Affiliations: [a] Department of Computer Science, Rutgers University, Piscataway, NJ, USA
| [b] Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Correspondence:
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Address for correspondence: Konstantinos P. Michmizos, Laboratory for Computational Brain, Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854, USA. Tel.: +1 848 445 8841; Fax: +1 732 445 0537; E-mail: konstantinos.michmizos@cs.rutgers.edu.
Abstract: BACKGROUND: Robot-aided sensorimotor therapy imposes highly repetitive tasks that can translate to substantial improvement when patients remain cognitively engaged into the clinical procedure, a goal that most children find hard to pursue. Knowing that the child’s brain is much more plastic than an adult’s, it is reasonable to expect that the clinical gains observed in the adult population during the last two decades would be followed up by even greater gains in children. Nonetheless, and despite the multitude of adult studies, in children we are just getting started: There is scarcity of pediatric robotic rehabilitation devices that are currently available and the number of clinical studies that employ them is also very limited. PURPOSE: We have recently developed the MIT’s pedi-Anklebot, an adaptive habilitation robotic device that continuously motivates physically impaired children to do their best by tracking the child’s performance and modifying their therapy accordingly. The robot’s design is based on a multitude of studies we conducted focusing on the ankle sensorimotor control. In this paper, we briefly describe the device and the adaptive environment we built around the impaired children, present the initial clinical results and discuss how they could steer future trends in pediatric robotic therapy. CONCLUSIONS: The results support the potential for future interventions to account for the differences in the sensorimotor control of the targeted limbs and their functional use (rhythmic vs. discrete movements and mechanical impedance training) and explore how the new technological advancements such as the augmented reality would employ new knowledge from neuroscience.
