Volume 15 - 2021 | https://doi.org/10.3389/fnbot.2021.742163
Neuromechanical Biomarkers for Robotic Neurorehabilitation
- 1Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
- 2Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
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.
Motor impairment due to neural diseases, such as stroke, is the third most common cause of the global burden of disease according to the WHO following neonatal conditions and heart diseases (WHO, 2019). In 2016, there were 80.1 million prevalent cases and 13.7 million new stroke cases in the world (Johnson et al., 2019). In particular, motor impairment of the upper limb occurs in 73–88% of the first time stroke survivors and in 55–75% of the patients with chronic stroke (Lawrence et al., 2001). The economic impact of this issue represents €60 billion annually only in the European Union, comprising healthcare costs of €27 billion, social care costs of €5 billion, and €16 billion due to the opportunity cost of the informal care by the support system of the patient (family and friends), along with a loss of the productivity costing €12 billion caused by the morbidity or death (Luengo-Fernandez et al., 2020).
Growing efforts have been done to improve the rehabilitation interventions (Frontera et al., 2017; Hayward et al., 2019), which rely on the effective diagnostic of the motor deficit, the accurate evaluation of the recovery or adaptation, and the optimized treatment for the recovery during the chronic stage. For this reason, a wide variety of strategies has been developed for the purpose of the motor restoration (Lin et al., 2019).
For example, stroke rehabilitation usually involves a rehabilitation training program based on a multidisciplinary approach (including physical, occupational, psychological, and speech therapy), which requires the intervention of many specialists (Figure 1, top).
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).
During the rehabilitation intervention, the training program is continuously tuned and monitored to maximize the functional independence of the patient. These programs aim at promoting the motor learning by stimulating the mechanisms of the brain plasticity, especially during the first 3 months following the brain injury when the probability of the function recovery is greater (Prabhakaran et al., 2008). However, there is solid evidence that the mechanisms of the brain plasticity associated to recovery may continue many years after stroke and the chronic patient can also benefit from the rehabilitation interventions (Irimia et al., 2018).
The rehabilitation training itself can be either conventional or experimental (Figure 1, middle) (Lin et al., 2019) and the latter supported by one or more available technologies such as robotics, muscle and brain stimulation, and virtual reality (Figure 1, bottom). In particular, in the recent years, robot-mediated therapy has been increasingly used in the rehabilitation to enable the highly adaptive, repetitive, intensive, and quantifiable physical training (Semprini et al., 2018; Iandolo et al., 2019). Robot-based rehabilitation is mainly supported by the end-effector robots, exoskeletons, and brain–computer interfaces (BCIs) (Figure 2, top panel), used in combination with real-time feedback to the patient, which is based on a feedback technology such as electrical stimulation, haptics, electromyography (EMG)-based assistance, and/or virtual reality (Figure 2, middle panel). The combination of these technologies can be used to create a personalized rehabilitation training program (Figure 2, bottom panel). For a comprehensive review on the current robotic technologies applied on the neurorehabilitation see (Nizamis et al., 2021).
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.
What is a Biomarker and its Relevance for Robot-Assisted Rehabilitation?
Many studies have shown that multidisciplinary robot-assisted training results in an additional reduction of motor impairments in comparison to the traditional rehabilitation approach in the different stages of recovery (Franceschini et al., 2020; Khalid et al., 2021). These effects on motor learning are mainly due to the precise feedback and assistance provided to the patients during practice. It has been demonstrated that not only this can improve the motivation of the patient, engagement, and adherence to the treatment, but also enhance the learning and recovery (Schmidt and Young, 1991; Zhang et al., 2017).
Although there are many studies addressing the clinical benefits of these interventions, the comparison of the clinical effectiveness of the robot-assisted training has had diverse results, with some clinical trials showing that the robot-assisted training did not improve motor function when compared with usual care (Rodgers et al., 2019), thus leading to the controversy in the field.
