Methods Inf Med 2017; 56(02): 145-155
DOI: 10.3414/ME16-02-0017
REHAB
Schattauer GmbH

Evaluation Results of an Ontology-based Design Model of Virtual Environments for Upper Limb Motor Rehabilitation of Stroke Patients

Cristina Ramírez-Fernández
1   Facultad de Ciencias, Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico
2   Instituto Tecnológico de Ensenada, Ensenada, Mexico
,
Alberto L. Morán
1   Facultad de Ciencias, Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico
,
Eloísa García-Canseco
1   Facultad de Ciencias, Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico
,
Jorge R. Gómez-Montalvo
3   Facultad de Matemáticas, Universidad Autónoma de Yucatán, Merida, Yucatán, Mexico
› Author Affiliations
Further Information

Publication History

received: 07 March 2016

accepted: 30 February 2016

Publication Date:
25 January 2018 (online)

Summary

Objectives: 1) To enhance the content of an ontology for designing virtual environments (VEs) for upper limb motor rehabilitation of stroke patients according to the suggestions and comments of rehabilitation specialists and software developers, 2) to characterize the perceived importance level of the ontology, 3) to determine the perceived usefulness of the ontology, and 4) to identify the safety characteristics of the ontology for VEs design according to the rehabilitation specialists.

Methods: Using two semi-structured Web questionnaires, we asked six rehabilitation specialists and six software developers to provide us with their perception regarding the level of importance and the usability of the ontology. From their responses we have identified themes related to perceived and required safety characteristics of the ontology.

Results: Significant differences in the importance level were obtained for the Stroke Disability, VE Configuration, Outcome Measures, and Safety Calibration classes, which were perceived as highly important by rehabilitation specialists. Regarding usability, the ontology was perceived by both groups with high usefulness, ease of use, learnability and intention of use. Concerning the thematic analysis of recommendations, eight topics for safety characteristics of the ontology were identified: adjustment of therapy strategies; selection and delimitation of movements; selection and proper calibration of the interaction device; proper selection of measuring instruments; gradual modification of the difficulty of the exercise; adaptability and variability of therapy exercises; feedback according to the capabilities of the patient; and real-time support for exercise training.

Conclusions: The rehabilitation specialists and software developers confirmed the importance of the information contained in the ontology regarding motor rehabilitation of the upper limb. Their recommendations highlight the safety features and the advantages of the ontology as a guide for the effective design of VEs.

