “larger interaction forces … lower relative distances … higher trust” narrative. Provide a missing-data table by condition, state exclusion criteria explicitly, and either impute/sensitivity-check or restrict the trust/force claims to the complete-case subsample with appropriate power caveats.
  3. §4 EDA and Abstract: Physiological support is weak. ECG differences are non-significant; EDA significance for ScrAmplitudesMean appears only under the sitting baseline and only for LA–NC and FC–NC pairs (Table 5). The Abstract’s phrasing “Physiological results reveal a slight increase in stress levels during robot interaction” is acceptable if qualified, but the Discussion should more clearly separate possible confounds (robot surface warmth under HH/FC electrodes, walking vs sitting baseline, short trial windows) from any contact-specific arousal claim. Do not let the mild EDA pattern carry equal weight with the clearer force and Likert results.
minor comments (7)
  1. Inconsistent parameter spelling: ScrAmplitesMean / ScrAmplMean / ScrAmplitudesMean appear across text, Table 4 and Fig. 4 caption; standardize.
  2. §3.6.3 / Table 1: State explicitly that the two-person calibration of distance bounds was not part of the 24-participant sample and that NC’s upper limit is path geometry rather than a contact-constrained maximum.
  3. §3.5: Robot speed capped at 0.5 m/s while participants’ usual gait speed averaged 1.16 m/s; this mismatch is noted anecdotally in Discussion but should be quantified (e.g., mean commanded speed per condition) and discussed as a possible reason some participants found NC/HH “too slow.”
  4. Sample is heavily female (3 male / 24); note this limitation more prominently when generalizing to older adults.
  5. Facial-emotion analysis was abandoned due to image quality (§3.6.4); a brief sentence in Methods or Limitations is sufficient, but the two face cameras can be de-emphasized in the setup description to avoid implying unused data streams.
  6. Complementary preprint [17] is cited for individual-difference analyses; ensure the present manuscript is self-contained on the main preference claim and that any overlapping data are clearly delineated.
  7. Minor language: “ScrAmplitesMean”, “Amplites”, occasional missing hyphens (human–robot), and “the robots forearm” → “the robot’s forearm” in Abstract/conditions list.

Circularity Check

0 steps flagged · score 1.0 of 10

Empirical multimodal user study; measured outcomes (force, distance, EDA/ECG, Likert/PANAS/NARS) are not derived from parameters fitted to the same claim, so no load-bearing circularity.

full rationale

The paper reports a within-subjects experiment with 24 older adults under four contact conditions, collecting independent sensor and questionnaire data. Contact force is estimated from joint torques after gravity compensation; absolute distance is extracted from laser point clouds via DBSCAN/AABB tracking; relative distance is a post-hoc normalization of that measured distance by pose-specific bounds (Table 1); ECG/EDA are baseline-corrected physiological time series; questionnaires are standard Likert/PANAS/NARS items. None of these quantities is obtained by fitting a free parameter to a subset of the preference data and then re-labeling the fit as a prediction. The interpretive step that lower relative distance indicates higher trust (§3.6.3) is an explicit hypothesis, not a definitional identity or a uniqueness theorem imported from the authors’ prior work. The complementary preprint [17] is cited only for individual-difference analyses of the same questionnaires and open answers; the main preference ordering (HH/FC over LA/NC) does not rest on it. No self-definitional loop, fitted-input-as-prediction, or ansatz-smuggled-via-citation appears in the derivation chain. The sole minor self-reference is non-load-bearing, yielding a score of 1 rather than 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

Load-bearing content is almost entirely empirical protocol choices and domain interpretations of biosignals and proximity, not free-fitted physical constants or invented mediators. The main interpretive axioms are that EDA/ECG index stress and that relative proximity indexes trust; free parameters are operational (speed cap, distance bounds, window lengths).

