Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It
Abstract
:1. Introduction
Paper Structure
- The background (Section 2) outlines how BCIs and games are used in stroke rehabilitation, motivating the need for game help.
- Study 1 (Section 3) describes the procedure and results of a study game that helps 19 stroke patients.
- Study 2 (Section 4) presents a follow-up study with 20 healthy subjects based on findings from Study 1 and explores 20 additional experiential measures of game help.
- The discussion (Section 5) reflects on the protocol design evolution, clinical implications, and how the work paves the way for future studies of game help.
2. Background
2.1. Rehabilitation with Brain–Computer Interfaces
2.2. Games for Rehabilitation
2.3. Game Help: Improving Perceived Control and Frustration
2.4. Performance Accommodation Mechanisms as Game Help
2.5. Methodology: Simulating the BCI to Study Game Help
Blinking: A Proxy to Simulate Motor Imagery BCI
2.6. Summary
3. Study: Stroke Patients’ Experience of Game Help
3.1. Participants
3.2. Procedure
3.3. Apparatus: Fishing Game
Fishing Game Help
3.4. Data Analysis
3.5. Results
3.5.1. Perceived Control
3.5.2. Frustration
3.6. Study Discussion
3.6.1. Help Preferences and Perceived Control
3.6.2. Help Preferences and Frustration
3.6.3. Blink Sensor Reliability with Stroke Patients
3.6.4. Study Procedure with Stroke Patients
3.6.5. Augmented Success Design Questions
3.6.6. Limitations
4. Follow-Up Exploration: Refining Experiential Measures of Help
4.1. Measurement Design
- Help Quantity: Users’ experienced quantity of help. Different help styles may have felt like receiving more or less help, e.g., P10’s “I didn’t really notice the clamp” (mitigated failure) or P17’s “It was like getting a small break when she came and helped” (input override).
- Help Appeal: How much did users appreciate the help they received? This is evident from the contrasting patient responses, like, for example, P10’s “It was nice that she helped” or P8’s “She shouldn’t interfere in the game”.
- Pacing: Do users experience that help affects the game’s pacing? This is inspired by P17’s “It was like getting a small break when she came and helped” and P6’s “It felt like it went slower, but I still caught many fish”. We explored whether the help types affected people’s sense of pacing: we did not expect particularly high psychological immersion effects from the game’s simple narrative [68], but we wanted to see whether players picked up on the variations in gameplay duration initially observed in the stroke patient study.
- Irritation: Do users wish to distinguish frustration and irritation when rating help? This is inspired by responses to frustration like P2’s “Fishing [and losing fish in a game] is not something that annoys me” or P7’s “It’s not something that affects me”.
- Attribution: Are users blaming the system or themselves for the poor performance? For example, this is evident from patients like P18’s “I lost way more fish this time” (emphasising themselves) and P16’s “It won’t do it” (emphasizing the system).
- Self-performance: Did users consider themselves good at playing the game? This is inspired by P5’s “It’s fun! But also a bit hard…”.
- Style: Did users enjoy the way the game was styled? E.g., P18’s comment: “It feels a bit silly”.
- Joy: Did users enjoy playing this game? E.g., P19’s comment: “It was pretty fun when I finally understood how to play” or P11’s “Repetitive”.
4.2. Participants
4.3. Procedure
4.4. Apparatus
4.5. Data Analysis
4.6. Results
4.6.1. Perceived Control and Frustration
4.6.2. Help Quantity and Help Appeal
4.6.3. Pacing and Irritation
4.6.4. Agreement with Patient Viewpoints
4.6.5. Experiment-Wide Measurements
4.7. Interview Analysis
4.7.1. Mitigated Failure
4.7.2. Augmented Success
4.7.3. Input Override
5. Discussion
5.1. Study Protocol Design
5.2. New Self-Report Measures
5.3. Clinical Implications of Findings
6. Conclusions
Future Work
