Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-25T19:45:59.561Z Has data issue: false hasContentIssue false
  • English
  • Français

AssistOn-Mobile: a series elastic holonomic mobile platform for upper extremity rehabilitation

Published online by Cambridge University Press:  16 September 2014

Mine Sarac ,
Mehmet Alper Ergin ,
Ahmetcan Erdogan  and
Volkan Patoglu
Mine Sarac
Affiliation:
Faculty of Engineering and Natural Sciences, Sabancι University, 34956 Istanbul, Turkey
Mehmet Alper Ergin
Affiliation:
Faculty of Engineering and Natural Sciences, Sabancι University, 34956 Istanbul, Turkey
Ahmetcan Erdogan
Affiliation:
Faculty of Engineering and Natural Sciences, Sabancι University, 34956 Istanbul, Turkey
Volkan Patoglu*
Affiliation:
Faculty of Engineering and Natural Sciences, Sabancι University, 34956 Istanbul, Turkey
*
*Corresponding author. E-mail: vpatoglu@sabanciuniv.edu
Get access Rights & Permissions [Opens in a new window]

Summary

We present the design, control, and human–machine interface of a series elastic holonomic mobile platform, AssistOn-Mobile, aimed to administer therapeutic table-top exercises to patients who have suffered injuries that affect the function of their upper extremities. The proposed mobile platform is a low-cost, portable, easy-to-use rehabilitation device targeted for home use. In particular, AssistOn-Mobile consists of a holonomic mobile platform with four actuated Mecanum wheels and a compliant, low-cost, multi-degrees-of-freedom series elastic element acting as its force sensing unit. Thanks to its series elastic actuation, AssistOn-Mobile is highly backdriveable and can provide assistance/resistance to patients, while performing omni-directional movements on plane. AssistOn-Mobile also features Passive Velocity Field Control (PVFC) to deliver human-in-the-loop contour tracking rehabilitation exercises. PVFC allows patients to complete the contour-tracking tasks at their preferred pace, while providing the proper amount of assistance as determined by the therapists. PVFC not only minimizes the contour error but also does so by rendering the closed-loop system passive with respect to externally applied forces; hence, ensures the coupled stability of the human-robot system. We evaluate the feasibility and effectiveness of AssistOn-Mobile with PVFC for rehabilitation and present experimental data collected during human subject experiments under three case studies. In particular, we utilize AssistOn-Mobile with PVFC (a) to administer contour following tasks where the pace of the tasks is left to the control of the patients, so that the patients can assume a natural and comfortable speed for the tasks, (b) to limit compensatory movements of the patients by integrating a RGB-D sensor to the system to continually monitor the movements of the patients and to modulate the task speeds to provide online feedback to the patients, and (c) to integrate a Brain–Computer Interface such that the brain activity of the patients is mapped to the robot speed along the contour following tasks, rendering an assist-as-needed protocol for the patients with severe disabilities. The feasibility studies indicate that AssistOn-Mobile holds promise in improving the accuracy and effectiveness of repetitive movement therapies, while also providing quantitative measures of patient progress.

Keywords

Rehabilitation roboticsSeries elastic actuationCompliant mechanismsHolonomic mobile platformPassive velocity field control
Type
Articles
Information
Robotica , Volume 32 , Issue 8: Rehabilitation Robotics and Human-Robot Interaction , December 2014 , pp. 1433 - 1459
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