free parameters (3)
  • Robot max speed cap = 0.5 m/s
    Experimenter limited speed to 0.5 m/s for safe teleoperation; affects naturalness ratings and comparison to free walking.
  • Condition-specific distance bounds (Table 1) = NC 0.44–1.50 m; HH 0.71–1.04 m; LA 0.62–0.75 m; FC 0.66–0.95 m
    Lower/upper lateral limits for normalizing absolute distance into relative distance were set by two-person tests (and 1.5 m tape for NC); these bounds directly drive the trust interpretation.
  • HRV / EDA analysis windows = 2 min / 30 s (HRV); 10 s (EDA)
    RMSSD/Pnn50 over 2 min updated every 30 s; SCL/SCR over 10 s; baseline segments chosen by protocol—standard but still analysis choices that affect significance.
assumptions (4)
  • ad hoc to paper Smaller relative human–robot distance indicates higher trust and confidence in the robot.
    Stated as hypothesis in §3.6.3 and used to interpret LA as lower trust; not independently validated in this sample.
  • domain assumption Elevated HR, reduced HRV (RMSSD, Pnn50), and elevated EDA (SCL, SCR count/amplitude) index stress, anxiety, or fear.
    Standard psychophysiology mapping cited via Kreibig and Boucsein; applied to short walking trials with possible activity and temperature confounds.
  • domain assumption Equivalent contact force can be recovered from joint torques via gravity-compensated Jacobian pseudo-inverse at a chosen point of interest.
    Standard rigid-body force estimation; accuracy depends on model, contact location choice (segments 5/6/7), and gravity-only control trials (§3.6.2).
  • domain assumption Healthy, non-frail older adults walking a short straight path under teleoperation are informative for geriatric walking-guidance robot design.
    Inclusion criteria and lab setting (§3.4–3.5); authors note ecological limits in Discussion.
invented entities (1)
  • Relative (normalized) distance metric
    purpose: Make absolute spacing comparable across contact poses with different kinematic reach ranges so proximity can be read as trust.
    Defined in §3.6.3 using Table 1 bounds; useful construct but not an independently measured psychological quantity.

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Cite this review

Pith. "Pith review of Effects of Robotic Touch on Older Users During Walking Guidance by a Humanoid Robot." pith.science (2026). https://pith.science/paper/6NZXXD4P

@misc{pith2026260709323,
  author       = {Pith},
  title        = {Pith review of: Effects of Robotic Touch on Older Users During Walking Guidance by a Humanoid Robot},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6NZXXD4P}},
  note         = {Machine review of arXiv:2607.09323}
}
read the original abstract

The shortage of healthcare staff is a challenge in geriatric care. To address this, robots can be integrated into care settings to provide assistance and emotional support. A promising application is walking guidance, particularly benefiting older adults as navigation skills deteriorate with aging. As walking guidance involves direct contact, the aim of this study is to understand how older adults perceive and respond to different touch modes during guided walking. 24 older adults (68 - 88 yrs.) walked four times a ten-meter trajectory guided by the robot TIAGo Pro in four contact conditions: no physical contact (NC); physical contact through holding the robot's wrist with the hand (HH); physical interaction through linking arms with the robot (LA); and physical contact through resting the forearm on the robots forearm (FC). A multimodal assessment approach included electrocardiogram, electrodermal activity, contact force, distance to robot, and questionnaires. Physiological results reveal a slight increase in stress levels during robot interaction. Behavioural and subjective measures, however, show overall acceptance of robotic touch. The two conditions corresponding to larger interaction forces (HH and FC) were associated with lower relative distances between participant and robot, indicating a higher trust and confidence. Questionnaire responses supported these findings, evidencing greater perceived safety, trust and comfort in these conditions. This study provides insights for the design of robotic walking guidance assistance, indicating that gentle, stable touch is preferred by older adults in comparison to contactless interaction.

