Rehabilitation Robotics

Pelvis and Skywalker Robots.

The application of robotics to deliver therapy is probably the most important development in rehabilitation since, as one wag put it, “… the invention of the pulley”.   It represents a paradigm shift for robotics, moving the field from its “classical” 3-Ds settings—dirty, dangerous, dull—considered too hazardous for humans to literally anybody’s living room. This change is leading to the next technology boom on service robotics and perhaps more important, it represents a real opportunity to augment human performance, ameliorate impairment, and promote neuro-recovery of a stroke patient after the damage has been done or to promote habilitation of a child with cerebral palsy.

 

We pioneered the field of rehabilitation robotics starting the development of the MIT-Manus in 1989 and developed multiple robotic tools: the MIT-Manus, wrist, hand, anti-gravity, pelvis, anklebot, pediatric anklebot, and MIT-skywalker. These robots have provided most of the clinical data supporting the use of robotics to the upper extremity (American Heart Association, Veterans Administration, Dep of Defense Guidelines for Stroke Care).

 

 

Ankle, Wrist, and Hand Modules.

Our Mission

  • To research, develop, manufacture, and deploy appropriate robotic technologies for restoring, maintaining, and augmenting human neuro-motor performance. To deploy these technologies in hospitals, rehabilitation hospitals, community health-care centers, home health-care, home, as well as minimizing work related injuries.
  • To develop and champion “Motion Processing” as a powerful and precise data-gathering tool for measuring neuro-motor behavior, and extracting (new) meaning from measured data.
  • To apply Motion Processing as a tool for more efficiently addressing motion-related information needs by, for example: augmenting therapies for neuro-motor recovery; enhancing existing methods for classifying functional levels of neurologically-impaired patients; and creating new efficiencies in medical information systems designed for in-patient and out-patient rehabilitation settings, as well as home setting.
  • To identify appropriate non-medical applications for Motion Processing. Examples include new human-robot interfaces, smart home appliances that can physically cooperate with consumers, and greater realism in gaming technology.

 

For the World Federation of Neurorehabilitation (WFNR)

See "Special Interest Group in Robotics" at our collaborator Moss Rehabilitation Research Institute at https://mrri.org/world-federation-for-neurorehabilitation/

 

Recent Highlighted Research

See our latest publication "Motor Function Assessment of Children with Cerebral Palsy using Monocular Video" in 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023 at https://ieeexplore.ieee.org/abstract/document/10331472

See our latest publication "Distilling Knowledge in Vision-based Human Motor Assessment for Improved Accuracy and Running Efficiency" in IEEE-BioRob 2024 Heidelberg Germany at EPUB

See our latest publication "Transverse Pelvic/Saddle Rotation Characterizing Push-off Initiation during Saddle-seat-type Body-weight-supported Treadmill Walking" in IEEE-BioRob 2024 Heidelberg Germany at EPUB

See our latest publication "Modeling Uncertainty in Computer Vision based Gross Motor Function Assessment of Children with Cerebral Palsy" in IEEE-BioRob 2024 Heidelberg Germany at EPUB

See our latest publication "Ankle Impedance in Healthy subjects at different walking speeds" in IEEE-BioRob 2024 Heidelberg Germany at EPUB

See our latest publication "A Low-Cost Smart Phone Home-Based System for Upper Limb Training" in IEEE-BioRob 2024 Heidelberg Germany at EPUB

See our latest publication "Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation" in IEEE Transactions on Neural Systems and Rehabilitation Engineering at https://ieeexplore.ieee.org/abstract/document/10560023

Selected References

For access to these references.