This has been primarily attributed to the individual clinical factors (age, stroke severity, infarct location, and comorbidities) and the unique profile of the patient (Prabhakaran et al., 2008), which lead to the need of tailoring the treatment and developing the useful parameters to interpret the heterogeneous clinical outcomes (Irimia et al., 2018). In this regard, the robot-assisted interventions provide the therapists with the objective, accurate, and repeatable measurements of the functions of the patient, which allow to objectively follow progress, to evaluate the effectiveness of the different treatments, or to adapt to the specific needs of the patients.
These measurements are formally named biomarkers. The term refers to a broad subcategory of the medical signs, which are “indicators of the normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions accurately and reproducibly measured from outside the patient” (Biomarkers Definitions Working Group, 2001). Thus, a biomarker can be molecular, histologic, radiographic, or physiologic and they can be formally classified according to its alleged application (Figure 3). The use of the biomarkers that have been well-characterized and validated across a variety of treatments and populations has become common in the research and in the clinical practice (Mayeux, 2004).
Figure 3. Summary of the types of the biomarkers and their formal classification. Adapted from Biomarkers Definitions Working Group (2001).
Nevertheless, in many cases, the level of evidence for the validation of the biomarkers does not allow their translation to clinical practice. This is the case of motor rehabilitation, where there is a current need for the objective evaluation and the correct prediction of the outcomes by using the robust biomarkers specific to an intervention. Thus, robot-assisted rehabilitation may help to improve the motor rehabilitation after stroke, traumatic brain injury, and the other neurologic disorders.
For example, the randomized controlled trials comparing the robot-assisted arm training with the other rehabilitation or placebo interventions showed improvement of the activities of daily living, arm function, and arm muscle strength in the post-stroke individuals (Mehrholz et al., 2018). However, the huge variations in terms of intensity, duration, amount of training, type of treatment, characteristics of the participant, and measurements used so far suggest caution in the interpretation of these results (Mehrholz et al., 2018). In this regard, the biomarkers might help to harmonize these results by providing more accurate information and helping to identify the proper respondent at the different technologies, enhancing the stratification of the patients. Nevertheless, the majority of this research is still exploratory: while the literature indicates a growing number of the potential biomarkers and indicators for the several pathologies characterized by the motor impairments, a gold standard rehabilitation-focused biomarker is still lacking at the clinical and preclinical levels (Wagner, 2014).
The growing number of clinical studies evaluating the effects of robotic training on rehabilitation generally relies on the traditional human-administered clinical scales, which often lack of resolution to detect subtle changes in the performance of the patient and can be subjective to the expertise of the physician. Recent studies are indicating that these clinical behavioral biomarkers are less predictive of the motor recovery compared to the neurophysiological biomarkers (Cramer et al., 2007; Quinlan et al., 2018; Lim et al., 2020).
Rehabilitation biomarkers are gradually evolving from simple clinical behavioral metrics based on quantitative scales to brain imaging and neurophysiological measurements (Babrak et al., 2019). There are many studies addressing the relationship between the validated clinical scales and instrumented biomarkers (Zollo et al., 2011; Kim et al., 2016; Connell et al., 2018; Do Tran et al., 2018; Saes et al., 2019; Rech et al., 2020; Riahi et al., 2020; Agrafiotis et al., 2021), but a standardized approach is still missing.
In this regard, efforts like the International Classification of Functioning, Disability, and Health (ICF), proposed by the WHO in 2001 (Stucki et al., 2002; World Health Organization, 2002), have been developed as a standardized framework of assessment, with the purpose of providing an integrated biopsychosocial model to describe the functioning in the rehabilitation (Figure 4). This model describes the health condition as influenced by the several factors related not only to the conditions of body structures and functions as a consequence of the impairment, but also to the repercussions on the activities and social participation of the subjects, which are, in turn, related to both the environmental barriers and personal factors. The ICF model allows for an assessment of the degree of disability regardless of the health condition, etiology of the disease, cultural background, age group, and gender (World Health Organization, 2002).
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).