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Feigin, V.L.; Stark, B.A.; Johnson, C.O.; Roth, G.A.; Bisignano, C.; Abady, G.G.; Hamidi, S. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021, 20, 795–820. [Google Scholar] [CrossRef]
- Dobkin, B.H. Rehabilitation after Stroke. N. Engl. J. Med. 2005, 352, 1677–1684. [Google Scholar] [CrossRef]
- Pichiorri, F.; Morone, G.; Petti, M.; Toppi, J.; Pisotta, I.; Molinari, M.; Paolucci, S.; Inghilleri, M.; Astolfi, L.; Cincotti, F.; et al. Brain–computer interface boosts motor imagery practice during stroke recovery. Ann. Neurol. 2015, 77, 851–865. [Google Scholar] [CrossRef]
- Jeunet, C.; N’Kaoua, B.; Lotte, F. Chapter 1—Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates. Prog. Brain Res. 2016, 228, 3–35. [Google Scholar] [CrossRef]
- Annema, J.H.; Verstraete, M.; Vanden Abeele, V.; Desmet, S.; Geerts, D. Videogames in therapy: A therapist’s perspective. In Proceedings of the 3rd International Conference on Fun and Games, Fun and Games ’10, Leuven, Belgium, 15–17 September 2010; pp. 94–98. [Google Scholar] [CrossRef]
- Putnam, C.; Cheng, J.; Seymour, G. Therapist Perspectives: Wii Active Videogames Use in Inpatient Settings with People Who Have Had a Brain Injury. Games Health J. 2014, 3, 366–370. [Google Scholar] [CrossRef]
- Grissmann, S.; Zander, T.O.; Faller, J.; Brönstrup, J.; Kelava, A.; Gramann, K.; Gerjets, P. Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces. Front. Hum. Neurosci. 2017, 11, 370. [Google Scholar] [CrossRef]
- Zander, T.O.; Kothe, C. Towards passive brain-computer interfaces: Applying brain-computer interface technology to human-machine systems in general. J. Neural Eng. 2011, 8, 025005. [Google Scholar] [CrossRef]
- Nijholt, A.; Bos, D.P.O.; Reuderink, B. Turning shortcomings into challenges: Brain–computer interfaces for games. Entertain. Comput. 2009, 1, 85–94. [Google Scholar] [CrossRef]
- Hunicke, R. The Case for Dynamic Difficulty Adjustment in Games. In Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, ACE ’05, Valencia, Spain, 15–17 June 2005; pp. 429–433. [Google Scholar] [CrossRef]
- Goršič, M.; Darzi, A.; Novak, D. Comparison of two difficulty adaptation strategies for competitive arm rehabilitation exercises. In Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK, 17–20 July 2017; pp. 640–645. [Google Scholar] [CrossRef]
- Rossau, I.G.; Skammelsen, R.B.; Czapla, J.J.; Hougaard, B.I.; Knoche, H.; Jochumsen, M. How can we help? Towards a design framework for performance-accommodation mechanisms for users struggling with input. In Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play; Association for Computing Machinery: New York, NY, USA, 2021; pp. 10–16. [Google Scholar] [CrossRef]
- Teixeira, P.J.; Carraça, E.V.; Markland, D.; Silva, M.N.; Ryan, R.M. Exercise, physical activity, and self-determination theory: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2012, 9, 78. [Google Scholar] [CrossRef]
- Kusec, A.; Velikonja, D.; DeMatteo, C.; Harris, J.E. Motivation in rehabilitation and acquired brain injury: Can theory help us understand it? Disabil. Rehabil. 2019, 41, 2343–2349. [Google Scholar] [CrossRef]
- World Health Organization. Adherence to Long-Term Therapies: Evidence for Action; Technical Report; World Health Organization: Geneva, Switzerland, 2003. [Google Scholar]
- Grindley, E.J.; Zizzi, S.J. Using a Multidimensional Approach to Predict Motivation and Adherence to Rehabilitation in Older Adults. Top. Geriatr. Rehabil. 2005, 21, 182–193. [Google Scholar] [CrossRef]
- Feingold-Polak, R.; Barzel, O.; Levy-Tzedek, S. A robot goes to rehab: A novel gamified system for long-term stroke rehabilitation using a socially assistive robot—Methodology and usability testing. J. NeuroEng. Rehabil. 2021, 18, 122. [Google Scholar] [CrossRef]
- Cervera, M.A.; Soekadar, S.R.; Ushiba, J.; Millán, J.D.R.; Liu, M.; Birbaumer, N.; Garipelli, G. Brain-computer interfaces for post-stroke motor rehabilitation: A meta-analysis. Ann. Clin. Transl. Neurol. 2018, 5, 651–663. [Google Scholar] [CrossRef] [PubMed]