       Design and Development of robotic tools for the field of neurorehabilitation 

  1. Krebs, H.I.; Hogan, N.; Aisen, M.L.; Volpe, B.T.; “Robot-Aided Neuro-Rehabilitation”, IEEE –Transactions on Rehabilitation Engineering, 6:1:75-87 (1998), PMCID 2692541.  
  2. Krebs, H.I.; Volpe, B.T.; Williams, D.; Celestino, J.; Charles, S.K.; Lynch, D.; Hogan, N; “Robot-Aided Neurorehabilitation: A Robot for Wrist Rehabilitation,” IEEE Transaction Neural Systems and Rehabilitation Engineering 15(3)327-335 (2007), PMCID 2733849.
  3. Masia, L, Krebs, HI, Cappa, P, Hogan, N. "Design and Characterization of Hand Module for Whole-Arm Rehabilitation Following Stroke," IEEE/ASME Transactions on Mechatronics 12:4:399-407 (2007). 
  4. Roy, A, Krebs, HI, Williams, D, Bever, CT, Forrester, LW, Macko, RM, Hogan, N, “Robot-Aided Neurorehabilitation: A Robot for Ankle Rehabilitation,” IEEE – Transaction Robotics, 25:3:569-582 (2009). 
  5. Michmizos K, Rossi S, Castelli E, Cappa P, Krebs HI. "Robot-Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation." IEEE –Transactions on Neural Systems and Rehabilitation Engineering 23:6: 1056-1067 (2015).
  6. Susko, T; Swaminathan, K; Krebs, HI; MIT-Skywalker: A Novel Gait Neurorehabilitation Robot for Stroke and Cerebral Palsy, IEEE –Transactions on Neural Systems and Rehabilitation Engineering, 24:10:1089-1099 (2016).
  7. Gonçalves, RS; Krebs, HI; MIT-Skywalker: Considerations on the Design of a Body Weight Support System, Journal of NeuroEngineering and Rehabilitation, 14:88:1-11 (2017).
  8. Davidson, JR; Krebs, HI; Variable Impedance Control with an Electrorheological Fluid Actuator, IEEE/ASME –Transactions of Mechatronics, 23:5:2156-2167 (2018).
  9. Huaroto, JJ; Suarez, E; Krebs, HI; Vela, E; A Soft Pneumatic Tactor as a Haptic Wearable Device for Upper Limb Amputees: Towards a Soft Robotic Liner, IEEE – Robotics and Automation Letters, 4:1:17-24 (2018).
  10. Pérez-Ibarra, JC; Siqueira, AAG; Krebs, HI; Real-Time Identification of Gait Events in Impaired Subjects using a Single-IMU Foot-Mounted Device, IEEE – Sensors Journal, 20(5):2617-2624 (2020).
  11. Pérez-Ibarra, JC; Siqueira, AAG; Krebs, HI; Identification of Gait Events in Healthy and Parkinson’s Disease Subjects Using Inertial Sensors: A Supervised Learning Approach, IEEE Sensors Journal, 20(24)14984-14993 (2020).
  12. Pérez-Ibarra, JC; Siqueira, AAG; Krebs, HI; Identification of Gait Events in Healthy and Parkinson’s Disease Subjects Using Inertial Sensors: An Adaptive Unsupervised Learning Approach, IEEE Transactions on Neural Systems and Rehabilitation Engineering,  28(12):2933-2943 (2020).
  13. Gonçalves, RS; Salim, VV; Krebs, HI; Development of a Markerless Control System for the MIT-Skywalker, International Journal of Mechanics and Control, 22:1: 143-152 (2021).
  14. Coelho, RM; Gouveia, J; Botto, MA; Krebs, HI; Martins, J; Real-time walking gait terrain classification from foot-mounted Inertial Measurement Unit using Convolutional Long Short-Term Memory neural network, Expert Systems with Applications, 203,117306 (2022).

       Design and Development of Adaptive Controllers 

  1. Krebs HI, Palazzolo JJ, Dipietro L, Ferraro M, Krol J, Rannekleiv K, Volpe BT, Hogan N. “Rehabilitation Robotics: Performance-based Progressive Robot-Assisted Therapy,” Autonomous Robots, Kluwer Academics 15:7-20 (2003).
  2. Michmizos K, Rossi S, Castelli E, Cappa P, Krebs HI. “Robot-Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation." IEEE –Transactions on Neural Systems and Rehabilitation Engineering 23:6: 1056-1067 (2015). 
  3. Pérez-Ibarra JC; Siqueira AAG, Silva-Couto MA, Russo TLD, Krebs HI “Adaptive Impedance Control Applied to Robot-Aided Neuro-Rehabilitation of the Ankle." IEEE – Robotics and Automation Letters, 4:2:185-192 (2019).