Thus, this framework introduces the need of a standardized and multidisciplinary approach for the development of measurement that can describe and evaluate the motor rehabilitations focusing on the unique (and multidomain) profile of the patient. Currently, this model has been used as a reference for the clinical practice, but its use in the research is still limited, mostly due to a lack of correlation in the literature between the clinical outcome measures and quantitative parameters such as kinematic and neurophysiological measurements. The categorization of these parameters in accordance with the ICF domains and their connection with clinical scales could provide the additional insights for the selection of the appropriate biomarkers and clinical scales in the assessment of the motor performance (see section Toward Personalized Neurorehabilitation: Adopting the Rehabilomics Approaches in the Robot-Assisted Rehabilitation for the further details).
Focus on Stroke: Current Biomarkers Related to Motor Recovery
Among the neurological diseases characterized by motor impairments, stroke is one of the most commonly studied. In this context, viable biomarkers of motor recovery have evolved along with brain imaging and neurophysiological technology in the past decades. While brain imaging techniques such as diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional MRI (fMRI), and conventional structural MRI (sMRI) have been systematically used for establishing the neurologic biomarkers (Buma et al., 2010; Kim and Winstein, 2017), the neurophysiological techniques [such as electroencephalography (EEG) and surface EMG (sEMG)] and kinematic measurements have been explored mainly in the research contexts (Stinear, 2017). Thus, regardless of the evident evolution, there is a shortfall in the high-level evidence for defining the most critical biomarkers of the motor rehabilitation based on the electrophysiology and kinematics measurements (Kim and Winstein, 2017).
In view of the wide variety of the biomarkers under development and their heterogeneity of the applications in the rehabilitation (depending on the neuroimaging method, condition of the patient, training modality, etc.), the following subsections provide an non-exhaustive overview of the biomarkers for the robot-assisted upper limb rehabilitation post-stroke focused on: (1) sEMG, which has been considered a “muscle activation measurement tool” in the past four decades, leading to a wide exploration in neurorehabilitation (Campanini et al., 2020) (Table 1); (2) EEG, which is widely used in the different clinical areas as non-invasive real-time tool to extract the features from the electrical activity of brain and presents high correlation with the various different pathologies (Table 2); and (3) robotic-based kinematic measurements, which have been extensively explored as a potential tool for assessing the motor functions (Table 3).
Table 1. List of the electromyography (EMG)-based biomarkers related to the motor rehabilitation focused on stroke.
Table 2. List of the electroencephalography (EEG)-based biomarkers related to the motor rehabilitation focused on stroke.
While there exists a wide variety of the kinematic parameters used to describe the temporal and spatial features of the endpoint or joint movement (such as the position, velocity, movement time, or the execution of a task or action), systematic reviews on the kinematic assessments show that these parameters are poorly standardized and the unbiased clinimetrics is rarely addressed (Schwarz et al., 2019).
Due to the great number of biomarkers in this category and their large variability across the literature in terms of the nomenclature and level of evidence, examples in Table 3 are presented according to the guidelines introduced in Schwarz et al. (2019), in which the clinically relevant kinematic measurements for the upper limb after stroke were selected from a large database according to their available clinimetric evidence and clustered according to their presumed physiological interpretation for both the three-dimensional (3D) and two-dimensional (2D) tasks. With respect to the previous efforts in standardization and the expertise of the authors, this classification considers the following categories:
1. Efficacy: Indication if the task or the objective was successfully achieved or not.
2. Efficiency: Quantification of the performance of a task.
3. Precision: Description of the variability of performance of the goal-directed movements.
4. Accuracy: Quantification of error of the performed movements compared with an optimal movement.
5. Smoothness: Deviation of the velocity profile from an optimal profile.
6. Spatial posture: Position-related aspects of the joints.
7. Temporal posture: Time-related aspects of the joints.
8. Workspace: Description of the reachable area or volume with a specific joint.
9. Speed: Velocity of the performance of the movements.
Toward Personalized Neurorehabilitation: Adopting the Rehabilomics Approaches in the Robot-Assisted Rehabilitation
The idea of the state-of-the-art biomarker platforms and the technologies focused on rehabilitation have led to the concept of the “Rehabilomics” (Wagner, 2010), i.e., a transdisciplinary evaluation of the biomarkers to understand the rehabilitation-relevant phenotypes related to biology, function, prognosis, treatment, and recovery for the patients with disabilities (Wagner, 2010).
