Robotic Devices for Rehabilitation Therapy
Clinical Trials of Robotic Therapy Devices
Commercial Translation
Simple Rehabilitation Technologies
Computational Neurorehabilitation
Robotic Devices for Rehabilitation Therapy
The lab developed several of the first robotic devices for rehabilitation therapy after neurologic injury, for people with stroke, spinal cord injury, and cerebral palsy. Such devices are important because they provide the growing number of individuals with neurologic impairment a means to access additional therapy, as well as scientific tools for better quantifying and understanding sensory motor plasticity. Our early work highlighted the primary importance of the patient’s active effort during robotic movement training after stroke. We found that the human motor system has an automatic tendency to slack, reducing its effort in a systematic way when assisted by a robotic therapy device. To address this issue, we helped develop the active assist control paradigm for robotic movement training. One of our active assist controllers that has been influential in the field models the patient’s motor deficit and provides anti-slacking assistance for movement. Over the last 20 years, we have developed more than 20 robotic devices for movement training, ranging from robotic gait training devices for spinal-injured rodents (adopted by over 30 laboratories), to the first robotic telerehabilitation system, to a sophisticated arm exoskeleton for assisting in multi-joint functional movement. Recently, we found that finger proprioception, measured robotically with a novel technique, predicts the ability of people to respond to robotic therapy, which we hypothesize is due to the involvement of a Hebbian-like learning mechanism in robot-assisted rehabilitation.
Reinkensmeyer DJ, Kahn LE, Averbuch M, McKenna-Cole AN, Schmit BD, Rymer WZ (2000) Understanding and treating arm movement impairment after chronic brain injury: Progress with the ARM Guide, Journal of Rehabilitation Research and Development, 37 No. 6, pp. 653-662. PMID: 11321001
Reinkensmeyer DJ, Pang CT, Nessler JA, Painter CC (2002) Web-based telerehabilitation for the upper-extremity after stroke, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 10, no. 2, pp. 102-108. PMID: 12236447
Wolbrecht, ET, Chan V, Reinkensmeyer DJ, Bobrow JE (2008) Optimizing compliant, model-based robotic assistance to promote neurorehabilitation, IEEE Trans Neural Syst and Rehab Eng, 16(3):286-97. PMID: 18586608
Rowe JB, Chan V, Ingemanson ML, Cramer SC, Wolbrecht ET, Reinkensmeyer DJ (2017) Robotic assistance for training finger movement using a Hebbian model: A randomized controlled trial, Neurorehabilitation and Neural Repair 31(8):769-780. PMID: 28803535
Clinical Trials of Robotic Therapy Devices
We have tested robotic devices and training algorithms in several randomized controlled trials at UC Irvine and the Rehabilitation Institute of Chicago, demonstrating that machine-assisted therapy is as effective as or even more effective than conventional rehabilitation therapy after stroke. However, our results also suggest that simpler robotics technology, such as spring-actuated arm supports, or one-degree-of-freedom machines, can achieve similar results. Thus, our laboratory has also advocated for and developed simple technologies for neurologic rehabilitation.
Kahn LE, Zygman ML, Rymer WZ, Reinkensmeyer DJ (2006) Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: A randomized controlled pilot study, Journal of Neuroengineering and Rehabilitation, 3:12. PMCID: PMC1550245
Housman SJ, Scott KM, Reinkensmeyer DJ (2009) A randomized controlled trial of gravity-supported, computer-enhanced arm exercise for individuals with severe stroke, Neurorehabilitation and Neural Repair, 23(5):505-14. PMID: 19237734
Reinkensmeyer DJ, Wolbrecht ET, Chan V, Chou C, Cramer SC, Bobrow JE (2012) Comparison of 3D, assist-as-needed robotic arm/hand movement training provided with Pneu-WREX to conventional table top therapy following chronic stroke, American Journal of Physical Medicine and Rehabilitation, 91 (11 Suppl 3):S232-41. PMCID: PMC3487467
Milot MH, Spencer SJ, Chan V, Allington JP, Klein J, Chou C, Bobrow JE, Cramer SC, Reinkensmeyer DJ (2013) A crossover pilot study evaluating the functional outcomes of two different types of robotic movement training in chronic stroke survivors using the arm exoskeleton BONES, Journal of Neuroengineering and Rehabilitation 10:112. PMCID: PMC3878268
Commercial Translation
This work led to the invention of T-WREX, an arm exoskeleton for rehabilitation training that was licensed by Hocoma, A.G., which in turn developed it into ArmeoSpring, one of the most widely used arm exoskeletons for rehabilitation training, which as of 2023 is in use in ~1500 rehabilitation facilities worldwide. The lab also helped invent the MusicGlove, a wearable system for hand retraining after stroke, now a successful product in use by ~10000 users, sold by Flint Rehabilitation Devices, a company former students and Prof. Reinkensmeyer started. The MusicGlove senses functional grips such as key-pinch grip and pencil grip, and the user makes these grips play a musical computer game similar to Guitar Hero. In home-based testing we have shown that the MusicGlove motivates users to achieve thousands of extra movement exercise repetitions, and those repetitions improve hand function even after chronic stroke.