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Reference graph

Works this paper leans on

41 extracted references · 16 canonical work pages

  1. [1]

    Statistisches Bundesamt: Statistischer Bericht Pflegekr¨ aftevorausberechnung (2024)

    2024

  2. [2]

    In: Pro- ceedings of the 2022 17th ACM IEEE Inter- national Conference on Human-Robot Inter- action, pp

    Odabasi, C., Graf, F., Lindermayr, J., Patel, M., Baumgarten, S.D., Graf, B.: Refilling Water Bottles in Elderly Care Homes With the Help of a Safe Service Robot. In: Pro- ceedings of the 2022 17th ACM IEEE Inter- national Conference on Human-Robot Inter- action, pp. 101–110. IEEE, Sapporo, Japan (2022)

    2022

  3. [3]

    In: Proceedings of the 2020 CHI Conference on Human Factors in Com- puting Systems

    Carros, F., Meurer, J., L¨ offler, D., Unbe- haun, D., Matthies, S., Koch, I., Wieching, R., Randall, D., Hassenzahl, M., Wulf, V.: Exploring Human-Robot Interaction with the Elderly: Results from a Ten-Week Case Study in a Care Home. In: Proceedings of the 2020 CHI Conference on Human Factors in Com- puting Systems. CHI ’20, pp. 1–12. Associ- ation for C...

    arXiv 2020

  4. [4]

    Adaptive Human Behavior and Physiol- ogy2(4), 344–362 (2016) https://doi.org/10

    Morrison, I.: Keep Calm and Cuddle on: Social Touch as a Stress Buffer. Adaptive Human Behavior and Physiol- ogy2(4), 344–362 (2016) https://doi.org/10. 1007/s40750-016-0052-x

    2016

  5. [5]

    Emotion (Washing- ton, D.C.)6(3), 528–533 (2006) https://doi

    Hertenstein, M.J., Keltner, D., App, B., Bulleit, B.A., Jaskolka, A.R.: Touch commu- nicates distinct emotions. Emotion (Washing- ton, D.C.)6(3), 528–533 (2006) https://doi. org/10.1037/1528-3542.6.3.528

    doi:10.1037/1528-3542.6.3.528 2006

  6. [6]

    Frontiers in Psychiatry11(2020) https: //doi.org/10.3389/fpsyt.2020.555058

    Eckstein, M., Mamaev, I., Ditzen, B., Sailer, U.: Calming Effects of Touch in Human, Animal, and Robotic Interaction—Scientific State-of-the-Art and Technical Advances. Frontiers in Psychiatry11(2020) https: //doi.org/10.3389/fpsyt.2020.555058 . Pub- lisher: Frontiers

    doi:10.3389/fpsyt.2020.555058 2020

  7. [7]

    Efficient Algorithms for Device Placement of DNN Graph Operators

    Wada, K., Shibata, T.: Robot therapy in a care house - its sociopsychological and physiological effects on the residents. In: Pro- ceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., pp. 3966–3971 (2006). https://doi.org/ 10.1109/ROBOT.2006.1642310 . ISSN: 1050- 4729

    work page Pith review arXiv doi:10.1109/robot.2006.1642310 2006

  8. [8]

    International Journal of Social Robotics11(2), 285–304 (2019) https://doi.org/10.1007/s12369-018-0500-9

    Willemse, C.J.A.M., Erp, J.B.F.: Social Touch in Human–Robot Interaction: Robot-Initiated Touches can Induce Positive Responses without Extensive Prior Bonding. International Journal of Social Robotics11(2), 285–304 (2019) https://doi.org/10.1007/s12369-018-0500-9

    work page doi:10.1007/s12369-018-0500-9 2019

Show all 41 references
  1. [9]

    1007/s12369-019-00542-x

    Fitter, N.T., Kuchenbecker, K.J.: How Does It Feel to Clap Hands with a Robot? International Journal of Social Robotics 16 12(1), 113–127 (2020) https://doi.org/10. 1007/s12369-019-00542-x

    2020

  2. [10]

    In: 2023 32nd IEEE Inter- national Conference on Robot and Human Interactive Communication (RO-MAN), pp

    Nakane, A., Yanokura, I., Ichikura, A., Okada, K., Inaba, M.: Development of Robot Guidance System Using Hand-holding with Human and Measurement of Psychologi- cal Security. In: 2023 32nd IEEE Inter- national Conference on Robot and Human Interactive Communication (RO-MAN), pp...

