Publications

Introduction: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods: We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients' hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results: Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model's performance to estimate FM-UE scores reaches an accuracy of R-2: 0.38 with an error (sigma: 12.8). Next, we evaluate its reliability (r = 0.89 for test-retest), longitudinal external validity (95% true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements (R-2: 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory (R-2: 0.40) and Barthel Index (R-2: 0.35). Conclusions: Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.

JTD Keywords: interactive feedback, motion classification, motion sensing, multivariate regression, posture monitoring, rehabilitation, stroke, Adult, Aged, Analytic method, Arm movement, Article, Barthel index, Brain hemorrhage, Cerebrovascular accident, Chedoke arm and hand activity inventory, Clinical protocol, Cognitive defect, Computer analysis, Controlled study, Convergent validity, Correlation coefficient, Disease severity, External validity, Female, Fugl meyer assessment for the upper extremity, Functional assessment, Functional status assessment, General health status assessment, Hemiparesis, Human, Interactive feedback, Ischemic stroke, Kinematics, Major clinical study, Male, Mini mental state examination, Motion classification, Motion sensing, Motor analog scale, Movement, Multivariate regression, Muscle function, Posture monitoring, Probability, Recovery, Rehabilitation, Reliability, Retrospective study, Stroke, Stroke patient, Test retest reliability, Therapy, Total goal directed movement, Upper extremities, Upper limb, Upper-limb, Wolf motor function test

Reaching and grasping (R&G) is a skilled voluntary movement which is critical for animals. In this work, we aim to identify muscle synergy patterns from R&G movements in rats and show how these patterns can be used to characterize such movements and investigate their consistency and repeatability. For that purpose, we analyzed the electromyographic (EMG) activity of five forelimb muscles recorded while the animals were engaged in R&G tasks. Our dataset included 200 R&G attempts from three different rats. Non-negative matrix factorization was used to decompose EMG signals and extract muscle synergies. We compared all pairs of attempts and created cross-validated models to study intra- and inter-subject variability. We found that three synergies were enough to accurately reconstruct the EMG envelopes. These muscle synergies and their corresponding activation coefficients were very similar for all the attempts in the database, providing a general pattern to describe the movement. Results suggested that the movement strategy adopted by an individual in its different attempts was highly repetitive, but also resembled the strategies adopted by the other animals. Inter-subject variability was not much higher than intra-subject variability. This study is a proof-of-concept, but the proposed approaches can help to establish whether there is a stereotyped pattern of neuromuscular activity in R&G movement in healthy rats, and the changes that occur in animal models of acute neurological injuries. Research on muscle synergies could elucidate motor control mechanisms, and lead to quantitative tools for evaluating upper limb motor impairment after an injury.

JTD Keywords: Electromyography, Motor control, Muscle synergies, Reaching and grasping, Upper limb

Robot-assisted rehabilitation therapies usually focus on physical aspects rather than on cognitive factors. However, cognitive aspects such as attention, motivation, and engagement play a critical role in motor learning and thus influence the long-term success of rehabilitation programs. This paper studies motor-related EEG activity during the execution of robot-assisted passive movements of the upper limb, while participants either: i) focused attention exclusively on the task; or ii) simultaneously performed another task. Six healthy subjects participated in the study and results showed lower desynchronization during passive movements with another task simultaneously being carried out (compared to passive movements with exclusive attention on the task). In addition, it was proved the feasibility to distinguish between the two conditions.

JTD Keywords: Electrodes, Electroencephalography, Induction motors, Medical treatment, Robot sensing systems, Time frequency analysis, Biomechanics, Cognition, Electroencephalography, Medical robotics, Medical signal detection, Medical signal processing, Patient rehabilitation, Attention, Cognitive aspects, Desynchronization, Engagement, Motivation, Motor learning, Motor task, Motor-related EEG activity, Physical aspects, Robot-assisted passive movement detection, Robot-assisted rehabilitation therapies, Upper limb