- Xue, S.; Wu, M.; Kolen, J.; Aghdaie, N.; Zaman, K.A. Dynamic Difficulty Adjustment for Maximized Engagement in Digital Games. In Proceedings of the 26th International Conference on World Wide Web Companion, Republic and Canton of Geneva, CHE, WWW ’17 Companion, Perth, Australia, 3–7 April 2017; pp. 465–471. [Google Scholar] [CrossRef]
- Niazi, I.K.; Navid, M.S.; Rashid, U.; Amjad, I.; Olsen, S.; Haavik, H.; Alder, G.; Kumari, N.; Signal, N.; Taylor, D.; et al. Associative cued asynchronous BCI induces cortical plasticity in stroke patients. Ann. Clin. Transl. Neurol. 2022, 9, 722–733. [Google Scholar] [CrossRef]
- Ibáñez, J.; Serrano, J.I.; Castillo, M.D.d.; Monge-Pereira, E.; Molina-Rueda, F.; Alguacil-Diego, I.; Pons, J.L. Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials. J. Neural Eng. 2014, 11, 056009. [Google Scholar] [CrossRef]
- Xu, R.; Jiang, N.; Mrachacz-Kersting, N.; Lin, C.; Asín Prieto, G.; Moreno, J.C.; Pons, J.L.; Dremstrup, K.; Farina, D. A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity. IEEE Trans. Bio-Med. Eng. 2014, 61, 2092–2101. [Google Scholar] [CrossRef]
- Bessière, K.; Newhagen, J.E.; Robinson, J.P.; Schneiderman, B. A model for computer frustration: The role of instrumental and dispositional factors on incident, session, and post-session frustration and mood. Comput. Hum. Behav. 2006, 22, 941–961. [Google Scholar] [CrossRef]
- Voznenko, T.I.; Urvanov, G.A.; Dyumin, A.A.; Andrianova, S.V.; Chepin, E.V. The Research of Emotional State Influence on Quality of a Brain-Computer Interface Usage. Procedia Comput. Sci. 2016, 88, 391–396. [Google Scholar] [CrossRef]
- Evain, A.; Argelaguet, F.; Strock, A.; Roussel, N.; Casiez, G.; Lécuyer, A. Influence of Error Rate on Frustration of BCI Users. In Proceedings of the International Working Conference on Advanced Visual Interfaces, Bari, Italy, 7–10 June 2016; pp. 248–251. [Google Scholar] [CrossRef]
- Wen, W. Does delay in feedback diminish sense of agency? A review. Conscious. Cogn. 2019, 73, 102759. [Google Scholar] [CrossRef]
- Cornelio, P.; Haggard, P.; Hornbaek, K.; Georgiou, O.; Bergström, J.; Subramanian, S.; Obrist, M. The sense of agency in emerging technologies for human–computer integration: A review. Front. Neurosci. 2022, 16, 949138. [Google Scholar] [CrossRef]
- Chavarriaga, R.; Fried-Oken, M.; Kleih, S.; Lotte, F.; Scherer, R. Heading for new shores! Overcoming pitfalls in BCI design. Brain-Comput. Interfaces 2017, 4, 60–73. [Google Scholar] [CrossRef] [PubMed]
- Bonnechère, B.; Jansen, B.; Omelina, L.; Van Sint Jan, S. The use of commercial video games in rehabilitation: A systematic review. Int. J. Rehabil. Res. 2016, 39, 277–290. [Google Scholar] [CrossRef]
- Molina, K.I.; Ricci, N.A.; de Moraes, S.A.; Perracini, M.R. Virtual reality using games for improving physical functioning in older adults: A systematic review. J. NeuroEng. Rehabil. 2014, 11, 156. [Google Scholar] [CrossRef] [PubMed]
- Barrett, N.; Swain, I.; Gatzidis, C.; Mecheraoui, C. The use and effect of video game design theory in the creation of game-based systems for upper limb stroke rehabilitation. J. Rehabil. Assist. Technol. Eng. 2016, 3, 2055668316643644. [Google Scholar] [CrossRef]
- Flores, E.; Tobon, G.; Cavallaro, E.; Cavallaro, F.I.; Perry, J.C.; Keller, T. Improving patient motivation in game development for motor deficit rehabilitation. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology, ACE ’08, Yokohama, Japan, 3–5 December 2008; pp. 381–384. [Google Scholar] [CrossRef]
- Salisbury, J.P.; Aronson, T.M.; Simon, T.J. At-Home Self-Administration of an Immersive Virtual Reality Therapeutic Game for Post-Stroke Upper Limb Rehabilitation. In Proceedings of the Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play, CHI PLAY ’20, Virtual, 2–4 November 2020; pp. 114–121. [Google Scholar] [CrossRef]