Sanchez RJ, Liu J, Rao S, Shah P, Smith R, Rahman T, Cramer SC, Bobrow JE, Reinkensmeyer DJ (2006) Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment, IEEE Trans on Neural Systems and Rehabilitation Engineering, 14:3, 378-389. PMID: 17009498
Friedman N, Chan V, Reinkensmeyer AN, Beroukhim A, Zambrao G, Bachman M, Reinkensmeyer DJ (2014) Retraining and assessing hand movement after stroke using the MusicGlove: Comparison with conventional hand therapy and isometric grip training, Journal of Neuroengineering and Rehabilitation Research, 11:76. PMCID: PMC4022276
Zondervan DK, Friedman N, Chang E, Zhao X, Augsburger R, Reinkensmeyer DJ, Cramer SC (2016). Home-based hand rehabilitation after chronic stroke: Randomized, controlled single-blind trial comparing the MusicGlove with a conventional exercise program, Journal of Rehabilitation Research and Development, 53(4):457-72. PMID: 27532880
Simple Rehabilitation Technologies
We have developed other simple but promising technologies for people with a disability. For example, we are developing a new type of lever-drive wheelchair that allows individuals with severe arm weakness to propel themselves bimanually. We believe this chair will replace standard wheelchairs in stroke inpatient units because it will help individuals to avoid nonuse of the affected upper extremity. Another recent promising technology is a “pedometer for the hand,” a wearable sensor called the “Manumeter.” An individual with a stroke can wear this nonobtrusive device, consisting of a wristband and ring, to monitor finger and hand use, motivating greater functional activity of the upper limb.
Zondervan DK, Augsburger R, Bodenhofer B, Friedman N, Reinkensmeyer DJ, Cramer SC (2015). Machine-based, self-guided home therapy for individuals with severe arm impairment after stroke: a randomized controlled trial, Neural Rehabilitation and Neural Repair, 29(5):395-406. PMCID: PMC4959835
Smith BW, Zondervan D, , Lord TJ, Chan V, Reinkensmeyer DJ (2014) Feasibility of a bimanual, lever-driven wheelchair for people with severe arm impairment after stroke, 36th IEEE EMBS Conference, 5292-5.
Friedman N, Rowe JB, Reinkensmeyer DJ, Bachman M (2014) The manumeter: A wearable device for monitoring daily use of the wrist and fingers, Journal of Biomedical and Health Informatics, 18:6, 1804-12. PMID: 25014974
Rowe J, Friedman F, Chan V, Cramer S, Bachman M, Reinkensmeyer DJ (2014) The variable relationship between arm and hand use: A rationale for using finger magnetometry to complement wrist accelerometry when measuring daily use of the upper extremity, 36th IEEE EMBS Conference, 4087-90.
Computational Neurorehabilitation
At the same time that we develop new technologies for neurorehabilitation, we are also developing some of the first computational models of stroke rehabilitation. We believe that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing mathematical models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. The emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. For example, we developed a model of hand strength recovery after stroke based on several key observations from primate neurophysiological experiments: that corticospinal cell activity sums to create a net excitatory flexor or extensor drive to a joint, that different corticospinal cells have different “gains” for exciting motoneuronal pools, and that the relationship between a corticospinal cell’s activity and its individual contribution to muscle force is linear up to a peak firing rate, then saturates for higher activity levels. The model also assumed that the key underlying mechanism of plasticity driving strength recovery after stroke was a reinforcement learning mechanism, in which the sensorimotor system modifies corticospinal cell activations based on repetitive movement experiences, using stochastic random search, such that limb force output is maximized. This model predicts a remarkably broad range of the features of stroke recovery, suggesting that reinforcement learning should be targeted in rehabilitation training to facilitate the search process for alternate neural pathways.
Reinkensmeyer DJ, Iobbi MG, Kahn LE, Kamper DG, Takahashi CD (2003) Modeling reaching impairment after stroke using a population vector model of movement control that incorporates neural firing rate variability, Neural Computation, 15(11):2619-2642. PMID: 14577856
Emken JL, Benitez R, Sideris A, Bobrow JE, Reinkensmeyer DJ (2007) Motor adaptation as a greedy optimization of error and effort, Journal of Neurophysiology, 97(6):3997-4006. PMID: 17392418
Reinkensmeyer DJ, Guigon E., Maier MA (2012) A computational model of use-dependent motor recovery following stroke: optimizing corticospinal activations via reinforcement learning can explain residual capacity and other strength recovery dynamics, Neural Networks 29-30: 60-69. PMCID: PMC3678524
Reinkensmeyer DJ, Burdet E, Casadio M, Krakauer JW, Kwakkel G, Lang CE, Swinnen S, Ward N, Schweighofer N (2015) Computational neurorehabilitation: Modeling plasticity and learning to predict recovery, Journal of Neuroengineering and Rehabilitation, 13(1):42. PMCID: PMC4851823