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Design, development and deployment of a hand/wrist exoskeleton for home-based rehabilitation after stroke - SCRIPT project

Published online by Cambridge University Press:  23 September 2014

F. Amirabdollahian ,
S. Ates ,
A. Basteris ,
A. Cesario ,
J. Buurke ,
H. Hermens ,
D. Hofs ,
E. Johansson ,
G. Mountain  and
N. Nasr
...Show all authors
F. Amirabdollahian*
Affiliation:
University of Hertfordshire, United Kingdom
S. Ates
Affiliation:
University of Twente, the Netherlands
A. Basteris
Affiliation:
University of Hertfordshire, United Kingdom
A. Cesario
Affiliation:
San Raffaele Pisana, Italy
J. Buurke
Affiliation:
Roessingh Research and Development, the Netherlands
H. Hermens
Affiliation:
Roessingh Research and Development, the Netherlands
D. Hofs
Affiliation:
Roessingh Research and Development, the Netherlands
E. Johansson
Affiliation:
User Interface Design, Germany
G. Mountain
Affiliation:
University of Sheffield, Sheffield, UK
N. Nasr
Affiliation:
University of Sheffield, Sheffield, UK
S. Nijenhuis
Affiliation:
Roessingh Research and Development, the Netherlands
G. Prange
Affiliation:
Roessingh Research and Development, the Netherlands
N. Rahman
Affiliation:
University of Hertfordshire, United Kingdom
P. Sale
Affiliation:
San Raffaele Pisana, Italy
F. Schätzlein
Affiliation:
University of Sheffield, Sheffield, UK
B. van Schooten
Affiliation:
Roessingh Research and Development, the Netherlands
A. Stienen
Affiliation:
University of Twente, the Netherlands
*
*Corresponding author. E-mail: f.amirabdollahian2@herts.ac.uk
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Summary

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Changes in world-wide population trends have provided new demands for new technologies in areas such as care and rehabilitation. Recent developments in the the field of robotics for neurorehabilitation have shown a range of evidence regarding usefulness of these technologies as a tool to augment traditional physiotherapy. Part of the appeal for these technologies is the possibility to place a rehabilitative tool in one's home, providing a chance for more frequent and accessible technologies for empowering individuals to be in charge of their therapy.

Objective: this manuscript introduces the Supervised Care and Rehabilitation Involving Personal Tele-robotics (SCRIPT) project. The main goal is to demonstrate design and development steps involved in a complex intervention, while examining feasibility of using an instrumented orthotic device for home-based rehabilitation after stroke.

Methods: the project uses a user-centred design methodology to develop a hand/wrist rehabilitation device for home-based therapy after stroke. The patient benefits from a dedicated user interface that allows them to receive feedback on exercise as well as communicating with the health-care professional. The health-care professional is able to use a dedicated interface to send/receive communications and remote-manage patient's exercise routine using provided performance benchmarks. Patients were involved in a feasibility study (n=23) and were instructed to use the device and its interactive games for 180 min per week, around 30 min per day, for a period of 6 weeks, with a 2-months follow up. At the time of this study, only 12 of these patients have finished their 6 weeks trial plus 2 months follow up evaluation.

Results: with the “use feasibility” as objective, our results indicate 2 patients dropping out due to technical difficulty or lack of personal interests to continue. Our frequency of use results indicate that on average, patients used the SCRIPT1 device around 14 min of self-administered therapy a day. The group average for the system usability scale was around 69% supporting system usability.

Conclusions: based on the preliminary results, it is evident that stroke patients were able to use the system in their homes. An average of 14 min a day engagement mediated via three interactive games is promising, given the chronic stage of stroke. During the 2nd year of the project, 6 additional games with more functional relevance in their interaction have been designed to allow for a more variant context for interaction with the system, thus hoping to positively influence the exercise duration. The system usability was tested and provided supporting evidence for this parameter. Additional improvements to the system are planned based on formative feedback throughout the project and during the evaluations. These include a new orthosis that allows a more active control of the amount of assistance and resistance provided, thus aiming to provide a more challenging interaction.