    2023

  3. [11]

    Jour- nal of Robotics and Mechatronics32(1), 8–20 (2020) https://doi.org/10.20965/jrm

    Hieida, C., Abe, K., Nagai, T., Omori, T.: Walking Hand-in-Hand Helps Relationship Building Between Child and Robot. Jour- nal of Robotics and Mechatronics32(1), 8–20 (2020) https://doi.org/10.20965/jrm. 2020.p0008

    2020 doi

  4. [12]

    Clinical Interventions in Aging 9, 801–811 (2014) https://doi.org/10.2147/ CIA.S56435

    Wu, Y.-H., Wrobel, J., Cornuet, M., Ker- herv´ e, H., Damn´ ee, S., Rigaud, A.-S.: Accep- tance of an assistive robot in older adults: a mixed-method study of human-robot inter- action over a 1-month period in the Living Lab setting. Clinical Interventions in Aging 9, 801–811 ...

    2014

  5. [13]

    IEEE (2016)

    Piezzo, C., Suzuki, K.: Design of an accom- panying humanoid as a walking trainer for the elderly. IEEE (2016). https://doi.org/10. 1109/ROMAN.2016.7745160

    2016

  6. [14]

    Moffat, S.D.: Aging and spatial navigation: what do we know and where do we go? Neu- ropsychology review19(4), 478–489 (2009) https://doi.org/10.1007/s11065-009-9120-3

    2009 doi

  7. [15]

    Journal of Hospital Medicine5(2), 69– 75 (2010) https://doi.org/10.1002/jhm.589

    Boustani, M., Baker, M.S., Campbell, N., Munger, S., Hui, S.L., Castelluccio, P., Far- ber, M., Guzman, O., Ademuyiwa, A., Miller, D., Callahan, C.: Impact and recognition of cognitive impairment among hospitalized elders. Journal of Hospital Medicine5(2), 69– 75 (2010) https:...

    2010 doi

  8. [16]

    BMC geri- atrics16(1), 154 (2016) https://doi.org/10

    Bj¨ ork, S., Juthberg, C., Lindkvist, M., Wimo, A., Sandman, P.-O., Winblad, B., Edvardsson, D.: Exploring the prevalence and variance of cognitive impairment, pain, neuropsychiatric symptoms and adl depen- dency among persons living in nursing homes; a cross-sectional study. ...

    2016

  9. [17]

    PREPRINT (Version 1) available at Research Square (2026) https://doi.org/10.21203/rs.3

    Eckstein, M., Mayer, C.J., Schmetterer, M., Leven, L., Buchner, T., Ackermann, M., Werner, C., Mombaur, K., Sailer, U.: Indi- vidual differences in touch attitudes shape acceptance of robotic touch in older adults: Evidence from a medical center setting. PREPRINT (Version 1) a...

    2026 doi

  10. [18]

    Robotics11(6), 127 (2022) https://doi.org/10.3390/robotics11060127

    Asgharian, P., Panchea, A.M., Ferland, F.: A Review on the Use of Mobile Service Robots in Elderly Care. Robotics11(6), 127 (2022) https://doi.org/10.3390/robotics11060127

    2022 doi

  11. [19]

    In: IECON ’98

    Schraft, R.D., Schaeffer, C., May, T.: Care- O-bot/sup TM/: the concept of a sys- tem for assisting elderly or disabled per- sons in home environments. In: IECON ’98. Proceedings of the 24th Annual Con- ference of the IEEE Industrial Electronics Society (Cat. No.98CH36200), vo...

