Neuromechanical Biomarkers for Robotic Neurorehabilitation

Abstract

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the "biomarkers" that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the "Rehabilomics" has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

Keywords: EEG; EMG; exoskeleton; kinematic measurement; motor control; robotic rehabilitation; stroke; upper limb rehabilitation.

Figure 1

Outline of the current training approaches and technologies used in the rehabilitation. A rehabilitation training program (middle) is used to support the multidisciplinary therapy (top). Rehabilitation training can be either conventional or experimental and the latter being found on one or more available technologies (bottom).

Figure 2

Overview of the robot-based rehabilitation technologies, feedback modalities, and rehabilitation training program. Robot-based rehabilitation technologies (top panel), which include the end-effector robots, exoskeletons, and brain–computer interfaces (BCIs), are used in combination with the feedback modalities (middle panel), ranging from electrical stimulation to haptics, electromyography (EMG)-based assistance, and virtual reality, in order to support the rehabilitation training program (bottom panel). Training program includes the assessment sessions to tune and monitor the specific treatment, aimed at promoting the motor learning by stimulating the mechanisms of the brain plasticity. Schematics in the top panel represent the degrees of freedom of movement for the different types of the end-effector robots and exoskeletons.

Figure 3

Summary of the types of the biomarkers and their formal classification. Adapted from Biomarkers Definitions Working Group (2001).

Figure 4

The International Classification of Functioning, Disability, and Health (ICF) model and its components: the model establishes the three levels of human functioning: (1) at the level of body or body part (body structures and functions domain), (2) the whole person (activities domain), and (3) the whole person considered in a social context (participation domain). In this classification, disability implies a certain degree of dysfunction at one or more of these same levels: impairments, activity limitations, and participation restrictions, respectively. It also includes the additional information on the personal and environmental factors (World Health Organization, 2002). Figure is open access courtesy of the National Academies of Sciences (2021) (Trang et al., 2020).

Figure 5

Relationship between the neuromechanical models and the Rehabilomics approach in the development of the motor-related biomarkers. Neuromechanics addresses the real-world behavior by considering the interaction between the context of the motor task, the mechanical structures of the body that are activated to produce the movement, the neural control necessary to produce and modulate the movement, and the specific requirements of the task (top panel). These parameters can be converted into quantitative and qualitative measurements by applying the recording techniques (such as electroencephalography, electromyography (EMG), kinematic measurements, validated clinical scales, and questionnaires) and can be combined to create a personalized profile of the patient (middle panel), in order to assess and predict the motor outcomes related to a specific intervention (bottom panel), before (bottom panel, baseline band in red) and after (bottom panel, post-training band in blue) the rehabilitation, and compare it with a normative band (bottom panel, healthy band in green).

Figure 6

The Rehabilomics research framework uses the WHO ICF model as a foundational representation of function for the biomarker-based assessments of the brain injury response to demonstrate how these biological constructs inform the multidimensional aspects of the motor function. The figure also describes that these functional domains affect the life satisfaction and also have feedback effects on the biological impact on the health and function. Figure is open access courtesy of the National Academies of Sciences (2021) (Trang et al., 2020). Adapted from Wang et al. (2014) with permission.

Figure 7

Main current gaps in the development of the biomarkers that can be grouped into the four main categories as follows: (1) Knowledge, (2) Research, (3) Translational, and (4) Clinical. A detailed description is illustrated in Table 4.