1.Bütefisch, C., Hummelsheim, H., Denzler, P. and Mauritz, K.-H., “Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand,” J. Neurolog. Sci. 130 (1), 59–68 (1995).Google Scholar
2.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).Google Scholar
3.Sunderland, A., Tinson, D. J., Bradley, E. L., Fletcher, D., Langton Hewer, R. and Wade, D. T., “Enhanced physical therapy improves recovery of arm function after stroke. A randomised controlled trial,” J. Neurolog. Neurosurg. Psychiatry 55 (7), 530–535 (1992).Google Scholar
4.Bayona, N. A., Bitensky, J., Salter, K. and Teasell, R., “The role of task-specific training in rehabilitation therapies,” Top. Stroke Rehabil. 12 (3), 58–64 (2005).Google Scholar
5.Kwakkel, G., Kollen, B. J. and Krebs, H. I., “Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review,” Neurorehabil. Neural Repair 22 (2), 111–121 (2008).Google Scholar
6.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), 392–393 (2009).Google Scholar
7.Prange, G. B., Jannink, M. J., Groothuis-Oudshoorn, C. G., 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), 171–184 (2006).Google Scholar
8.Nykanen, K., The Effectiveness of Robot-Aided Upper Limb Therapy in Stroke Rehabilitation: A Systematic Review of Randomized Controlled Studies Master's Thesis (Finland: University of Jyvaskyla, 2010).Google Scholar
9.Nef, T., Guidali, M. and Riener, R., “ARMin III-arm therapy exoskeleton with an ergonomic shoulder actuation,” Appl. Bionics Biomech. 6 (2), 127–142 (2009).CrossRefGoogle Scholar
10.Frisoli, A., Bergamasco, M., Carboncini, M. C. and Rossi, B., “Robotic assisted rehabilitation in virtual reality with the L-EXOS,” Stud. Health Technol. Inform. 145, 40–54 (2009).Google Scholar
11.Carignan, C., Liszka, M. and Roderick, S., “Design of an Arm Exoskeleton with Scapula Motion for Shoulder Rehabilitation,” International Conference on Advanced Robotics, Seattle, WA, USA (Jul. 18–20, 2005) pp. 524–531.Google Scholar
12.Sanchez, R. J., Liu, J., Rao, S., Shah, P., Smith, R., Rahman, T., Cramer, S. C., Bobrow, J. E. and Reinkensmeyer, D. J., “Automating arm movement training following severe stroke: Functional exercises with quantitative feedback in a gravity-reduced environment,” IEEE Trans. Neural Syst. Rehabil. Eng. 14 (3), 378–389 (2006).Google Scholar
13.Yalcin, M. and Patoglu, V., “Kinematics and Design of AssistOn-SE: A Self-Adjusting Shoulder-Elbow Exoskeleton,” IEEE International Conference on Biomedical Robotics and Biomechatronics, Rome, Italy (Jun. 24–27, 2012) pp. 1579–1585.Google Scholar
14.Stienen, A. H. A., Hekman, E. E. G., der Helm, F. C. T. V., Prange, G. B., Jannink, M. J. A., Aalsma, A. M. M. and der Kooij, H. V., “Dampace: Dynamic Force-Coordination Trainer for the Upper Extremities,” IEEE International Conference on Rehabilitation Robotics, Noordwijk, The Netherlands (Jun. 12–15, 2007) pp. 820–826.CrossRefGoogle Scholar
15.Krebs, H., Ferraro, M., Buerger, S., Newbery, M., Makiyama, A., Sandmann, M., Lynch, D., Volpe, B. and Hogan, N., “Rehabilitation robotics: Pilot trial of a spatial extension for MIT-Manus,” J. Neuroeng. Rehabil. 1 (5), 1–15 (2004).Google Scholar
16.Casadio, M., Sanguineti, V., Morasso, P. G. and Arrichiello, V., “Braccio di Ferro: A new haptic workstation for neuromotor rehabilitation,” Technol. Health Care 14 (3), 123–142 (2006).CrossRefGoogle Scholar
17.Loureiro, R. C. V. and Harwin, W. S., “Robot Aided Therapy: Challenges ahead for Upper Limb Stroke Rehabilitation,” International Conference on Disability, Virtual Reality and Associated Technologies (2004) pp. 33–39.Google Scholar
18.Van der Linde, R. Q., Lammertse, P., Frederiksen, E. and Ruiter, B., “The HapticMaster, a New High-Performance Haptic Interface,” Eurohaptics, Pisa, Italy (Mar. 18–20, 2005) pp. 1–5.Google Scholar