    1998

  12. [20]

    (ed.) Care-O-bot®3 – Vision of a Robot Butler, pp

    Reiser, U., Jacobs, T., Arbeiter, G., Par- litz, C., Dautenhahn, K.: In: Trappl, R. (ed.) Care-O-bot®3 – Vision of a Robot Butler, pp. 97–116. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/ 978-3-642-37346-6 9

    2013

  13. [21]

    Yasuto, , K

    Yoshimitsu, K., , S. Yasuto, , K. Etsuko, , K. Kenta, , K. Kaori, , H. Kaoru, , M. Yoshihiro, , Masamune, K.: Exploring the potential of service robots in guiding patients and assist- ing mobility in medical facilities. Advanced Robotics38(24), 1758–1769 (2024) https:// doi.or...

    2024 doi

  14. [22]

    In: Eighteenth National Conference on Artificial Intelli- gence, pp

    Montemerlo, M., Pineau, J., Roy, N., Thrun, S., Verma, V.: Experiences with a mobile robotic guide for the elderly. In: Eighteenth National Conference on Artificial Intelli- gence, pp. 587–592. American Association for Artificial Intelligence, USA (2002) 17

    2002

  15. [23]

    In: AAAI Workshop on Automation as Eldercare, vol

    Pollack, M.E., Brown, L., Colbry, D., Orosz, C., Peintner, B., Ramakrishnan, S., Eng- berg, S., Matthews, J.T., Dunbar-Jacob, J., McCarthy, C.E.,et al.: Pearl: A mobile robotic assistant for the elderly. In: AAAI Workshop on Automation as Eldercare, vol. 2002 (2002). AAAI Pres...

    2002

  16. [24]

    Journal of Personal- ity and Social Psychology43(3), 450 (1982) https://doi.org/10.1037/0022-3514.43.3.450

    Ryan, R.M.: Control and information in the intrapersonal sphere: An extension of cogni- tive evaluation theory. Journal of Personal- ity and Social Psychology43(3), 450 (1982) https://doi.org/10.1037/0022-3514.43.3.450

    1982 doi

  17. [25]

    https: //pal-robotics.com/robot/tiago-pro/

    PAL Robotics: TIAGo Pro. https: //pal-robotics.com/robot/tiago-pro/. Accessed: 2026-01-29 (2024)

    2026

  18. [26]

    Mini-mental state

    Folstein, M.F., Folstein, S.E., McHugh, P.R.: “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 12(3), 189–198 (1975) https://doi.org/10. 1016/0022-3956(75)90026-6

    1975

  19. [27]

    Interaction Studies7(3), 437–454 (2006) https://doi.org/10.1075/is.7.3.14nom

    Nomura, T., Suzuki, T., Kanda, T., Kato, K.: Measurement of negative attitudes toward robots. Interaction Studies7(3), 437–454 (2006) https://doi.org/10.1075/is.7.3.14nom

    2006 doi

  20. [28]

    Computers in Human Behavior18(3), 315–325 (2002) https: //doi.org/10.1016/S0747-5632(01)00039-5

    Gaudron, J.-P., Vignoli, E.: Assessing computer anxiety with the interac- tion model of anxiety: development and validation of the computer anxi- ety trait subscale. Computers in Human Behavior18(3), 315–325 (2002) https: //doi.org/10.1016/S0747-5632(01)00039-5

    2002 doi

  21. [29]

    Institution: Ameri- can Psychological Association (2018)

    Wilhelm, F.H., Kochar, A.S., Roth, W.T., Gross, J.J.: Social Touch Questionnaire. Institution: Ameri- can Psychological Association (2018). https://doi.org/10.1037/t67888-000

    2018 doi

  22. [30]

    Respiratory Care59(4), 531–537 (2014) https://doi.org/ 10.4187/respcare.02688

    Karpman, C., LeBrasseur, N.K., DePew, Z.S., Novotny, P.J., Benzo, R.P.: Measur- ing Gait Speed in the Out-Patient Clinic: Methodology and Feasibility. Respiratory Care59(4), 531–537 (2014) https://doi.org/ 10.4187/respcare.02688

    2014 doi

  23. [31]

    Journal of Personality and Social Psy- chology54(6), 1063–1070 (1988) https://doi

    Watson, D., Clark, L.A., Tellegen, A.: Devel- opment and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psy- chology54(6), 1063–1070 (1988) https://doi. org/10.1037/0022-3514.54.6.1063