In this context, the development of the biomarkers based on the models of the motor control mechanisms needs to take into account how the real-world behavior emerges from the interaction between the neural, biomechanical, and environmental dynamics, in order to understand the healthy functions, disability, and rehabilitation progress. This perspective is the main purpose of the studies of the neuromechanics (Nishikawa et al., 2007; Valero-Cuevas, 2016), which aims at modeling the healthy movement and studying how these patterns change in the motor deficits, mainly for the robotic design and control (Pham et al., 2014; Szczecinski et al., 2017; Kühn et al., 2018). The research on the biomarker has been mainly focused in a physiological perspective and there is a need for the methodological approaches based on the neuromechanical assessments. In this scenario, the Rehabilomics can provide the new tools to better understand the motor rehabilitation from a multidisciplinary perspective (Figure 5).
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).
Since the Rehabilomics has been primarily focused on the proteomics, genomics and metabolomics (Wagner and Zitelli, 2013; Skriver et al., 2014; Wagner, 2017; Wagner and Kumar, 2019), kinematics measures, and neuroimaging and electrophysiological recordings, they have also been widely explored as the potential biomarkers in the field of the robot-assisted neurorehabilitation (Philips et al., 2017; Belfatto et al., 2018; Pirondini et al., 2018; Krauth et al., 2019; Mane et al., 2019; Irastorza-Landa et al., 2021). In particular, the kinematics and electrophysiological indicators can be exploited as biomarkers, mainly because they are non-invasive and portable techniques, suitable for measuring the activity in both the acute and chronic phases.
In addition, the Rehabilomics approach has been directly related to the ICF framework (as shown in Stinear, 2017 and Section What Is a Biomarker and Its Relevance for Robot-Assisted Rehabilitation? Figure 4) by linking the profile of the patients (personal factors, their conditions and complications, and physiological environment) to the different dimensions of the ICF model (Figure 6). In this approach, the biomarkers could improve the stratification of the patients based on their individual biopsychosocial profiles, which could increase the statistical power of the trials to detect the intervention effects and enhance the outcomes assessment (Wagner, 2017). Thus, the consideration of such biomarkers into the ICF domains by using the Rehabilomics approach is most likely the next step in developing an integrated assessment of the robot-assisted rehabilitation treatments, optimizing clinical assessment procedures, and enhancing the effectiveness of such interventions (Do Tran et al., 2018).
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.
Current Gaps in the Area
Currently, both the robotic-based interventions and the potential neurorehabilitation-based biomarkers are the presenting limitations, which are preventing their translation into the clinical practice. These can be clustered into knowledge, research, clinical, and translational gaps, which are summarized in Figure 7 and further described in Table 4.
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.
Latest Trends and Perspectives in the Field
In the previous section, some insights and future research directions have been identified. Highlights in these emerging topics are summarized in the following subsections.
Digital Biomarkers Based on At-Home Digital Surveillance
Growing efforts in the field of mobile health are being done for improving rehabilitation therapies. On one side, the possibility of self-assessment, large-scale population screening, and continuous monitoring through mobile applications are giving rise to the development of self-paced at-home therapies by using the commonly available devices and gadgets such as smartphones and smartwatches (Zhang et al., 2020). On the other hand, the current trends on telerehabilitation (providing the rehabilitation therapies through the information and communication technologies; Cramer, 2016) have opened the possibility of providing the rehabilitation training remotely in the home of the patient or the other environments outside of the typical rehabilitation setting. The development of such remote tools for the rehabilitation management is creating a new field in the digital biomarkers (which are defined as biomarkers collected and measured by means of the digital devices; Babrak et al., 2019) related to the motor rehabilitation.
In particular, in stroke rehabilitation, wearable motor sensors are being combined with digital biomarkers to monitor the longitudinal performance of the patients (Hou et al., 2018). The state-of-the-art biomarkers such as functional range of motion (fROM) for the quantification of upper limb reaching in the 3D visualizations, convergence points (CPs) for walking analysis based on the gait parameters, and physical activity (PA) for evaluation of the energy consumption (Derungs et al., 2020) are opening the door for the exploitation of the digital biomarkers in the rehabilitation.