- Reis, E.; Postolache, G.; Teixeira, L.; Arriaga, P.; Lima, M.L.; Postolache, O. Exergames for motor rehabilitation in older adults: An umbrella review. Phys. Ther. Rev. 2019, 24, 84–99. [Google Scholar] [CrossRef]
- Alankus, G.; Lazar, A.; May, M.; Kelleher, C. Towards customizable games for stroke rehabilitation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, Atlanta, GR, USA, 10–15 April 2010; pp. 2113–2122. [Google Scholar] [CrossRef]
- Aufheimer, M.; Gerling, K.; Graham, T.N.; Naaris, M.; Konings, M.J.; Monbaliu, E.; Hallez, H.; Ortibus, E. An Examination of Motivation in Physical Therapy Through the Lens of Self-Determination Theory: Implications for Game Design. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI ’23, Hamburg, Germany, 23–28 April 2023; pp. 1–16. [Google Scholar] [CrossRef]
- Fruchter, D.; Feingold Polak, R.; Berman, S.; Levy-Tzedek, S. Hierarchy in Algorithm-Based Feedback to Patients Working with a Robotic Rehabilitation System: Toward User-Experience Optimization. IEEE Trans. Hum.-Mach. Syst. 2022, 52, 907–917. [Google Scholar] [CrossRef]
- O’Loughlin, E.K.; Barnett, T.A.; McGrath, J.J.; Consalvo, M.; Kakinami, L. Factors Associated with Sustained Exergaming: Longitudinal Investigation. JMIR Serious Games 2019, 7, e13335. [Google Scholar] [CrossRef]
- Cerasoli, C.P.; Nicklin, J.M.; Ford, M.T. Intrinsic motivation and extrinsic incentives jointly predict performance: A 40-year meta-analysis. Psychol. Bull. 2014, 140, 980–1008. [Google Scholar] [CrossRef]
- Skjæret, N.; Nawaz, A.; Morat, T.; Schoene, D.; Helbostad, J.L.; Vereijken, B. Exercise and rehabilitation delivered through exergames in older adults: An integrative review of technologies, safety and efficacy. Int. J. Med. Inform. 2016, 85, 1–16. [Google Scholar] [CrossRef]
- Castillo, J.F.V.; Vega, M.F.M.; Cardona, J.E.M.; Lopez, D.; Quiñones, L.; Gallo, O.A.H.; Lopez, J.F. Design of Virtual Reality Exergames for Upper Limb Stroke Rehabilitation Following Iterative Design Methods: Usability Study. JMIR Serious Games 2024, 12, e48900. [Google Scholar] [CrossRef]
- Lanzoni, D.; Vitali, A.; Regazzoni, D.; Rizzi, C. Design of Customized Virtual Reality Serious Games for the Cognitive Rehabilitation of Retrograde Amnesia After Brain Stroke. J. Comput. Inf. Sci. Eng. 2021, 22, 031009. [Google Scholar] [CrossRef]
- Paraschos, P.D.; Koulouriotis, D.E. Game Difficulty Adaptation and Experience Personalization: A Literature Review. Int. J. Hum.-Comput. Interact. 2023, 39, 1–22. [Google Scholar] [CrossRef]
- Novak, V.D.; Hass, D.; Hossain, M.S.; Sowers, A.F.; Clapp, J.D. Effects of adaptation accuracy and magnitude in affect-aware difficulty adaptation for the multi-attribute task battery. Int. J. Hum.-Comput. Stud. 2024, 183, 103180. [Google Scholar] [CrossRef]
- Cimolino, G.; Chen, R.X.; Gutwin, C.; Graham, T.N. Automation Confusion: A Grounded Theory of Non-Gamers’ Confusion in Partially Automated Action Games. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI ’23, Hamburg, Germany, 23–28 April 2023; pp. 1–19. [Google Scholar] [CrossRef]
- Denisova, A.; Cairns, P. Adaptation in Digital Games: The Effect of Challenge Adjustment on Player Performance and Experience. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, CHI PLAY ’15, London, UK, 5–7 October 2015; pp. 97–101. [Google Scholar] [CrossRef]
- Cimolino, G.; Askari, S.; Graham, T.N. The Role of Partial Automation in Increasing the Accessibility of Digital Games. Proc. ACM Hum.-Comput. Interact. 2021, 5, 1–30. [Google Scholar] [CrossRef]
- Hougaard, B.I.; Rossau, I.G.; Czapla, J.J.; Miko, M.A.; Bugge Skammelsen, R.; Knoche, H.; Jochumsen, M. Who Willed It? Decreasing Frustration by Manipulating Perceived Control through Fabricated Input for Stroke Rehabilitation BCI Games. Proc. Annu. Symp. Comput.-Hum. Interact. Play 2021, 5, 1–19. [Google Scholar] [CrossRef]
- van de Laar, B.; Bos, D.P.O.; Reuderink, B.; Poel, M.; Nijholt, A. How Much Control Is Enough? Influence of Unreliable Input on User Experience. IEEE Trans. Cybern. 2013, 43, 1584–1592. [Google Scholar] [CrossRef]