       Translational Effort: rehabilitation robotic and stroke 

  1. Aisen, M.L.; Krebs, H.I.; McDowell, F.; Hogan, N.; Volpe, B.T.; “The Effect of Robot Assisted Therapy & Rehabilitative Training on Motor Recovery Following a Stroke”; Archives of Neurology; 54:443-446 (1997).
  2. Volpe, B.T., Krebs, H.I., Hogan, N., Edelstein, L., Diels, C.M., Aisen, M.; “A Novel Approach to Stroke Rehabilitation: Robot Aided Sensorymotor Stimulation”, Neurology, 54:1938-1944 (2000).
  3. Ferraro, M., Palazzolo, J.J., Krol, J., Krebs, H.I., Hogan, N., Volpe, B.T., “Robot Aided Sensorimotor Arm Training Improves Outcome in Patients with Chronic Stroke,” Neurology, 61:1604-1607 (2003).
  4. Lo A, 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., Bever C.T., Bravata D.M., Duncan P. W., Corn B.H., Malffucci A.D., Nadeua S.E., Conroy S.S., Powell J.M., Huang G.D., Peduzzi P. “Robot-Assisted Therapy for Long-Term Upper-Limb Impairment after Stroke.” New England Journal of Medicine; 362:1772-1783 (2010).
  5. Forrester, LW, Roy, A, Krebs, HI, Macko, RF, “Ankle Training With a Robotic Device Improves Hemiparetic Gait After a Stroke,” Neurorehabilitation and Neural Repair 25:4:369-377 (2011).
  6. Chang J, Lin R, Saul M, Koch P, Krebs H I, Volpe BT, “Intensive robotic training in patients with chronic stroke differentially improves gait,” NeuroRehabilitation 41:1:61-68 (2017). 
  7. Buchwald A, Falconer C, Rykman-Peltz A, Cortes M, Pascual-Leone A, Thickbroom GW, Krebs H I, Fregni F, Gerber LM, Oromendia C, Chang J, Volpe BT, Edwards DJ, “Robotic Arm Rehabilitation in Chronic Stroke Patients With Aphasia May Promote Speech and Language Recovery (but Effect Is Not Enhanced by Supplementary tDCS),” Frontiers in Neurology 9:853 (2018).
  8. Rodgers H, Bosomworth H, Krebs HI, VanWijck F, Howel D, Wilson N, Alvarado N, Andole S, Cohen DL, Dawson J, Fernandez-Garcia C, Finch T, For GA, Francis R, Hogg S, Hughes N, Price CI, Ternent L, Turner DL, Vale L, Wilkes S, Shaw L, “Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial,” The Lancet, 394:10192:51-62 (2019).
  9. Edwards, D. J., M. Cortes, A. Rykman-Peltz, J. Chang, J. Elder, G. Thickbroom, J. J. Mariman, L. M. Gerber, C. Oromendia, H. I. Krebs, F. Fregni, B. T. Volpe and A. Pascual-Leone, "Clinical improvement with intensive robot-assisted arm training in chronic stroke is unchanged by supplementary tDCS." Restor Neurol Neurosci 37(2): 167-180 (2019).
  10. Conroy, S. S., G. F. Wittenberg, H. I. Krebs, M. Zhan, C. T. Bever and J. Whitall, "Robot-Assisted Arm Training in Chronic Stroke: Addition of Transition-to-Task Practice." Neurorehabil Neural Repair 33(9): 751-761 (2019).
  11. Duret, C., Grosmaire A-G, Krebs, H.I. “Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach.” Frontiers in Neurology, vol 10., article 412, pp.1-8 (2019).
  12. Rodgers, H; Bosomworth, H; VanWijck, F; Krebs, HI; Shaw, L; Usual Care: the Big but Unmanaged Problem of Rehabilitation Evidence – Authors’ Reply; Lancet, 385:10221:337-338, (2020).
  13. Park, C; Oh-Park, M; Dohle, C; Bialek, A; Friel, K; Edwards, D; Krebs, HI; You, JH; Effects of Innovative Hip-knee-ankle Interlimb Coordinated Robot Training on Ambulation, Cardiopulmonary Function, Depression, and Fall Confidence in Acute Hemiplegia, Neurorehabilitation, 46(4)577-587 (2020).
  14. Bleckmann Reis, S; Bernardo, WM; Oshiro, CA; Krebs, HI; Conforto, AB; Effects of Robotic Therapy Associated with Noninvasive Brain Stimulation on Upper-Limb Rehabilitation After Stroke: Systematic Review and Meta-analysis of Randomized Clinical Trials, Neurorehabilitation and Neural Repair 35(3) 256–266 (2021).
  15. Fernandez-Garcia, C; Ternent, L; Homer, TM; Rodgers, H; Bosomworth, H; Shaw, L; Aird, L; Andole, S; Cohen, DL; Dawson, J; Finch, T; Ford, GA; Francis, R; Hogg, S; Hughes, N; Krebs, HI;  Price, CI; Turner, DL; van Wijck, F; Wilkes, S; Wilson, N; Vale, L; Economic evaluation of robot-assisted training versus an enhanced upper limb therapy programme or usual care for patients with moderate or severe upper limb functional limitation due to stroke: results from the RATULS randomised controlled trial, BMJ Open, 11(5) p. e042081 (2021).
  16. Moretti, CB; Edwards, DJ; Hamilton, T; Cortes, M; Rykman Peltz, A; Chang, JL; Delbem, ACB;  Volpe, BT; Krebs, HI; Robotic Kinematic measures of the arm in chronic Stroke: part 1 – Motor Recovery patterns from tDCS preceding intensive training, Bioelectronic Medicine 7, 20 (2021).

