Effect of human-robot interaction on muscular synergies on healthy people and post-stroke chronic patients

Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment

Recent studies have investigated muscle synergies as biomarkers for stroke, but it remains controversial if muscle synergies and clinical observation convey the same information on motor impairment. We aim to identify whether muscle synergies and clinical scales convey the same information or not. Post-stroke patients were administered an upper limb treatment. Before (T0) and after (T1) treatment, we assessed motor performance with clinical scales and motor output with EMG-derived muscle synergies. We implemented an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to identify the underlying relationships among all variables, at T0 and T1, and a general linear regression model to infer any relationships between the similarity between the affected and unaffected synergies (Median-sp) and clinical outcomes at T0. Clinical variables improved with rehabilitation whereas muscle-synergy parameters did not show any significant change. EFA and CFA showed that clinical variables and muscle-synergy parameters (except Median-sp) were grouped into different factors. Regression model showed that Median-sp could be well predicted by clinical scales. The information underlying clinical scales and muscle synergies are therefore different. However, clinical scales well predicted the similarity between the affected and unaffected synergies. Our results may have implications on personalizing rehabilitation protocols.

Robotic Assistance for Upper Limbs May Induce Slight Changes in Motor Modules Compared With Free Movements in Stroke Survivors: A Cluster-Based Muscle Synergy Analysis

Background: The efficacy of robot-assisted rehabilitation as a technique for achieving motor recovery is still being debated. The effects of robotic assistance are generally measured using standard clinical assessments. Few studies have investigated the value of human-centered instrumental analysis, taking the modular organization of the human neuromotor system into account in assessing how stroke survivors interact with robotic set-ups. In this paper, muscle synergy analysis was coupled with clustering procedures to elucidate the effect of human-robot interaction on the spatial and temporal features, and directional tuning of motor modules during robot-assisted movements. Methods: Twenty-two stroke survivors completed a session comprising a series of hand-to-mouth movements with and without robotic assistance. Patients were assessed instrumentally, recording kinematic, and electromyographic data to extract spatial muscle synergies and their temporal components. Patients' spatial synergies were grouped by means of a cluster analysis, matched pairwise across conditions (free and robot-assisted movement), and compared in terms of their spatial and temporal features, and directional tuning, to examine how robotic assistance altered their motor modules. Results: Motor synergies were successfully extracted for all 22 patients in both conditions. Seven clusters (spatial synergies) could describe the original datasets, in both free and robot-assisted movements. Interacting with the robot slightly altered the spatial synergies' features (to a variable extent), as well as their temporal components and directional tuning. Conclusions: Slight differences were identified in the characteristics of spatial synergies, temporal components and directional tuning of the motor modules of stroke survivors engaging in free and robot-assisted movements. Such effects are worth investigating in the framework of a modular description of the neuromusculoskeletal system to shed more light on human-robot interaction, and the effects of robotic assistance and rehabilitation.

Muscle Synergies-Based Characterization and Clustering of Poststroke Patients in Reaching Movements

Background

A deep characterization of neurological patients is a crucial step for a detailed knowledge of the pathology and maximal exploitation and customization of the rehabilitation therapy. The muscle synergies analysis was designed to investigate how muscles coactivate and how their eliciting commands change in time during movement production. Few studies investigated the value of muscle synergies for the characterization of neurological patients before rehabilitation therapies. In this article, the synergy analysis was used to characterize a group of chronic poststroke hemiplegic patients.

Methods

Twenty-two poststroke patients performed a session composed of a sequence of 3D reaching movements. They were assessed through an instrumental assessment, by recording kinematics and electromyography to extract muscle synergies and their activation commands. Patients’ motor synergies were grouped by the means of cluster analysis. Consistency and characterization of each cluster was assessed and clinically profiled by comparison with standard motor assessments.

Results

Motor synergies were successfully extracted on all 22 patients. Five basic clusters were identified as a trade-off between clustering precision and synthesis power, representing: healthy-like activations, two shoulder compensatory strategies, two elbow predominance patterns. Each cluster was provided with a deep characterization and correlation with clinical scales, range of motion, and smoothness.

Conclusion

The clustering of muscle synergies enabled a pretherapy characterization of patients. Such technique may affect several aspects of the therapy: prediction of outcomes, evaluation of the treatments, customization of doses, and therapies.