19.Hesse, S., Schmidt, H., Werner, C., Rybski, C., Puzich, U. and Bardeleben, A., “A new mechanical arm trainer to intensify the upper limb rehabilitation of severely affected patients after stroke: Design, concept and first case series.” Eura Medicophys. 43 (4), 463–468 (2007).Google ScholarPubMed
20.Chen, Y. L., Kuo, T. S. and Chang, W. H., “Aid Training System for Upper Extremity Rehabilitation,” International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, vol. 2 (Oct. 25–28, 2001) pp. 1360–1363.Google Scholar
21.Peattie, A., Korevaar, A., Wilson, J., Sandilands, B., Chen, X. and King, M., “Automated Variable Resistance System for Upper Limb Rehabilitation,” Australasian Conference on Robotics and Automation, Sydney, Australia (Dec. 2–4, 2009) pp. 1–8.Google Scholar
22.Burdea, G. C., Cioi, D., Martin, J., Fensterheim, D. and Holenski, M., “The Rutgers Arm II rehabilitation system–-A feasibility study,” IEEE Trans. Neural Syst. Rehabil. Eng. 18 (5), 505–514 (2010).Google Scholar
23.Perry, J. C., Zabaleta, H., Belloso, A., de Pablo, C. R., Cavallaro, F. I. and Keller, T., “ARMassist: Development of a Functional Prototype for At-home Telerehabilitation of Post-Stroke Arm Impairment,” IEEE International Conference on Biomedical Robotics and Biomechatronics, Rome, Italy (Jun. 24–27, 2012) pp. 1561–1566.Google Scholar
24.Avizzano, C. A., Satler, M., Cappiello, G., Scoglio, A., Ruffaldi, E. and Bergamasco, M., “MOTORE: A Mobile Haptic Interface for Neuro-Rehabilitation,” IEEE International Symposium on Robot and Human Interactive Communication, Atlanta, Georgia, USA (Aug. 1–4, 2011) pp. 383–388.CrossRefGoogle Scholar
25.Luo, D., Roth, M., Wiesener, C., Schauer, T., Raisch, J. and Schmidt, H., “Reha-Maus: A Novel Robot for Upper Limb Rehabilitation,” Workshop Automatisierungstechnische Verfahren fur die Medizin (AUTOMED), Zurich, Switzerland (Oct. 29–30, 2011) pp. 33–34.Google Scholar
26.Luo, D., Schauer, T., Roth, M. and Raisch, J., “Position and Orientation Control of an Omni-Directional Mobile Rehabilitation Robot,” IEEE International Conference on Control Applications, Dubrovnik, Croatia (Oct. 3–5, 2012) pp. 50–56.Google Scholar
27.An, C. H. and Hollerbach, J. M., “Dynamic Stability Issues in Force Control of Manipulators,” American Control Conference, Minneapolis, MN, USA (Jun. 10–12, 1987) pp. 821–827.Google Scholar
28.Eppinger, S. and Seering, W., “Understanding Bandwidth Limitations in Robot Force Control,” IEEE International Conference on Robotics and Automation, Raleigh, NC, USA (Mar. 1987) pp. 904–909.Google Scholar
29.Erdogan, A. and Patoglu, V., “Online Generation of Velocity Fields for Passive Contour Following,” IEEE World Haptics Conference (WHC), Istanbul, Turkey (Jun. 21–24, 2011) pp. 245–250.Google Scholar
30.Erdogan, A. and Patoglu, V., “Slacking Prevention During Assistive Contour Following Tasks with Guaranteed Coupled Stability,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Algarve, Portugal (Oct. 7–12, 2012) pp. 1587–1594.Google Scholar
31.Sarac, M., Ergin, M. A. and Patoglu, V., “AssistOn-Mobile: A Series Elastic Holonomic Mobile Platform for Upper Extremity Rehabilitation,” IEEE World Haptics Conference (WHC), Daejeon, Korea (Apr. 14–18, 2013) pp. 283–288.CrossRefGoogle Scholar
32.[Online]. Available: http://advancehkg.com/solutions/sol-detail-en.asp?id=108Google Scholar
33.Han, K. L., Choi, O. K., Lee, I., Hwang, I., Lee, J. S. and Choi, S., “Design and Control of Omni-Directional Mobile Robot for Mobile Haptic Interface,” International Conference on Control, Automation and Systems, Seoul, Korea (Oct. 14–17, 2008) pp. 1290–1295.Google Scholar
34.Chandler, R. F., Clauser, C. E., McConville, J. T., Reynolds, H. M. and Young, J. W., “Investigation of Inertial Properties of the Human Body,” (NTIS, Virginia, 1975) pp. 72–79.Google Scholar
35.Welch, G. and Bishop, G., An Introduction to the Kalman Filter (University of North Carolina at Chapel Hill, North Carolina, 2001).Google Scholar
36.Durrant-Whyte, H., Introduction to Decentralised Data Fusion. The Australian Center for Field Robotics, The University of Sydney, Sydney, Australia (2004).Google Scholar