Keywords

Rehabilitation TechnologyHome-based therapy systemsStroke rehabilitationAdaptive systems
Type
Articles
Information
Robotica , Volume 32 , Issue 8: Rehabilitation Robotics and Human-Robot Interaction , December 2014 , pp. 1331 - 1346
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2014

References

1.Schaechter, J. D., “Motor rehabilitation and brain plasticity after hemiparetic stroke,” Prog. Neurobiology 73 (1), 61–72 (2004).Google Scholar
2.Krakauer, J. W., “Arm function after stroke: From physiology to recovery,” Semin. Neurology 25 (4), 384–395 (2005).Google Scholar
3.Fisher, B. E. and Sullivan, K. J., “Activity-dependent factors affecting poststroke functional outcomes,” Top. Stroke Rehabil. 8 (3), 31–44 (2001).Google Scholar
4.Wagenaar, R. C. and Meijer, O. G., “Effects of stroke rehabilitation: A critical review of the literature,” Rehabil. Sci. 4 (3), 61–73 (1991).Google Scholar
5.Kwakkel, G., Wagenaar, R. C., Koelman, T. W., Lankhorst, G. J. and Koetsier, J. C., “Effects of intensity of rehabilitation after stroke a research synthesis,” Stroke 28 (8), 1550–1556 (1997).Google Scholar
6.Kwakkel, G., Wagenaar, R. C., Twisk, J. W. R., Lankhorst, G. J. and Koetsier, J. C., “Intensity of leg and arm training after primary middle-cerebral-artery stroke: a randomised trial,” Lancet 354 (9174), 191–196 (1999).CrossRefGoogle ScholarPubMed
7.Nelles, G., Jentzen, W., Jueptner, M., Müller, S. and Diener, H. C., “Arm training induced brain plasticity in stroke studied with serial positron emission tomography,” Neuroimage 13 (6), 1146–1154 (2001).Google Scholar
8.Platz, T., “Evidence-based arm rehabilitation–a systematic review of the literature,” Der Nervenarzt 74 (10), 841 (2003).CrossRefGoogle ScholarPubMed
9.Kwakkel, G., van Peppen, R., Wagenaar, R. C., Dauphinee, S. W., Richards, C., Ashburn, A., Miller, K., Lincoln, N., Partridge, C., Wellwood, I.et al., “Effects of augmented exercise therapy time after stroke a meta-analysis,” Stroke 35 (11), 2529–2539 (2004).Google Scholar
10.Lo, A. C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., Ringer, R. J., Wagner, T. H., Krebs, H. I., Volpe, B. T.et al., “Robot-assisted therapy for long-term upper-limb impairment after stroke,” New England J. Med. 362 (19), 1772–1783 (2010).CrossRefGoogle ScholarPubMed
11.Weiller, C., Jüptner, M., Fellows, S., Rijntjes, M., Leonhardt, G., Kiebel, S., Müller, S., Diener, H. C. and Thilmann, A. F., “Brain representation of active and passive movements,” Neuroimage 4 (2), 105–110 (1996).Google Scholar
12.Kaelin-Lang, A., Sawaki, L. and Cohen, L. G., “Role of voluntary drive in encoding an elementary motor memory,” J. Neurophysiol. 93 (2), 1099–1103 (2005).Google Scholar
13.Feys, H. M., De Weerdt, W. J., Selz, B. E., Steck, G. A. C., Spichiger, R., Vereeck, L. E., Putman, K. D. and Van Hoydonck, G. A., “Effect of a therapeutic intervention for the hemiplegic upper limb in the acute phase after stroke a single-blind, randomized, controlled multicenter trial,” Stroke 29 (4), 785–792 (1998).Google Scholar
14.Barreca, S., Wolf, S. L., Fasoli, S. and Bohannon, R., “Treatment interventions for the paretic upper limb of stroke survivors: A critical review,” Neurorehabilitation Neural Repair 17 (4), 220–226 (2003).CrossRefGoogle ScholarPubMed
15.Kahn, L. E., Lum, P. S., Rymer, W. Z. and Reinkensmeyer, D. J., “Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does?,” J. Rehabil. Res. Dev. 43 (5), 619 (2006).Google Scholar
16.Feydy, A., Carlier, R., Roby-Brami, A., Bussel, B., Cazalis, F., Pierot, L., Burnod, Y. and Maier, M. A., “Longitudinal study of motor recovery after stroke recruitment and focusing of brain activation,” Stroke 33 (6), 1610–1617 (2002).Google Scholar