- Greville, W.J.; Buehner, M.J. Temporal predictability facilitates causal learning. J. Exp. Psychol. Gen. 2010, 139, 756–771. [Google Scholar] [CrossRef]
- Bos, D.P.O.; Laar, B.L.A.v.d.; Reuderink, B.; Poel, M.; Nijholt, A. Perception and manipulation of game control. In Proceedings of the 6th International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2014, Chicago, IL, USA, 9–11 July 2014; pp. 57–66. [Google Scholar] [CrossRef]
- Jochumsen, M.; Hougaard, B.I.; Kristensen, M.S.; Knoche, H. Implementing Performance Accommodation Mechanisms in Online BCI for Stroke Rehabilitation: A Study on Perceived Control and Frustration. Sensors 2022, 22, 9051. [Google Scholar] [CrossRef] [PubMed]
- Jochumsen, M.; Khan Niazi, I.; Samran Navid, M.; Nabeel Anwar, M.; Farina, D.; Dremstrup, K. Online multi-class brain-computer interface for detection and classification of lower limb movement intentions and kinetics for stroke rehabilitation. Brain-Comput. Interfaces 2015, 2, 202–210. [Google Scholar] [CrossRef]
- Hougaard, B.I.; Knoche, H.; Kristensen, M.S.; Jochumsen, M. Modulating Frustration and Agency using Fabricated Input for Motor Imagery BCIs in Stroke Rehabilitation. IEEE Access 2022, 10, 72312–72327. [Google Scholar] [CrossRef]
- Fard, P.; Grosse-Wentrup, M. The Influence of Decoding Accuracy on Perceived Control: A Simulated BCI Study. arXiv 2014, arXiv:1410.6752. [Google Scholar]
- McCrea, S.; Geršak, G.; Novak, D. Absolute and Relative User Perception of Classification Accuracy in an Affective Video Game. Interact. Comput. 2017, 29, 271–286. [Google Scholar] [CrossRef]
- Katsuragawa, K.; Kamal, A.; Lank, E. Effect of Motion-Gesture Recognizer Error Pattern on User Workload and Behavior. In Proceedings of the 22nd International Conference on Intelligent User Interfaces, IUI ’17, Limassol, Cyprus, 13–16 March 2017; pp. 439–449. [Google Scholar] [CrossRef]
- Cardona-Rivera, J.P.; Zagal, R.E.; Debus, M.S. A Typology of Imperative Game Goals. Game Stud. 2020, 20, 1. [Google Scholar]
- Hougaard, B.I.; Knoche, H. Aiming, Pointing, Steering: A Core Task Analysis Framework for Gameplay. Proc. ACM Hum.-Comput. Interact. 2024, 8, 49. [Google Scholar] [CrossRef]
- Biasiucci, A.; Leeb, R.; Iturrate, I.; Perdikis, S.; Al-Khodairy, A.; Corbet, T.; Schnider, A.; Schmidlin, T.; Zhang, H.; Bassolino, M.; et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 2018, 9, 2421. [Google Scholar] [CrossRef] [PubMed]
- Christensen, R.H.B. Ordinal—Regression Models for Ordinal Data. R Package Version. 2015. Available online: https://cran.r-project.org/package=ordinal (accessed on 6 March 2025).
- Taylor, S.J.; Bogdan, R.; DeVault, M. Introduction to Qualitative Research Methods: A Guidebook and Resource, 4th ed.; Wiley: Hoboken, NJ, USA, 2015. [Google Scholar]
- Hougaard, B.I.; Knoche, H. Telling the story right: How therapists aid stroke patients interpret personal visualized game performance data. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, Trento, Italy, 20–23 May 2019; pp. 435–443. [Google Scholar] [CrossRef]
- Sarsenbayeva, Z.; Van Berkel, N.; Velloso, E.; Goncalves, J.; Kostakos, V. Methodological Standards in Accessibility Research on Motor Impairments: A Survey. ACM Comput. Surv. 2022, 55, 1–35. [Google Scholar] [CrossRef]
- Gundry, D.; Deterding, S. Validity Threats in Quantitative Data Collection with Games: A Narrative Survey. Simul. Gaming 2019, 50, 302–328. [Google Scholar] [CrossRef]
- Yelnik, A.P.; Lebreton, F.O.; Bonan, I.V.; Colle, F.M.; Meurin, F.A.; Guichard, J.P.; Vicaut, E. Perception of Verticality After Recent Cerebral Hemispheric Stroke. Stroke 2002, 33, 2247–2253. [Google Scholar] [CrossRef]
- Ang, D.; Mitchell, A. Comparing Effects of Dynamic Difficulty Adjustment Systems on Video Game Experience. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play, CHI PLAY ’17, Amsterdam, The Netherlands, 15–18 October 2017; pp. 317–327. [Google Scholar] [CrossRef]
