Haptics-enabled Interactive NeuroRehabilitation Mechatronics: Classification, Functionality, Challenges and Ongoing Research

Abstract This review paper defines NeuroRehabilitation Mechatronics (NRM) as an overlap between two areas of applied science: Bio-mechatronics and Neural Engineering. NRM is an umbrella terminology which covers diverse existing mechatronic technologies that assist patients to regain their motor functions that are lost due to neural and/or physical damage. Two major categories of NRM technologies are identified in this paper: (a) Haptics-enabled Interactive Robotic Neuro-rehabilitation (HIRN) systems, and (b) Assistive Neural Technologies. The main functional difference between the two categories is explained in this paper to provide a better understanding of the boundaries and main functionality of each category. HIRN systems accelerate brain or spinal cord plasticity and recovery, over time. However, assistive neural systems instantly augment the capabilities of an injured individual in performing activities of daily living and the primary goal of this category is not to produce a carryover recovery effect. Accelerated trends of society aging and under-resourced world healthcare systems are discussed as the factors which necessitate further development of NRM technology. This review paper mainly focuses on the first category of NRM systems, i.e., HIRN technology. The paper introduces different classes of this technology and aims to provide a view of the existing technical, technological and control challenges of the current state of this technology together with the ongoing lines of research. For this purpose, the existing commercialized HIRN systems, the specific design, modes of operation, functionality, effectiveness, existing technical issues, control design and possible future developments are studied. In this regard, it is shown that although the effectiveness of HIRN technology is widely accepted and endorsed by official organizations, such as the American Heart Association, there are still conflicting clinical studies with contradictory conclusions. Reasons for these contradictions are discussed in the paper. Two major challenges are identified in this regard, namely conservative patient-robot interaction stabilizing algorithms, and insufficient adaptability of the control parameters to the needs and biomechanics of the patient. Accordingly, based on recent literature, possible future trends for this technology are envisioned such as (a) making the control design of the robots more flexible and intelligent to better match the patients’ needs and biomechanics; (b) designing stabilizing algorithms which can guarantee physical patient-robot interaction stability with minimum conservatism while maximizing the fidelity of force field applied to the patient’s limb; and (c) making it possible to have rehabilitation robots in patients’ homes (e.g., via cloud-based and remote neurorehabilitation) to increase the duration of interactive rehabilitation and thereby improve outcomes while reducing cost.