 
  • References

  • 1 Hong K, Saver J. Quantifying the Value of Stroke Disability Outcomes: WHO Global Burden of Disease Project Disability Weights for Each Level of the Modified Rankin Scale. Stroke. 2009; 40 (12) 3828-3833.
  • 2 Takeuchi N, Izumi S. Rehabilitation with Post-stroke Motor Recovery: A Review with a Focus on Neural Plasticity. Stroke Res Treat. 2013; 2013: 1-13.
  • 3 Belda-Lois J, Mena-del Horno S, Bermejo-Bosch I, Moreno J, Pons J, Farina D. et al. Rehabilitation of gait after stroke: a review towards a top-down approach. J Neuroeng Rehabil. 2011; 8 (66) 1-19.
  • 4 Wolf A, Scheiderer R, Napolitan N, Belden C, Shaub L, Whitford M. Efficacy and task structure of bimanual training post stroke: a systematic review. Top Stroke Rehabil. 2014; 21 (03) 181-196.
  • 5 Henderson A, Korner-Bitensky N, Levin M. Virtual Reality in Stroke Rehabilitation: A Systematic Review of its Effectiveness for Upper Limb Motor Recovery. Top Stroke Rehabil. 2007; 14 (02) 52-61.
  • 6 Turolla A, Dam M, Ventura L, Tonin P, Agostini M, Zucconi C. et al. Virtual reality for the rehabilitation of the upper limb motor function after stroke: a prospective controlled trial. J Neuroeng Rehabil. 2013; 10 (01) 1-9.
  • 7 Levin M, Weiss P, Keshner E. Emergence of Virtual Reality as a Tool for Upper Limb Rehabilitation: Incorporation of Motor Control and Motor Learning Principles. Phys Ther. 2015; 95 (03) 415-426.
  • 8 Duncan P. Outcome measures in stroke rehabilitation. Handb Clin Neurol. 2013; 110: 105-111.
  • 9 Kleim J, Jones T. Principles of Experience-Dependent Neural Plasticity: Implications for Rehabilitation After Brain Damage. J Speech Lang Hear Res. 2008; 51 (01) S225-239.
  • 10 Saposnik G, Levin M. Virtual Reality in Stroke Rehabilitation: A Meta-Analysis and Implications for Clinicians. Stroke. 2011; 42 (05) 1380-1386.
  • 11 Ramírez-Fernández C, García-Canseco E, Morán AL. Towards a Set of Design Principles for Hapto-Virtual Rehabilitation Environments: Preliminary Results in Fine Motor Hand Therapy. In: Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. 2014: 394-397.
  • 12 Ramírez-Fernández C, Morán AL, García-Canseco E, Orihuela-Espina F. Design Factors of Virtual Environments for Upper Limb Motor Rehabilitation of Stroke Patients. In: Proceedings of the 5th Mexican Conference on Human-Computer Interaction - MexIHC ‘14. 2014: 22-25.
  • 13 Timmermans A, Seelen H, Willmann R, Kingma H. Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design. J Neuroeng Rehabil. 2009; 6 (01) 1-18.
  • 14 Studer R, Benjamins V, Fensel D. Knowledge Engineering: Principles and Methods. Data Knowl Eng. 1998; 25 1-2 161-197.
  • 15 Corcho O, Fernández-López M, Gómez-Pérez A. Methodologies, tools and languages for building ontologies. Where is their meeting point?. Data and Knowledge Engineering. 2003; 46 (01) 41-64.
  • 16 Chandrasekaran B, Josephson J, Benjamins V. What Are Ontologies, and Why Do We Need Them?. IEEE Intelligent Systems and their Applications. 1999; 14 (01) 20-26.
  • 17 Pellens B, De Troyer O, Bille W, Kleinermann F, Romero R. An Ontology-Driven Approach for Modeling Behavior in Virtual Environments. In: On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshops Lecture Notes in Computer Science. 2005: 1215-1224.
  • 18 Tang S, Hanneghan M. Game Content Model: An Ontology for Documenting Serious Game Design. In: Proceedings - 4th International Conference on Developments in eSystems Engineering. 2011: 431-436.
  • 19 Missikoff M, Smith F, Taglino F. Ontology building and maintenance in collaborative virtual environments. Concurr Comput. 2015; 27 (11) 2796-2817.
  • 20 Button K, van Deursen RW, Soldatova L, Spasic I. TRAK ontology: Defining standard care for the rehabilitation of knee conditions. J Biomed Inform. 2013; 46: 615-625.
  • 21 Zikos D, Galatas G, Metsis V, Makedon F. A web ontology for brain trauma patient computer-assisted rehabilitation. Stud Health Technol Inform. 2013; 190: 100-102.
  • 22 Dogmus Z, Erdem E, Patoglu V. REHABROBOONTO: Design, development and maintenance of a rehabilitation robotics ontology on the cloud. Robot Comput Integr Manuf. 2015; 33: 100-109.