- McMahan, A. Immersion, Engagement, and Presence: A Method for Analyzing 3-D Video Games; Routledge: London, UK, 2004; pp. 1–20. [Google Scholar]
- Koo, T.K.; Li, M.Y. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J. Chiropr. Med. 2016, 15, 155–163. [Google Scholar] [CrossRef]
- Darzi, A.; McCrea, S.M.; Novak, D. User Experience with Dynamic Difficulty Adjustment Methods for an Affective Exergame: Comparative Laboratory-Based Study. JMIR Serious Games 2021, 9, e25771. [Google Scholar] [CrossRef]
- Frommel, J.; Fischbach, F.; Rogers, K.; Weber, M. Emotion-based Dynamic Difficulty Adjustment Using Parameterized Difficulty and Self-Reports of Emotion. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, CHI PLAY ’18, Melbourne, VIC, Australia, 28–31 October 2018; pp. 163–171. [Google Scholar] [CrossRef]
- Guo, Z.; Ren, X. An exploratory study of the relationship between objective game difficulty and subjective game difficulty. Int. J. Hum. Comput. Stud. 2025, 199, 103502. [Google Scholar] [CrossRef]
- Sakaue, S.; Kimura, T.; NISHINO, H. Reducing Objective Difficulty Without Influencing Subjective Difficulty in a Video Game. In Proceedings of the 5th ACM International Conference on Multimedia in Asia, MMAsia ’23, Tainan, Taiwan, 6–8 December 2024; pp. 1–5. [Google Scholar] [CrossRef]
- Flint, A.; Denisova, A.; Bowman, N. Comparing Measures of Perceived Challenge and Demand in Video Games: Exploring the Conceptual Dimensions of CORGIS and VGDS. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI ’23, Hamburg, Germany, 23–28 April 2023; pp. 1–19. [Google Scholar] [CrossRef]
Self-Report Measures | Study Goal | Question |
---|---|---|
Perceived control (7-point scale) | Whether help types affected people’s perceived control. | “I felt I was in control of the fisherman reeling in the fish.” [52] (highly disagree→highly agree) |
Frustration (7-point scale) | Whether help types affected people’s frustration. | “How much frustration did you feel in this condition?” [52] (not at all→very much) |
Game Measures | Description | |
Blink Conv. Rate | The percentage of blinks that resulted in reeling up a fish compared to the total number of blinks across all trials. Participants performed one or more blinks during each trial until either successful or until the black cursor had left the green area. | |
Blink Recognition | Blink sensor reliability in % was computed by determining whether each trial contained an occurrence of at least one real blink (superfluous blinks were discarded in negative feedback situations). Blink recognition less than 100% indicates sensor reliability issues or that participants were not blinking as instructed in one or more trials. | |
Positive Feedback | % of fish reeling attempts that resulted in positive feedback (reeling/catching a fish as a result of blinking or help). | |
Help Rate | % of fish reeling attempts, which resulted in help feedback (augmented success, input override, mitigated failure). | |
Fish Caught | A count of fish successfully reeled all the way up to the fisherman. | |
Fish Reel/Unreel | A count of fish reeled up or down one level. | |
Fish Lost | A count of fish which managed to escape (requires three unreels). | |
Duration | The duration of playing the game once. | |
Qualitative Measures | Description | |
Video | Screen recording combined with webcam recording of players. Used by the experimenters to monitor irregularities with gameplay during post-analysis, e.g., if players didn’t blink as instructed. | |
Interview Audio | Audio recording of a post-study interview with the participant. Participant responses were analyzed thematically and grouped. |
Variables | Aug. Success | Input Override | Mit. Failure | Ref. Condition | ICC3 |
---|---|---|---|---|---|
Self-report Measures | |||||
Perc. Control | 3.75 (1.39) | 5.69 (1.08) | 5.07 (1.58) | 5.31 (1.20) | 0.36 |