17.Prange, G. B., Jannink, M. J. A., Groothuis-Oudshoorn, C. G. M., Hermens, H. J. and IJzerman, M. J., “Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke,” J. Rehabil. Res. Dev. 43 (2), 171 (2006).CrossRefGoogle Scholar
18.Kwakkel, G., Kollen, B. J. and Krebs, H. I., “Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review,” Neurorehabilitation Neural Repair 22 (2), 111–121 (2008).Google Scholar
19.Mehrholz, J., Platz, T., Kugler, J. and Pohl, M., “Electromechanical and robot-assisted arm training for improving arm function and activities of daily living after stroke,” Stroke 40 (5), e392–e393 (2009).Google Scholar
20.Hesse, S., Schulte-Tigges, G., Konrad, M., Bardeleben, A. and 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. 84 (6), 915–920 (2003).CrossRefGoogle ScholarPubMed
21.Krebs, H. I., Celestino, J., Williams, D., Ferraro, M., Volpe, B. and Hogan, N., “A Wrist Extension for Mit-Manus,” In: Advances in Rehabilitation Robotics (Springer Berlin Heidelberg, Germany, 2004) pp. 377–390.Google Scholar
22.Krebs, H. I., Volpe, B. T., Williams, D., Celestino, J., Charles, S. K., Lynch, D. and Hogan, N., “Robot-aided neurorehabilitation: A robot for wrist rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 15 (3), 327–335 (2007).Google Scholar
23.Masia, L., Krebs, H. I., Cappa, P. and Hogan, N., “Design and characterization of hand module for whole-arm rehabilitation following stroke,” IEEE/ASME Trans. Mechatronics 12 (4), 399–407 (2007).CrossRefGoogle ScholarPubMed
24.Loureiro, R. C. V., Lamperd, B., Collin, C. and Harwin, W. S., “Reach & Grasp Therapy: Effects of the gentle/g System Assessing Sub-Acute Stroke Whole-Arm Rehabilitation,” IEEE International Conference on Rehabilitation Robotics, 2009. ICORR 2009, IEEE (2009) pp. 755–760.Google Scholar
25.Carmeli, E., Peleg, S., Bartur, G., Elbo, E. and Vatine, J.-J., “Handtutortm enhanced hand rehabilitation after stroke?a pilot study,” Physiotherapy Res. Int. 16 (4), 191–200 (2011).Google Scholar
26.Winstein, C. J. and Stewart, J. C., “Conditions of task practice for individuals with neurologic impairments,” Textbook Neural Repair Rehabil. 2, 89–102 (2006).Google Scholar
27.Molier, B. I., Van Asseldonk, E. H. F., Hermens, H. J. and Jannink, M. J. A., “Nature, timing, frequency and type of augmented feedback; does it influence motor relearning of the hemiparetic arm after stroke? a systematic review,” Disability & Rehabil. 32 (22), 1799–1809 (2010).Google Scholar
28.Molier, B. I., Influence of Augmented Feedback on Learning Upper Extremity Tasks After Stroke (University of Twente, the Netherlands, 2012).Google Scholar
29.Patton, J. L. and Mussa-Ivaldi, F. A., “Robot-assisted adaptive training: Custom force fields for teaching movement patterns,” IEEE Trans. Biomed. Eng. 51 (4), 636–646 (2004).Google Scholar
30.van Asseldonk, E. H. F., Wessels, M., Stienen, A. H. A., van der Helm, F. C. T. and van der Kooij, H., “Influence of haptic guidance in learning a novel visuomotor task,” J. Physiol.-Paris 103 (3), 276–285 (2009).Google Scholar
31.Shea, C. H., Lai, Q., Black, C. and Park, J.-H., “Spacing practice sessions across days benefits the learning of motor skills,” Hum. Mov. Sci. 19 (5), 737–760 (2000).Google Scholar
32.Krebs, H. I., Volpe, B. T., Ferraro, M., Fasoli, S., Palazzolo, J., Rohrer, B., Edelstein, L., Hogan, N.et al., “Robot-aided neurorehabilitation: From evidence-based to science-based rehabilitation,” Top. Stroke Rehabil. 8 (4), 54–70 (2002).Google Scholar
33.Gaver, B., Dunne, T. and Pacenti, E., “Design: cultural probes,” Interactions 6 (1), 21–29 (1999).Google Scholar
34.Cooper, A., Reimann, R. and Cronin, D., About face 3: The Essentials of Interaction Design (John Wiley & Sons, Wiley Publishing Inc, Indianapolis, USA, 2012).Google Scholar