       Translational Effort: rehabilitation robotic and cerebral palsy 

  1. Fasoli, S.E., Fragala-Pinkham, M., Hughes, R., Hogan, N., Krebs, H.I., Stein, J., “Upper Limb Robotic Therapy for Children with Hemiplegia,” American Journal of Rehabilitation 87:11:929-936 (2008), PMID:18936558.
  2. Krebs, H.I., Landenheim, B., Hippolyte, C., Monterroso, L., Mast, J., “Robot-Assisted Task Specific Training,” Journal of Developmental Medicine & Child Neurology, 51:S4:140-145 (2009).
  3. Krebs HI, Fasoli SE, Dipietro L, Fragala-Pinkham M, Hughes R, Stein J, Hogan N, “Motor Learning Characterizes Habilitation of Children with Hemiplegic Cerebral Palsy,” Neurorehab Neu Repair 26:7:855-860 (2012), PMCID 4688005.
  4. Michmizos K, Rossi S, Castelli E, Cappa P, Krebs HI. "Robot-Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation." IEEE –Transactions on Neural Systems and Rehabilitation Engineering 23:6: 1056-1067 (2015). 

       Ankle and Wrist Psychophysics 

  1. Vaisman L, Dipietro L, Krebs HI, “A Comparative Analysis of Speed Profile Models for Wrist Pointing Movements,” IEEE –Transactions on Neural Systems and Rehabilitation Engineering, 21:5:756-766 (2013), PMCID 4689593.
  2. Michmizos K, Krebs HI. “Pointing with the Ankle: the Speed-Accuracy Tradeoff,” Experimental Brain Research, 232:2:647-657 (2014), PMCID 3919136.
  3. Michmizos K, Krebs HI. “Reaction Time in Ankle Movements: a Diffusion Model Analysis,” Experimental Brain Research, 232:11:3475-3488 (2014), PMCID 4697767.
  4. Michmizos K, Vaisman L, Krebs, HI. "A Comparative Analysis of Speed Profile Models for Ankle Pointing Movements: Evidence that Lower and Upper Extremity Discrete Movements are controlled by a Single Invariant Strategy." Frontiers in Human Neuroscience 8:962 (2014), PMCID 4692803.
  5. Lee H, Rouse E, Krebs H I, “Summary of Human Ankle Mechanical Impedance during Walking,” IEEE- Journal of Translational Engineering in Health and Medicine 4:1-7 (2016). 
  6. Durand S, Rohan CP-Y, Hamilton T, Skalli W, Krebs H I, “Passive Wrist Stiffness: The Influence of Handedness,” JMIR Biomedical Engineering 4:1:e11670 (2019). 
  7. Hamilton T, Durand S, Krebs H I, “The Impact of Aging and Hand Dominance on the Passive Wrist Stiffness of Squash Players: Pilot Study,” IEEE- Transactions on Biomedical Engineering 66:3:656-665 (2018). 
  8. Forner-Cordero A, Pinho JP, Umemura G, Lourenço JC, Mezêncio B, Itiki C, Krebs HI, “Effects of supraspinal feedback on human gait: rhythmic auditory distortion, ” Journal of NeuroEngineering and Rehabilitation 16:159:1-10 (2019).
  9. Watanabe, E; Hirai, H; Krebs, HI; Equilibrium Point-based Control of Muscle-driven Anthropomorphic Legs Reveals Modularity of Human Motor Control during Pedalling, Advanced Robotics, 34(5)328-342 (2020).
  10. Pérez-Ibarra JC; Siqueira AAG; Krebs HI; Identification of Gait Phases in Healthy and Parkinson’s Disease Subjects using Inertial Sensors: A Supervised Learning Approach, IEEE – Sensors Journal, EPUB (2020).
  11. Coelho, RM; Durand, S; Martins, J; Krebs, HI; Multivariable passive ankle impedance in stroke patients: A preliminary study, Journal of Biomechanics, 130:110829 (2022).
  12. Noro, K; Hirai, H; Okamoto, H; Kogawa, D; Kamimukai, C; Nagao, H; Kaneko, Y; Hori, K; Yamamoto, S; Yamada, N; Yajima, T; Matsui, K; Nishikawa, A; Krebs, HI; Asymmetric Inter-limb Coordination in the Legs of 10-11-Year-Old Boys during Overground Sprinting, Journal of the Robotics Society of Japan (JESJ) 40 (3) 259-262 (2022).