Frustration | 2.88 (1.78) | 2.25 (1.29) | 2.53 (1.85) | 2.12 (1.26) | 0.03 |
Hardest | 7 | 2 | 3 | 3 | 0.04 |
Easiest | 1 | 3 | 1 | 10 | 0.32 |
Game Measures | |||||
Blink Recognition | 85% (0.25) | 90% (0.15) | 93% (0.17) | 86% (0.24) | - |
Blink Conv. Rate | 18% (0.14) | 29% (0.08) | 26% (0.08) | 38% (0.18) | - |
Pos. Feedback | 30% (0.17) | 87% (0.12) | 58% (0.18) | 60% (0.15) | - |
Help Feedback | 17% (0.07) | 31% (0.02) | 31% (0.03) | 0% (0.00) | - |
Fish Caught | 3.31 (2.21) | 8.06 (1.53) | 6.13 (2.07) | 5.88 (2.13) | - |
Fish Lost | 3.94 (1.12) | 0.19 (0.54) | 0.27 (0.70) | 1.38 (1.31) | - |
Fish Reel | 2.75 (1.48) | 9.31 (1.62) | 5.40 (2.32) | 6.06 (1.73) | - |
Fish Unreel | 10.00 (2.34) | 2.44 (1.90) | 2.00 (2.45) | 6.69 (1.82) | - |
Duration | 190 s (17 s) | 205 s (24 s) | 171 s (23 s) | 196 s (35 s) | - |
Predicted | Fixed Effect | AIC | ML | LR | |
---|---|---|---|---|---|
Perc. Control | (1|Participant) + Fish Reel | 193.68 | −89.84 | 34.91 | <0.001 * |
(1|Participant) + Pos. Feedback | 195.57 | −90.79 | 33.02 | <0.001 * | |
(1|Participant) + Fish Lost | 197.88 | −91.94 | 30.72 | <0.001 * | |
(1|Participant) + Fish Unreel | 204.25 | −95.13 | 24.34 | <0.001 * | |
(1|Participant) + Condition | 209.33 | −95.67 | 23.26 | <0.001 * | |
(1|Participant) + Fish Caught | 209.63 | −97.81 | 18.97 | <0.001 * | |
(1|Participant) + Blink Conv. Rate | 219.05 | −102.52 | 9.55 | 0.002 * | |
Frustration | (1|Participant) + Pos. Feedback | 185.12 | −85.56 | 7.96 | 0.005 * |
(1|Participant) + Fish Caught | 186.03 | −86.01 | 7.05 | 0.008 * | |
(1|Participant) + Fish Reel | 186.55 | −86.28 | 6.53 | 0.011 * | |
(1|Participant) + Fish Lost | 187.12 | −86.56 | 5.96 | 0.015 * | |
(1|Participant) + Blink Conv. Rate | 187.26 | −86.63 | 5.82 | 0.016 * | |
(1|Participant) + Fish Unreel | 187.72 | −86.86 | 5.36 | 0.021 * |
Study Goal | Expected Relation to Game Measures | |
---|---|---|
Perceived Control | Whether help types affected people’s perceived control. (question unchanged) | ↑ Increased with Blink Conv. Rate and Help Rate. ↑ Increased by no. of Fish Caught and Fish Reeled. ↓ Reduced by the no. of Fish Lost and Fish Unreel. |
Frustration | Whether help types affected people’s frustration. (question unchanged) | ↑ Increased by no. of Fish Lost and Fish Unreel. ↓ Reduced by Blink Conv. Rate and Help Rate. |
Help Quantity * | Whether help types moderated people’s perception of how much help they received. “How much did you feel the game helped you?” | ↑ Increased by Help Rate. ↓ Reduced by no. of Fish Lost and Fish Unreel. |
Help Appeal * | Whether people appreciated types of help differently (for example given differences in depiction, narration and outcome). “I like how the game helped me.” | ↑ Increased by no. of Fish Caught and Fish Reeled. ↓ Reduced by no. of Fish Lost and Fish Unreel. |
Pacing * | Whether help types moderated how people felt the game in terms of speed. “I felt like the game went fast.” | ↓ Reduced by increasing Duration. |
Irritation * | Whether help type differ in irritation. Whether people make differentiation between frustration from irritation. “How irritated did you feel in this condition?” | (Same as Frustration). ↑ Increased by no. of Fish Lost and Fish Unreel. ↓ Reduced by Blink Conv. Rate and Help Rate. |
Self-Report Measures | Aug. Success | Input Override | Mit. Failure | Ref. Condition | ICC3 |
---|---|---|---|---|---|
Perc. Control | 5.16 (1.01) | 4.63 (1.42) | 5.37 (0.96) | 5.16 (0.76) | 0.04 |
Frustration | 2.47 (0.96) | 2.74 (1.10) | 2.21 (0.92) | 2.79 (1.13) | 0.06 |
Help Quantity | 3.95 (1.65) | 6.05 (1.13) | 5.58 (1.46) | - | 0.42 |
Help Appeal | 4.63 (1.57) | 3.26 (1.59) | 4.21 (1.58) | - | 0.18 |
Pacing | 3.68 (1.06) | 3.84 (1.12) | 3.68 (0.75) | 3.68 (1.16) | 0.00 |
Irritation | 1.74 (0.65) | 2.05 (1.03) | 1.84 (1.12) | 1.89 (1.05) | 0.00 |
Hardest | 2 | 4 | 3 | 10 | 0.14 |
Easiest | 4 | 8 | 7 | 0 | 0.14 |
Game Measures | |||||
Blink Recognition | 99% (0.02) | 99% (0.03) | 98% (0.04) | 99% (0.04) | - |