    1988 doi

  24. [32]

    Biological Psy- chology84(3), 394–421 (2010) https://doi

    Kreibig, S.D.: Autonomic nervous system activity in emotion: a review. Biological Psy- chology84(3), 394–421 (2010) https://doi. org/10.1016/j.biopsycho.2010.03.010

    2010 doi

  25. [33]

    Springer, Boston, MA (2012)

    Boucsein, W.: Electrodermal Activ- ity. Springer, Boston, MA (2012). https://doi.org/10.1007/978-1-4614-1126-0

    2012 doi

  26. [34]

    Applied Sciences12(2), 807 (2022) https://doi.org/10.3390/app12020807

    Xiao, H., Li, W., Zeng, G., Wu, Y., Xue, J., Zhang, J., Li, C., Guo, G.: On-Road Driver Emotion Recognition Using Facial Expres- sion. Applied Sciences12(2), 807 (2022) https://doi.org/10.3390/app12020807

    2022 doi

  27. [35]

    Behavioral and eval- uative consequences of non-functional touch from a robot

    Hoffmann, L., Kr¨ amer, N.C.: The persuasive power of robot touch. Behavioral and eval- uative consequences of non-functional touch from a robot. PLOS ONE16(5), 0249554 (2021) https://doi.org/10.1371/journal.pone. 0249554

    2021 doi

  28. [36]

    International Journal of Social Robotics16(3), 619–634 (2024) https: //doi.org/10.1007/s12369-024-01110-8

    Guo, F., Fang, C., Li, M., Ren, Z., Zhang, Z.: How do Robot Touch Characteristics Impact Users’ Emotional Responses: Evidence from ECG and fNIRS. International Journal of Social Robotics16(3), 619–634 (2024) https: //doi.org/10.1007/s12369-024-01110-8

    2024 doi

  29. [37]

    preprint (2026)

    Yamamoto, H., Mayer, C.J., Raithel, C., Buchner, T., Werner, C., Hirata, Y., Eck- stein, M., Mombaur, K.: Perception of Social Robots as Communication Partners in Healthcare for Older Adults. preprint (2026). https://arxiv.org/abs/2605.21053

    2026 arXiv

  30. [38]

    Available at SSRN 6621561 (2026)

    Mayer, C.J., Raithel, C., Yamamoto, H., Buchner, T., Iwan, B.S.V., Tempel, A., Staatz, E., Schommer, F., Schmetterer, M., Misok, D., et al.: Trust, stress, and oxy- tocin: Psychophysiological responses of older adults to social robot interactions. Available at SSRN 6621561 (2026)

    2026

  31. [39]

    International Journal of Social Robotics13(7), 1657–1677 (2021) https:// doi.org/10.1007/s12369-021-00749-x

    Zhou, Y., Kornher, T., Mohnke, J., Fischer, 18 M.H.: Tactile Interaction with a Humanoid Robot: Effects on Physiology and Subjective Impressions. International Journal of Social Robotics13(7), 1657–1677 (2021) https:// doi.org/10.1007/s12369-021-00749-x

    2021 doi

  32. [40]

    Journal of the American College of Cardiology31(3), 593–601 (1998) https: //doi.org/10.1016/S0735-1097(97)00554-8

    Umetani, K., Singer, D.H., McCraty, R., Atkinson, M.: Twenty-Four Hour Time Domain Heart Rate Variability and Heart Rate: Relations to Age and Gender Over Nine Decades. Journal of the American College of Cardiology31(3), 593–601 (1998) https: //doi.org/10.1016/S0735-1097(97)00554-8

    1998 doi

  33. [41]

    In: Pfitzner, D.W., Salmon, J.K

    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discover- ing Clusters in Large Spatial Databases with Noise. In: Pfitzner, D.W., Salmon, J.K. (eds.) Second International Conference on Knowl- edge Discovery and Data Mining (KDD’96). Proceedings of ...

    1996

Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.