       Biomarkers in neurorehabilitation 

  1. Krebs, HI; Hogan, N; Aisen, ML; Volpe, BT; Quantization of Continuous Arm Movements in Humans with Brain Injury, PNAS, 96:4645-4649 (1999), PMCID 16386.
  2. Levy-Tzedek, S; Krebs, HI; Song, D; Hogan, N; Poizner, H; Non-Monotonicity on a Spatio-Temporally Defined Cyclic Task: Evidence of Two Movement Types? Exp Brain Res, 202:4:733-746 (2010), PMCID 2858809.
  3. Bosecker, C; Dipietro, L; Volpe, B; Krebs, HI; Kinematic Robot-Based Evaluation Scales and Clinical Counterparts to Measure Upper Limb Motor Performance in Patients with Chronic Stroke, Neurorehabilitation and Neural Repair 24:62-69 (2010), PMCID 4687968.
  4. Dipietro, L; Krebs, HI; Volpe, BT; Stein, J; Bever, C; Mernoff, ST; Fasoli, SE; Hogan, N; Learning, Not Adaptation, Characterizes Stroke Motor Recovery: Evidence from Kinematic Changes Induced by Robot-Assisted Therapy in Trained and Untrained Task in the Same Workspace, IEEE Trans Neural Sys and Rehab Eng 20:1:48-57 (2012).
  5. Krebs, HI; Krams, M; Agrafiotis, DK; DiBernardo, A; Chavez, JC; Littman, GS; Yang, E; Byttebier, G; Dipietro, L; Rykman, A; McArthur, K; Hajjar, K; Lees, KR; Volpe, BT; Robotic Measurement of Arm Movements After Stroke Establishes Biomarkers of Motor Recovery, Stroke 45:1:200-204 (2014), PMCID 4689592.
  6. Agrafiotis, DK; Yang, E; Littman, GS; Byttebier, G; Dipietro, L; DiBernardo, A; Chavez, JC; Rykman, A; McArthur, K; Hajjar, K; Lees, KR; Volpe, BT; Krams, M; Krebs, HI; Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements, PLoS ONE 16(1): e0245874 (2021).
  7. Umemura, GS; Pinho, JP; Duysens, J; Krebs, HI; Forner-Cordero, A; Sleep deprivation affects gait control, Nature Scientific Reports, 11(1), epp.1-11 (2021).
  8. Moretti, CB; Hamilton, T; Edwards, DJ; Cortes, M; Rykman Peltz, A; Chang, JL; Delbem, ACB;  Volpe, BT; Krebs, HI; Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures, Bioelectronic Medicine 7, 21 (2021).
  9. Coelho, RM; Martins, J; Krebs, HI; Biomarkers for rhythmic and discrete dynamic primitives in locomotion, Nature Scientific Reports 12:20165 (2022).