November 29, 2019

Robotic technology in Neuro Rehabilitation

By

Ning Cao, MD   Alberto Esquenazi, MD

Moss Rehab, USA

The development of rehabilitation robots started in the late 1980s. In the past three decades , we have witnessed  an exponential growth of  robotic  technology used in both inpatient and outpatient settings (Masiero et al., 2014). The recent development of improved data processing and engineer technology has made possible to carry out more intense , task specific motor practice provided with the assistance of robots to help patients with central nervous system disease to drive plasticity-dependent recovery (Morone et al., 2017). In particular, the patients who are more impaired benefit most due to being able to perform higher amount of practice than provided by conventional manual therapy. (Schroder, Truijen, Van Criekinge, & Saeys, 2019). However, the utilization of robotic therapy has limitations and challenges as there are several unanswered questions remain: the time window, the dosage and what kind of exercise yield best results?

 

A recent robotic symposium held at MossRehab/Moss Rehabilitation Research Institute in 2019 provided the platform for world leading clinicians and scientists in neurorehabilitation as well as engineering and human motor control experts to share their experience and vision. The topics span from robot design history to present day soft robotics, clinical integration and practical issues. (Molinari, Esquenazi 2016)  The discussions focused on the unsolved issues in the efficacy trial design, robot technology development and feasibility of its application in real-world practice under current health care regulation. Although there are no clear guidelines on how to deploy robotic technology in clinical practice due to limited  evidence from large scale of efficacy trials,  standardized methodologies and patient selection guidelines are needed to demonstrate consistency of outcomes.  Therefore, utilization of robotic intervention may have advantage to help achieve these goals . (Belda-Lois et al., 2011)  In current state, most robotic design primarily  rely on interaction with the peripheral system for motor practice to induce central nervous system plastic changes. As  more sophisticated sensory input from robots or better robot-patient interface design are implemented  we should see improvement in neuroplasticity. However, this bottom-up approach may have limitation to achieve the optimal results. Instead, the addition of a top-down design should also be investigated to further engage attention, and execution of motor learning to facilitate better motor recovery. Another approach is to consider the use of artificial  intelligence (AI)   for decision making to help design therapeutic programs geared toward individual patient’s impairment and desired functional goals. (Fazekas & Tavaszi, 2019).

While deeper understood mechanisms behind neuro recovery are the foundation for innovation and breakthrough of rehabilitation treatments, the consensus is that a collaborative effort between clinicians, scientists and engineers would further improve interventions and patients’ recovery and treatment outcome. The use of robotic technology is widening treatment options and should help us better understand the mechanism of neuro recovery by objective and quantifiable measures of subjective measures.

______________________________________________________________________________________________________________________________________

Belda-Lois, J. M., Mena-del Horno, S., Bermejo-Bosch, I., Moreno, J. C., Pons, J. L., Farina, D., . . . Rea, M. (2011). Rehabilitation of gait after stroke: a review towards a top-down approach. J Neuroeng Rehabil, 8, 66. doi:10.1186/1743-0003-8-66

Fazekas, G., & Tavaszi, I. (2019). The future role of robots in neuro-rehabilitation. Expert Rev Neurother, 19(6), 471-473. doi:10.1080/14737175.2019.1617700

Masiero, S., Poli, P., Rosati, G., Zanotto, D., Iosa, M., Paolucci, S., & Morone, G. (2014). The value of robotic systems in stroke rehabilitation. Expert Rev Med Devices, 11(2), 187-198. doi:10.1586/17434440.2014.882766

Morone, G., Paolucci, S., Cherubini, A., De Angelis, D., Venturiero, V., Coiro, P., & Iosa, M. (2017). Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr Dis Treat, 13, 1303-1311. doi:10.2147/NDT.S114102

Schroder, J., Truijen, S., Van Criekinge, T., & Saeys, W. (2019). Feasibility and effectiveness of repetitive gait training early after stroke: A systematic review and meta-analysis. J Rehabil Med, 51(2), 78-88. doi:10.2340/16501977-2505

Molinari M, Esquenazi A, Agius Anastasi A, Krag Nielsen R, Stoller O, D’Andrea A and Bayon Catalayud M. Rehabilitation Technologies Application in Stroke and Traumatic Brain Injury Patients. In Emerging Therapies in Neurorehabilitation II, Biosystems & Biorobotics J Pons, R Raya and J Gonzales (edts) Springer International Publishing, Switzerland. Chapter 2: 29-64, 2016. 10.1007/978-3-319-24901-8_2.