Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-13T06:58:00.631Z Has data issue: false hasContentIssue false

Functional and usability assessment of a robotic exoskeleton arm to support activities of daily life

Published online by Cambridge University Press:  22 July 2014

Emilia Ambrosini*
Affiliation:
NeuroEngineering and Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
Simona Ferrante
Affiliation:
NeuroEngineering and Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
Mauro Rossini
Affiliation:
Valduce Hospital, Villa Beretta, Rehabilitation Centre, Costa Masnaga, Lecco, Italy
Franco Molteni
Affiliation:
Valduce Hospital, Villa Beretta, Rehabilitation Centre, Costa Masnaga, Lecco, Italy
Margit Gföhler
Affiliation:
Research Group for Machine Design and Rehabilitation Engineering (E 307-3), Technische Universität Wien, Vienna, Austria
Werner Reichenfelser
Affiliation:
Research Group for Machine Design and Rehabilitation Engineering (E 307-3), Technische Universität Wien, Vienna, Austria
Alexander Duschau-Wicke
Affiliation:
Hocoma AG, Volketswil, Switzerland
Giancarlo Ferrigno
Affiliation:
NeuroEngineering and Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
Alessandra Pedrocchi
Affiliation:
NeuroEngineering and Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
*
*Corresponding author. E-mail: emilia.ambrosini@polimi.it

