Analysis of kinematic and muscular synergies as assessment tools during rehabilitative robotic training - Budhota, Aamani (2018)
Cerebrovascular accident (commonly referred as Stroke) is the leading cause of disability worldwide [1]. Depending on the location and severity of the lesion, it results in different neurological deficits in motion, sensation, cognition, and emotion affecting their activities of daily living (ADL). Hemiparesis (paralysis on one side of the body) that affects 80% stroke survivors is the major post-stroke motor deficit. The motor damage can be recovered by rehabilitation therapy and exercises. In recent years, to meet the growth in size of the stroke population coupled with the limited availability of trained therapists and financial resources robotic devices have been introduced as a potential solution. The motor recovery depends on many factors :intensity, frequency of robotic therapy and mainly ’control strategy’ adopted in the specific robot. Also, the heterogeneity in stroke location and its effects provides an impetus to develop and validate user-specific adaptive rehabilitation control strategies for faster recovery post-stroke by minimizing the motor-coordination difficulty. This thesis aims to augment the recovery process in stroke subjects via rehabilitation robotics as an optimal assessment tool by validating and assessing novel control algorithms based on motor control principles. Specifically, I address the following: 1) Validate the efficacy of the novel robotic control strategies that adapt haptic force from assistance to disturbance based on user performance to provide optimal training The boost in development and utilization of robotic devices in clinical settings suggest that intense neuro-rehabilitative treatments can significantly improve the functional recovery in post-stroke. However, the choice of control strategy that provides optimal motor recovery is still an open question. H-MAN, a novel upper limb rehabilitation planar robot designed by our team at Nanyang Technological University is employed iv in a longitudinal study. A novel control strategy with underlying principles of ’Challenge Point Framework’, that transits haptic forces from guidance to disturbance based on user’s movement smoothness is implemented. The motor performance of the poststroke subjects undergoing therapy (with robot vs conventional) is assessed. The results indicate that a performance based adaptive controller (with both assistive and disturbing feedback forces) produces significant gains in motor functions. 2) Promote the adaptation of technology-aided tool for sensorimotor assessment In addition to designing better control algorithms, an arguably important factor is the ’lack of clear understating of the level of sensorimotor deficits’. Rehabilitation robotic devices has the potential to provide objective, high resolution, subject-independent sensorimotor assessments. However, their adaptability in clinical setting is yet to be established. With results from two clinical studies, I propose few recommendations to develop standardized robotic tasks and performance metrics similar to clinical scales. Integrating physiological (EMG) measures with task metrics (from robotic tasks) during specific motor task resulted in the superior level of assessment. 3) Investigate the feasibility of muscle synergies to evaluate the task relevance Muscle synergies are believed to be basic building blocks of Central Nervous System (CNS) for motor control and are proposed as a solution for ’Degrees of Freedom’ problem in the literature. Through ’Augmented Synergies’ proposed in this thesis, I aim to explain their task relevance with results from an isometric shoulder task in control healthy population. Relating muscle synergies with action enable us to identify the compensatory strategies subjects. This will in-turn aid in developing a subject-specific training paradigms with muscle synergies as feedback to augment the recovery process.