35.Monk, A., Davenport, L., Haber, J. and Wright, P., Improving your Human-Computer Interface: A Practical Technique (Prentice Hall London, 1993).Google Scholar
36.Leon, B., Basteris, A. and Amirabdollahian, F., “Comparing Recognition Methods to Identify Different Types of Grasps for Hand Rehabilitation,” 7th International Conference on Advances in Computer-Human Interactions. (ACHI2014) (2014) pp. 109–114.Google Scholar
37.Ates, S., Lobo-Prat, J., Lammertse, P., van der Kooij, H. and Stienen, A. H. “Script Passive Orthosis: Design and Technical Evaluation of the Wrist and Hand Orthosis for Rehabilitation Training at Home. IEEE. . . International Conference on Rehabilitation Robotics:[proceedings] (2013) pp. 1–6.Google Scholar
38.Magill, R. A. and Anderson, D. I., Motor Learning and Control: Concepts and Applications, Vol. 11 (McGraw-Hill, New York, 2007).Google Scholar
39.Magermans, D. J., Chadwick, E. K. J., Veeger, H. E. J. and Van Der Helm, F. C. T., “Requirements for upper extremity motions during activities of daily living,” Clinical Biomechanics 20 (6), 591–599 (2005).Google Scholar
40.van Andel, C. J, Wolterbeek, N., Doorenbosch, C. A. M., Veeger, DirkJan H. E. J. and Harlaar, J., “Complete 3d kinematics of upper extremity functional tasks,” Gait Posture 27 (1), 120–127 (2008).Google Scholar
41.Emken, J. L., Bobrow, J. E. and Reinkensmeyer, D. J., “Adaptive human-robot interaction based on lag-lead modelling for home-based stroke rehabilitation,” IEEE International Conference on Systems, Man and Cybernetics (SMC2013) (IEEE, 2013).Google Scholar
42.Basteris, A. and Amirabdollahian, F., “Rapid Assessment of Range of Motion and Movement Duration During Human-Robot Interaction,” World Congress for NeuroRehabilitation (WCNR) 2014, Istanbul, Turkey (Apr. 8–12, 2014).Google Scholar
43.Emken, J. L., Bobrow, J. E. and Reinkensmeyer, D. J., “Robotic Movement Training as an Optimization Problem: Designing a Controller that Assists Only as Needed,” Proceedings of the 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005, (IEEE, 2005) pp. 307–312.Google Scholar
44.Chemuturi, R., Amirabdollahian, F. and Dautenhahn, K., “Adaptive training algorithm for robot-assisted upper-arm rehabilitation, applicable to individualised and therapeutic human-robot interaction,” J. Neuroengineering Rehabil. 10 (1), 102 (2013).CrossRefGoogle ScholarPubMed
45.Guadagnoli, M. A. and Lee, T. D., “Challenge point: A framework for conceptualizing the effects of various practice conditions in motor learning,” J. Motor Behav. 36 (2), 212–224 (2004).Google Scholar
46.Luft, A. R., McCombe-Waller, S., Whitall, J., Forrester, L. W., Macko, R., Sorkin, J. D., Schulz, J. B., Goldberg, A. P. and Hanley, D. F., “Repetitive bilateral arm training and motor cortex activation in chronic stroke: a randomized controlled trial,” Jama 292 (15), 1853–1861 (2004).Google Scholar
47.Bangor, A., Kortum, P. and Miller, J., “Determining what individual sus scores mean: Adding an adjective rating scale,” J. Usability Stud. 4 (3), 114–123 (2009).Google Scholar
48.Coupar, F., Pollock, A., Legg, L. A., Sackley, C. and van Vliet, P., “Home-based therapy programmes for upper limb functional recovery after stroke,” Cochrane Database Syst Rev. (2012). doi: 10.1002/14651858.CD006755.pub2.Google Scholar
49.Prange, G. B., Nijenhuis, S. M., Sale, P., Cesario, A., Nasr, N., Mountain, G., Amirabdollahian, F. and Buurke, J. H., “Preliminary Findings of Feasibility and Compliance of Technology-Supported Distal Arm Training at Home after Stroke,” In: Replace, Repair, Restore, Relieve - Bridging Clinical and Engineering Solutions in Neurorehabilitation (Jensen, W., Andersen, O. K. and Akay, M., eds.) (Springer International Publishing, Berlin, 2014) pp. 665–673.Google Scholar