37.Webster, D. and Celik, O., “Experimental Evaluation of Microsoft Kinect's A accuracy and Capture Rate for Stroke Rehabilitation Applications,” IEEE Haptics Symposium, Houston, Texas (Feb. 23–26, 2014) pp. 455–460.Google Scholar
38.Sensinger, J. W. and Weir, R. F., “Improvements to Series Elastic Actuators,” IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Beijing, China (Aug. 13–16, 2006) pp. 1–7.Google Scholar
39.[Online]. Available: http://www.rethinkrobotics.com/products/baxter/Google Scholar
40.Sensinger, J. W. and Weir, R. F., “Unconstrained Impedance Control using a Compact Series Elastic Actuator,” IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Beijing, China (Aug. 13–16, 2006) pp. 1–6.Google Scholar
41.Pratt, G. A. and Williamson, M. M., “Series Elastic Actuators,” IEEE International Conference on Intelligent Robots and Systems, Pittsburgh, PA, USA, vol. 1 (Aug. 5–9, 1995) pp. 399–406.Google Scholar
42.Howell, L. L., Compliant Mechanisms (John Wiley and Sons, Hoboken, NJ, USA, 2001).Google Scholar
43.Merlet, J. P., Parallel Robots (Springer-Verlag, Dordrecht, The Netherlands, 2006).Google Scholar
44.Tian, Y., Shirinzadeh, B., Zhang, D. and Zhong, Y., “Three flexure hinges for compliant mechanism designs based on dimensionless graph analysis,” Precis. Eng. 34 (1), 92–100 (2010).Google Scholar
45.Tian, Y., Shirinzadeh, B. and Zhang, D., “Closed-form compliance equations of filleted V-shaped flexure hinges for compliant mechanism design,” Precis. Eng. 34 (3), 408–418 (2010).Google Scholar
46.Nielson, A. J. and Howell, L. L., “An inverstigation of compliant micro half pantographs using the pseudorigid body model,” Mech. Based Des. Struct. Mach. 29 (3), 317–330 (2001).Google Scholar
47.Kang, B. H., Wen, J. T., Dagalakis, N. G. and Gorman, J. J., “Analysis and design of parallel mechanisms with flexure joints,” IEEE Trans. Robot. 21 (6), 1179–1185 (2005).Google Scholar
48.Chiu, T.-C., Coordination Control of Multiple Axes Mechanical System: Theory and ExperimentsPh.D. Dissertation (Berkeley, CA: University of California, 1994).Google Scholar
49.Tung, E. D., Identification and Control of High-speed Machine ToolsPh.D. Dissertation (Berkeley, CA: University of California, 1993).Google Scholar
50.Li, P. and Horowitz, R., “Control of smart exercise machines. I. Problem formulation and nonadaptive control,” IEEE/ASME Tran. Mechatronics 2 (4), 237–247 (1997).Google Scholar
51.Li, P. and Horowitz, R., “Passive velocity field control of mechanical manipulators,” IEEE Trans. Robot. Autom. 15 (4), 751–763 (1999).Google Scholar
52.Li, P. and Horowitz, R., “Passive velocity field control (PVFC). Part I. Geometry and robustness,” IEEE Trans. Autom. Control 46 (9), 1346–1359 (2001).Google Scholar
53.Li, P. and Horowitz, R., “Passive velocity field control (PVFC). Part II. Application to contour following,” IEEE Trans. Autom. Control 46 (9), 1360–1371 (2001).Google Scholar
54.Moreno-Valenzuela, J., “On Passive velocity Field Control of Robot Arms,” IEEE Conference on Decision and Control, San Diego, CA, USA (Dec. 13–15, 2006) pp. 2955–2960.Google Scholar
55.Lum, P. S., Mulroy, S., Amdur, R. L., Requejo, P., Prilutsky, B. I. and Dromerick, A. W., “Gains in upper extremity function after stroke via recovery or compensation: Potential differential effects on amount of real-world limb use,” Top. Stroke Rehabil. 16 (4), 237–253 (2009).Google Scholar
56.Sarac, M., Koyas, E., Erdogan, A., Cetin, M. and Patoglu, V., “Brain Computer Interface based Robotic Rehabilitation with Online Modification of Task Speed,” IEEE International Conference on Rehabilitation Robotics, Seattle, Washington, USA (Jun. 24–26, 2013) pp. 1–7.Google Scholar
57.Pratt, J., Krupp, B. and Morse, C., “Series elastic actuators for high fidelity force control,” Industrial Robot: An International Journal 29 (3), 234–241 (2002).Google Scholar