Summary

An assistive device for upper limb support was developed and evaluated in terms of usability, user satisfaction and motor performance on six end-users affected by neuro-motor disorders (three spinal cord injury; one multiple sclerosis; two Friedreich's ataxia). The system consisted of a lightweight 3-degrees-of-freedom robotic exoskeleton arm for weight relief, equipped with electromagnetic brakes. Users could autonomously control the brakes using a USB-button or residual electromyogram activations. The system functionally supported all of the potential users in performing reaching and drinking tasks. For three of them, time, smoothness, straightness and repeatability were also comparable to healthy subjects. An overall high level of usability (system usability score, median value of 90/100) and user satisfaction (Tele-healthcare Satisfaction Questionnaire - Wearable Technology, median value of 104/120) were obtained for all subjects.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1.Ripat, J. D. and Woodgate, R. L., “The role of assistive technology in self-perceived participation,” Int. J. Rehabil. Res. 35, 170177 (2012).Google Scholar
2.Brose, S. W., Weber, D. J., Salatin, B. A., Grindle, G. G., Wang, H., Vazquez, J. J. and Cooper, R. A., “The role of assistive robotics in the lives of persons with disability,” Am. J. Phys. Med. Rehabil. Assoc. Acad. Physiatr. 89, 509521 (2010).Google Scholar
3.Maheu, V., Frappier, J., Archambault, P. S. and Routhier, F., “Evaluation of the JACO robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities,” Proceedings of the 2011 IEEE International Conference Rehabilitation Robotics, Zurich, Switzerland (Jun. 29–Jul. 1, 2011) pp. 1–5.Google Scholar
4.Driessen, B. J., Evers, H. G. and van Woerden, J. A., “MANUS–a wheelchair-mounted rehabilitation robot,” Proc. Inst. Mech. Eng. [H] 215, 285290 (2001).Google Scholar
5.Bien, Z., Kim, D.-J., Chung, M.-J., Kwon, D.-S. and Chang, P.-H., “Development of a wheelchair-based rehabilitation robotic system (KARES II) with various human-robot interaction interfaces for the disabled,” Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Kobe, Japan (Jul. 20–24, 2003), vol. 2, pp. 902–907.Google Scholar
6.Iwamuro, B. T., Cruz, E. G., Connelly, L. L., Fischer, H. C. and Kamper, D. G., “Effect of a gravity-compensating orthosis on reaching after stroke: evaluation of the Therapy Assistant WREX,” Arch. Phys. Med. Rehabil. 89, 21212128 (2008).Google Scholar
7.Rahman, T., Sample, W., Jayakumar, S., King, M. M., Wee, J. Y., Seliktar, R., Alexander, M., Scavina, M. and Clark, A., “Passive exoskeletons for assisting limb movement,” J. Rehabil. Res. Dev. 43, 583590 (2006).Google Scholar
8.Herder, J. L., Vrijlandt, N., Antonides, T., Cloosterman, M. and Mastenbroek, P. L., “Principle and design of a mobile arm support for people with muscular weakness,” J. Rehabil. Res. Dev. 43, 591604 (2006).Google Scholar
9.Nef, T., Mihelj, M. and Riener, R., “ARMin: a robot for patient-cooperative arm therapy,” Med. Biol. Eng. Comput. 45, 887900 (2007).Google Scholar
10.Nikitczuk, J., Weinberg, B. and Mavroidis, C., “Control of electro-rheological fluid based resistive torque elements for use in active rehabilitation devices,” Smart Mater. Struct. 16, 418 (2007).Google Scholar
11.Krebs, H. I., Hogan, N., Aisen, M. L. and Volpe, B. T., “Robot-aided neurorehabilitation,” IEEE Trans. Rehabil. Eng. 6, 7587 (1998).Google Scholar
12.Klamroth-Marganska, V., Blanco, J., Campen, K., Curt, A., Dietz, V., Ettlin, T., Felder, M., Fellinghauer, B., Guidali, M.et al., “Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial,” Lancet Neurol. 13, 159166 (2014).Google Scholar
13.Kiguchi, K. and Hayashi, Y., “An EMG-based control for an upper-limb power-assist exoskeleton robot,” IEEE Trans. Syst. Man Cybern. B 42, 10641071 (2012).Google Scholar
14.Lenzi, T., De Rossi, S. M. M., Vitiello, N. and Carrozza, M. C., “Intention-based EMG control for powered exoskeletons,” IEEE Trans. Biomed. Eng. 59, 21802190 (2012).Google Scholar
15.Casadio, M., Sanguineti, V., Solaro, C. and Morasso, P. G., “A haptic robot reveals the adaptation capability of individuals with multiple sclerosis.,” 1225–1233 (2007).Google Scholar
16.Lum, P. S., Burgar, C. G. and Shor, P. C., “Evidence for improved muscle activation patterns after retraining of reaching movements with the MIME robotic system in subjects with post-stroke hemiparesis,” IEEE Trans. Neural Syst. Rehabil. Eng. 12, 186194 (2004).Google Scholar
17.Kim, H., Miller, L. M., Fedulow, I., Simkins, M., Abrams, G. M., Byl, N. and Rosen, J., “Kinematic data analysis for post-stroke patients following bilateral versus unilateral rehabilitation with an upper limb wearable robotic system,” IEEE Trans. Neural Syst. Rehabil. Eng. 21, 153164 (2013).Google Scholar
18.Pedrocchi, A., Ferrante, S., Ambrosini, E., Gandolla, M., Casellato, C., Schauer, T., Klauer, C., Pascual, J., Vidaurre, C.et al., “MUNDUS project: MUltimodal Neuroprosthesis for daily Upper limb Support,” J. Neuroengineering Rehabil. 10, 66 (2013).Google Scholar
19.Rohm, M., Schneiders, M., Müller, C., Kreilinger, A., Kaiser, V., Müller-Putz, G. R. and Rupp, R., “Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury,” Artif. Intell. Med. 59, 133142 (2013).Google Scholar
20.Ambrosini, E., Ferrante, S., Schauer, T., Klauer, C., Gaffuri, M., Ferrigno, G. and Pedrocchi, A., “A myocontrolled neuroprosthesis integrated with a passive exoskeleton to support upper limb activities,” J. Electromyogr. Kinesiol. 24, 307317 (2014).Google Scholar
21.Reichenfelser, W., Karner, J and Gföhler, M., “Design concept for a mobile arm support,” Proceedings of the 20th Annual Meeting of the Europe Society of Movement Analysis for Adults and Children, Vienna, Austria (Sep. 12–17, 2011) pp. 162–163.Google Scholar
22.Ambrosini, E., Ferrante, S., Gföhler, M., Reichenfelser, W., Karner, J., Schauer, T., Klauer, C., Ferrigno, G. and Pedrocchi, A., “A hybrid assistive system to support daily upper limb activities,” Proceedings of the 17th Annual International FES Society Conference, Banff, Canada (Sep. 9–12, 2012) pp. 1–4.Google Scholar
23.Gilliaux, M., Lejeune, T., Detrembleur, C., Sapin, J., Dehez, B. and Stoquart, G., “A robotic device as a sensitive quantitative tool to assess upper limb impairments in stroke patients: A preliminary prospective cohort study,” J. Rehabil. Med. 44, 210217 (2012).Google Scholar
24.Stokes, V. P., Lanshammar, H. and Thorstensson, A., “Dominant pattern extraction from 3-D kinematic data,” IEEE Trans. Biomed. Eng. 46, 100106 (1999).Google Scholar
25.Baroni, G., Pedrocchi, A., Ferrigno, G., Massion, J. and Pedotti, A., “Motor coordination in weightless conditions revealed by long-term microgravity adaptation,” Acta Astronaut. 49, 199213 (2001).Google Scholar
26.Brooke, J., “SUS: A ‘Quick and Dirty' Usability Scale,’ In:Usability Evaluation in Industry (Jordan, P. W., Thomas, B., Weerdmeester, B. A. and McClelland, A. L., eds.) (Taylor and Francis, London, 1996) pp. 17.Google Scholar
27.Corben, L. A., Georgiou-Karistianis, N., Bradshaw, J. L., Hocking, D. R., Churchyard, A. J. and Delatycki, M. B., “The Fitts task reveals impairments in planning and online control of movement in Friedreich ataxia: Reduced cerebellar-cortico connectivity?,” Neuroscience 192, 382390 (2011).Google Scholar
28.Corben, L. A., Delatycki, M. B., Bradshaw, J. L., Horne, M. K., Fahey, M. C., Churchyard, A. J. and Georgiou-Karistianis, N., “Impairment in motor reprogramming in Friedreich ataxia reflecting possible cerebellar dysfunction,” J. Neurol. 257, 782791 (2010).Google Scholar