Initiatives such as the Parkinson's Disease Digital Biomarker DREAM Challenge (Sieberts et al., 2021) are boosting the design of the digital biomarkers-based applications for the rehabilitation. For instance, recent algorithms for the self-reported symptoms of the Parkinson's disease (Ryu et al., 2019; Zhang et al., 2020) and the biomarker-based assessments of the tremor and bradykinesia through a wrist-worn wearable (Mahadevan et al., 2020) have been published. Additionally, the exploitation of the personal devices such as the smartphones and tablets has led to the birth of the novel methods to evaluate the performance of the users. For example, tappigraphy is a non-invasive and unobtrusive method based on the screen tapping actions, which contains the important indicators of homeostasis both in the healthy and pathological conditions: for some neurological diseases, it has been already shown the efficacy of the tapping activity for the prognostic and diagnostic functions (Gindrat et al., 2015; Balerna and Ghosh, 2018; Akeret et al., 2020; Duckrow et al., 2021; Ghosh, 2021). These new type of biomarkers need not only to be clinically relevant to correctly assess the status of the patient (Manta et al., 2020), but also have to be robust enough to be recorded and interpreted under the different conditions and by the different users. Another major challenge is the requirement of the high-quality engagement of the patient necessary to obtain and deploy these biomarkers (Goldsack et al., 2021).
Creating the Computational Neurorehabilitation Models for the Patient-Tailored Therapies
Computational models in neurorehabilitation (CMN) are encompassed by the personalized medicine and computational intelligence. CNM describes the complex human motor system in terms of the interactions between the sensorimotor activity and the behavioral outcomes of the patient by applying a computational model of the mechanisms of plasticity that are involved in recovery (Reinkensmeyer et al., 2016). It has set a framework to design the clinical experiments by simulating the rehabilitative parameters instead of using the current trial-and-error approach. This could not only allow to optimize the therapy design, but also personalize it in terms of content, timing, dosage, scheduling, etc., according to the profile of the individuals (Reinkensmeyer et al., 2016).
The concept of the patient-tailored therapies by using the computational neurorehabilitation is currently exploring the development of the new biomarkers from three main perspectives: (1) a neuroscience perspective (i.e., developing the mathematical models of the mechanisms of the activity-dependent plasticity; Reinkensmeyer et al., 2016), (2) a clinical perspective in which the clinically relevant biomarkers are being identified and used to create the algorithms for decision-making (i.e., prescribing the individualized intensities of the rehabilitation; Jeffers et al., 2018), and (3) a personalized biomechanical and sensor perspective in which the biomarkers are being used to complement the human movement analysis and wearable system design (Derungs and Amft, 2020). In particular, biomechanical simulations and motion data models are being used to create the personalized “digital twins.” This concept refers to the digital representation of the patient based on their profile health (Schwartz et al., 2020), which allows to simulate the different types of the biomarkers through this model, making the predictions and simulations of the evolution of the patient (Voigt et al., 2021) and testing and evaluating the wearable robotic systems before deploying the physical prototypes (Derungs and Amft, 2020).
Developing an Integrated Treatment of Stroke-Induced Motor, Cognitive, and Affect-Related Deficits
Following the notion that the robot-assisted neurorehabilitation demands a highly patient-tailored process, which entails the identification of the unique needs, priorities, and recovery profile of the patient, the integration of the biomarkers belonging to the different domains (sensorimotor, cognitive-behavioral, autonomic, psychological, and psychosocial) is being undertaken (Bui and Johnson, 2018; Zariffa, 2018; Picelli et al., 2020). The idea of developing the profile of the patient that combines the relevance of the multifactorial biomarkers is a new approach that is starting to being explored, with the design of the dedicated study protocols for defining a related profile of the biomarkers of long-term recovery after stroke (Picelli et al., 2020) and the exploration of the novel biomarkers related to the other aspects of the motor function rather than sensorimotor such as alterations in the body representations (Maggio et al., 2021), eye–hand coupling assessment (Rizzo et al., 2017), quantification of visuospatial neglect (VSN) (Svaerke et al., 2019), and somatic (or cognitive-related) biomarkers (Martinez-Pernia, 2020). Additionally, the combination of the neuroimaging technologies is supporting this multifactorial exploration by combining EMG, EEG, and inertial data to obtain the rehabilitation-relevant biomarkers (Gao et al., 2018; Zhang et al., 2019; Picelli et al., 2020).