Blink Conv. Rate | 26% (0.04) | 25% (0.04) | 26% (0.04) | 26% (0.04) | - |
Pos. Feedback | 70% (0.00) | 99% (0.03) | 69% (0.03) | 70% (0.00) | - |
Help Rate | 28% (0.02) | 31% (0.03) | 30% (0.00) | 0% (0.00) | - |
Fish Caught | 7.68 (0.67) | 7.68 (0.67) | 5.16 (0.50) | 5.32 (0.58) | - |
Fish Lost | 0.21 (0.42) | 0.00 (0.00) | 0.00 (0.00) | 0.53 (0.51) | - |
Fish Reel | 6.32 (0.67) | 12.11 (0.81) | 8.63 (0.60) | 8.68 (0.58) | - |
Fish Unreel | 5.79 (0.42) | 0.21 (0.54) | 0.21 (0.54) | 5.47 (0.51) | - |
Duration | 179 s (9 s) | 180 s (8 s) | 144 s (7 s) | 146 s (5 s) | - |
Predicted | Fixed Effect | AIC | ML | LR | |
---|---|---|---|---|---|
Perc. Control | (1|Participant) + Help Appeal | 233.07 | −104.53 | 11.97 | 0.063 |
Frustration | (1|Participant) + Condition Order | 219.36 | −101.68 | 7.02 | 0.071 |
Help Quantity | (1|Participant) + Fish Unreel | 179.64 | −81.82 | 21.80 | <0.001 * |
(1|Participant) + Condition | 181.70 | −81.85 | 21.73 | <0.001 * | |
(1|Participant) + Fish Reel | 182.68 | −83.34 | 18.76 | <0.001 * | |
(1|Participant) + Blink Conv. Rate | 192.78 | −88.39 | 8.66 | 0.003 * | |
(1|Participant) + Pos. Feedback | 192.94 | −88.47 | 8.50 | 0.004 * | |
Help Appeal | (1|Participant) + Fish Reel | 214.36 | −99.18 | 9.46 | 0.002 * |
(1|Participant) + Pos. Feedback | 216.91 | −100.45 | 6.91 | 0.009 * | |
(1|Participant) + Condition | 217.27 | −99.64 | 8.55 | 0.014 * | |
(1|Participant) + Pacing | 217.99 | −98.00 | 11.83 | 0.019 * | |
(1|Participant) + Blink Conv. Rate | 219.33 | −101.66 | 4.49 | 0.034 * | |
(1|Participant) + Fish Unreel | 219.49 | −101.75 | 4.33 | 0.038 * | |
Irritation | (1|Participant) + Fish Reel | 173.64 | −80.82 | 1.67 | 0.196 |
Pacing | (1|Participant) + Fish Caught | 179.71 | −83.86 | 1.29 | 0.257 |
Help Type | Agreement to Stroke Patient Viewpoints | Mean | SD | Spread (1–7) | |
---|---|---|---|---|---|
V1 | Aug. Success | “I think it was useful that he got strong and helped me reel in the fish.”—P9, Positive | 5.11 | 1.70 | |
V2 | Aug. Success | “He got stronger, but I didn’t think it helped me much.”—P10, Negative | 3.11 | 1.73 | |
V3 | Input Overr. | “I liked it when she took the fish up a notch at times, when I couldn’t”—P10, Positive | 3.84 | 1.74 | |
V4 | Input Overr. | “It irritated me that she interfered with the game.”—P8, Negative | 4.26 | 2.00 | |
V5 | Mit. Failure | “When the fish stood still, it was like saying “Let’s just try that again!”—P5, Positive | 5.79 | 1.44 | |
V6 | Mit. Failure | “When the fish stood still, it felt like the game went slower.”—P18, Negative | 3.16 | 1.64 |
Question | Mean | SD | Spread (1–7) | |
---|---|---|---|---|
E1 | “It irritated me how bad I was at blinking correctly.” | 3.55 | 2.16 | |
E2 | “It irritated me when the game did not register my blinks.” | 5.25 | 1.41 | |
E3 | “Losing the fish is not something that irritates me.” | 3.65 | 1.81 | |
E4 | “I was thinking about how I could be better at catching fish.” | 4.95 | 1.82 | |
E5 | “I enjoyed the way the game was styled.” | 6.25 | 0.79 | |
E6 | “I felt I was good at playing this game.” | 4.60 | 1.39 | |
E7 | “I enjoyed playing this game very much.” | 4.85 | 1.14 |
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Hougaard, B.I.; Knoche, H.; Kristensen, M.S.; Jochumsen, M. Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It. Sensors 2025, 25, 2742. https://doi.org/10.3390/s25092742
Hougaard BI, Knoche H, Kristensen MS, Jochumsen M. Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It. Sensors. 2025; 25(9):2742. https://doi.org/10.3390/s25092742
Chicago/Turabian StyleHougaard, Bastian Ilsø, Hendrik Knoche, Mathias Sand Kristensen, and Mads Jochumsen. 2025. "Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It" Sensors 25, no. 9: 2742. https://doi.org/10.3390/s25092742
APA StyleHougaard, B. I., Knoche, H., Kristensen, M. S., & Jochumsen, M. (2025). Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It. Sensors, 25(9), 2742. https://doi.org/10.3390/s25092742