Neuromechanical Biomarkers for Robotic Neurorehabilitation

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.

Data-Driven Investigation of Gait Patterns in Individuals Affected by Normal Pressure Hydrocephalus

Normal pressure hydrocephalus (NPH) is a chronic and progressive disease that affects predominantly elderly subjects. The most prevalent symptoms are gait disorders, generally determined by visual observation or measurements taken in complex laboratory environments. However, controlled testing environments can have a significant influence on the way subjects walk and hinder the identification of natural walking characteristics. The study aimed to investigate the differences in walking patterns between a controlled environment (10 m walking test) and real-world environment (72 h recording) based on measurements taken via a wearable gait assessment device. We tested whether real-world environment measurements can be beneficial for the identification of gait disorders by performing a comparison of patients’ gait parameters with an aged-matched control group in both environments. Subsequently, we implemented four machine learning classifiers to inspect the individual strides’ profiles. Our results on twenty young subjects, twenty elderly subjects and twelve NPH patients indicate that patients exhibited a considerable difference between the two environments, in particular gait speed (p-value p=0.0073), stride length (p-value p=0.0073), foot clearance (p-value p=0.0117) and swing/stance ratio (p-value p=0.0098). Importantly, measurements taken in real-world environments yield a better discrimination of NPH patients compared to the controlled setting. Finally, the use of stride classifiers provides promise in the identification of strides affected by motion disorders.

Two-dimensional versus three-dimensional measurement of infant cervical active motion

INTRODUCTION

Upright infant active cervical motion (ACM) is difficult to measure accurately by a single examiner. Clinically, physical therapists use visual estimation, which has limited reliability and concurrent validity with gold standards. Consistent, reliable, and valid active motion measurements are needed to document infant status and response to intervention.

PURPOSE

Two-dimensional (2D) photo digitization measurements were compared to three-dimensional (3D) motion analysis measures of infant active neck rotation and lateral flexion.

METHODS

Typically-developing infants participated (five boys, nine girls; 3-7.5 months). An experienced pediatric physical therapist and six novice raters marked photographs and used two different 2D methods to measure cervical rotation and three different 2D methods to measure cervical lateral flexion in photographs. To determine the intra- and interrater reliability of the 2D measurement methods and their concurrent validity with the 3D measures, a subset of lateral flexion photos was marked and measured by 14 experienced pediatric physical therapists.

RESULTS

Novice and experienced examiner measurements of 2D ACM exhibited moderate to excellent intra- and inter-rater reliability. The results of the 2D lateral flexion ACM measurements completed by novice and experienced raters consistently differed from those obtained using the 3D measurement methods.

CONCLUSION

The 2D rotation ACM measurement methods were reliable and demonstrated concurrent validity with the gold standard 3D measure. Infants' lateral flexion ACM examined using 2D measures did not correlate with the results of 3D measurements. This indicated that 2D measurements of active infant cervical lateral flexion could not be used as a valid indicators of 3D motion.