This approach could lead to the potential development of reliable one-off measures to evaluate the functionality of a single patient by developing a biomarker profile in which a reference value is present. The reference value could be a curve adjusted to the stratification of the patient with respect to the healthy population and, therefore, the value obtained from the patient could be compared against this reference, allowing to quantify the motor function in a single shot. It would be necessary to obtain and validate these reference values (or profiles) by collecting the standardized information from a large number of the patients and healthy subjects.
These multidisciplinary assessments must take into account the feasibility of their implementation in the clinical practice in which the time spent for the assessment and the level of the invasiveness and comfort for the patient are major constraints. Hence, the optimization of the calculation of biomarkers, by means of the dimensionality reduction and standardization, along with the inclusion of user-centered design principles to the process of developing new interventions and biomarkers (Markopoulos et al., 2011; Almenara et al., 2017; Wentink et al., 2019), will lead not only to the creation of the truly personalized and integrated rehabilitation technologies, but also to a significant reduction in the time spent in assessing the status of the patient.
In this study, the most current relevant biomarker candidates for the rehabilitation were shortlisted and for many of them promising correlations with clinical outcomes have been found. Their use in the robot-assisted rehabilitation is at a point of the fast advancement due to the diffusion of the robotic technologies and new frameworks for multidisciplinary work such as the concept of the Rehabilomics. In particular, the development of the biomarkers based on EEG, EMG, and kinematics is a promising area in which exploratory work reported in the literature has been increasing in the recent years. Nevertheless, there are still important gaps in the area to overcome and the future studies should take into consideration more robust cross-validation protocols, addressing issues such as standardized procedures, proper sample sizes, and stratification of the patient. Further research is needed in order to identify the most informative biomarker (or set of biomarkers) to design the more optimized and patient-tailored rehabilitation therapies. This will also provide the better understanding of the prognosis and recovery and help to developing the more quantitative grounded treatment strategies to improve the recovery. This approach potentially allows a deeper understanding of the robot-assisted rehabilitation process and its interaction with the human motor control and behavioral mechanisms, boosting the development of the better human-inspired assistive technologies.
FG, MC, and MS conceived the study and reviewed the figures. FG and MS designed the figures. FG wrote the first draft of the manuscript and prepared the figures. All authors contributed to the writing of the manuscript and approved its final version.
This study was supported by the Istituto Nazionale Assicurazione Infortuni sul Lavoro (INAIL) (Project grant number PR19-RR-P2).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The authors gracefully acknowledge Silvia Chiappalone for providing the graphics of Figure 2.
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Keywords: robotic rehabilitation, upper limb rehabilitation, motor control, EMG, EEG, kinematic measurement, stroke, exoskeleton
Citation: Garro F, Chiappalone M, Buccelli S, De Michieli L and Semprini M (2021) Neuromechanical Biomarkers for Robotic Neurorehabilitation. Front. Neurorobot. 15:742163. doi: 10.3389/fnbot.2021.742163
Received: 15 July 2021; Accepted: 22 September 2021;
Published: 27 October 2021.
Edited by:Diego Torricelli, Consejo Superior de Investigaciones Científicas (CSIC), Spain
Reviewed by:Ye Ma, Ningbo University, China
Longbin Zhang, Royal Institute of Technology, Sweden
Marta Gandolla, Politecnico di Milano, Italy
Copyright © 2021 Garro, Chiappalone, Buccelli, De Michieli and Semprini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Marianna Semprini, email@example.com