  • 23 Ramírez-Fernández C, García-Canseco E, Morán AL, Gómez-Montalvo JR. Ontology-based Design Model of Virtual Environments for Upper Limb Motor Rehabilitation of Stroke Patients. REHAB 2015 Workshop. ICTs for improving Patients Rehabilitation Research Techniques; 2015
  • 24 Gennari J, Musen M, Fergerson R, Grosso W, Crubezy M, Eriksson H. et al. The evolution of Protégé: an environment for knowledge-based systems development. International Journal of Human Computer Studies. 2003; 58 (01) 89-123.
  • 25 Nielsen J, Molich R. Heuristic evaluation of user interfaces. CHI’90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 1990: 249-256.
  • 26 Davis F. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Miss Q. 1989; 13 (03) 319-340.
  • 27 Brooke J. SUS – A quick and dirty usability scale. Usability Evaluation in Industry. 1996: 189-194.
  • 28 Szalma JL. Individual differences in performance, workload, and stress in sustained attention: Optimism and pessimism. Pers Individ Dif. 2009; 47 (05) 444-451.
  • 29 Hart S, Staveland L. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. Human Mental Workload. 1988; 52: 139-183.
  • 30 Watson D. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol. 1988; 54 (06) 1063-1070.
  • 31 Brewer B, McDowell S, Worthen-Chaudhari L. Poststroke Upper Extremity Rehabilitation: A Review of Robotic Systems and Clinical Results. Top Stroke Rehabil. 2007; 14 (06) 22-44.
  • 32 Rivas JJ, Orihuela-Espina F, Sucar LE, Palafox L, Hernandez-Franco J, Bianchi-Berthouze N. Detecting affective states in virtual rehabilitation. 9th International Conference on Pervasive Computing Technologies for Healthcare (Pervasive-Health). 2015: 287-292.
  • 33 Subramanian S, Knaut L, Beaudoin C, McFadyen B, Feldman A, Levin M. Virtual reality environments for post-stroke arm rehabilitation. J Neuroeng Rehabil. 2007; 4 (20) 1-5.
  • 34 Sigrist R, Rauter G, Riener R, Wolf P. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. Psychon Bull Rev. 2013; 20 (01) 21-53.
  • 35 Rogers Y, Sharp H, Preece J. Interaction Design: Beyond Human-Computer Interaction.. 3rd ed. Chichester: John Wiley and Sons; 2011
  • 36 Dix A, Finlay J, Abowd G, Beale R. Human-Computer Interaction.. 3rd ed. Harlow: Pearson/Prentice-Hall; 2004
  • 37 Fabregas A, Paternina C, Mancilla A. Simulación de sistemas productivos con Arena.. Barranquilla, Colombia: Ediciones Uninorte; 2003
  • 38 Zar JH. Biostatistical analysis.. Harlow: Prentice Hall; 2010
  • 39 Marian M. Ontologies Representation and Management, as a Semantic Tool for Organizational Memory Consolidation. Annals of the University of Oradea, Economic Science Series. 2009; 18 (04) 976-980.
  • 40 Ramírez-Fernández C, Morán AL, García-Canseco E. Haptic Feedback in Motor Hand Virtual Therapy Increases Precision and Generates Less Mental Workload. In: EAI Endorsed Transactions On Pervasive Health and Technology. 2015
  • 41 Cameirão M, Badia S, Duarte E, Frisoli A, Verschure P. The Combined Impact of Virtual Reality Neurorehabilitation and Its Interfaces on Upper Extremity Functional Recovery in Patients with Chronic Stroke. Stroke. 2012; 43 (10) 2720-2728.
  • 42 Zhao C, Zhang L. Research of Information Presentation for Electronic Medical Record Based on Ontology. 2013 6th International Conference on Information Management. Innovation Management and Industrial Engineering; 2013: 489-492.
  • 43 Simon AM, Kelly BM, Ferris DP. Preliminary Trial of Symmetry-Based Resistance in Individuals with Post-Stroke Hemiparesis. In: 31st Annual International Conference of the IEEE EMBS. 2009: 5294-5299.
  • 44 Fischer H, Kahn L, Pelosin E, Roth H, Barbas J, Rymer WZ. et al. Can Robot-Assisted Therapy Promote Generalization of Motor Learning Following Stroke?: Preliminary Results. The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. 2006: 865-868.
  • 45 Giuffrida JP, Lerner A, Steiner R, Daly J. Upper-extremity stroke therapy task discrimination using motion sensors and electromyography. IEEE Trans Neural Syst Rehabil Eng. 2008; 16 (01) 82-90.
  • 46 Hesse S, Schulte-Tigges G, Konrad M, Bardeleben A, Werner C. Robot-assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects. Arch Phys Med Rehabil. 2003; 84 (06) 915-920.
  • 47 Metzger J-C, Lambercy O, Califfi A, Dinacci D, Petrillo C, Rossi P. et al. Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot. J Neuroeng Rehabil. 2014; 11: 154.