Self-Paced Reaching after Stroke: A Quantitative Assessment of Longitudinal and Directional Sensitivity Using the H-Man Planar Robot for Upper Limb Neurorehabilitation

Hussain A, Budhota A, Hughes CML, et al. Self-Paced Reaching after Stroke: A Quantitative Assessment of Longitudinal and Directional Sensitivity Using the H-Man Planar Robot for Upper Limb Neurorehabilitation. Frontiers in Neuroscience. 2016;10:477

Analysis of kinematic and muscular synergies as assessment tools during rehabilitative robotic training

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.Doctor of Philosophy (IGS

Upper extremity proprioception in healthy aging and stroke populations, and the effects of therapist- and robot-based rehabilitation therapies on proprioceptive function

The world’s population is aging, with the number of people ages 65 or older expected to surpass 1.5 billion people, or 16% of the global total. As people age, there are notable declines in proprioception due to changes in the central and peripheral nervous systems. Moreover, the risk of stroke increases with age, with approximately two-thirds of stroke-related hospitalizations occurring in people over the age of 65. In this literature review, we first summarize behavioral studies investigating proprioceptive deficits in normally aging older adults and stroke patients, and discuss the differences in proprioceptive function between these populations. We then provide a state of the art review the literature regarding therapist- and robot-based rehabilitation of the upper extremity proprioceptive dysfunction in stroke populations and discuss avenues of future research.NMRC (Natl Medical Research Council, S’pore)Published versio

Evaluation of a wearable biosensor to monitor potassium imbalance in patients receiving hemodialysis

Background: A non-invasive method capable of promptly detecting clinically important blood potassium changes could benefit care and safety for significant patient populations, including those with end-stage kidney disease. Methods: A total of 96 patients receiving maintenance hemodialysis participated in service evaluations of a wearable biosensor across four renal centers (two in UK, one in US and one in Saudi Arabia). All the patients had standard blood tests taken before and after their routine hemodialysis sessions and the results were used as reference potassium measurements for simultaneous, photoplethysmography-based, non-invasive digital samples obtained by the wearable biosensor. These digital samples were subsequently analyzed utilizing a machine learning model designed to identify excursions in serum potassium concentration by quantifying changes across a ternary classification strategy— hyperkalemia (K+ > 5.2 mEq/L), normokalaemia (K+ 3.5–5.2 mEq/L) or hypokalemia (K+  5.2 mEq/L) or normokalemia (3.5 ≥ K+ ≤ 5.2). The total weighted recall of the biosensor and model was 86%. The overall weighted precision of the model was 86% with an F1-score of 0.86 indicating that the model achieved both high sensitivity and a low rate of false positives Conclusions: This evaluation demonstrates wearable technology capable of identifying important blood potassium changes outside of the normal reference range, in a group of patients receiving hemodialysis

Apparatus and procedure.

<p>(A) Participant (blindfolded) holding the handle of H-Man, the robotic device employed in the study. (B) Experimental procedure: starting from the initial position (1), H-Man placed the handle on the target position and held it there for 2 seconds (2), after which the handle was returned to the initial position to start the new movement towards the same target (3), which was stopped via a hand-held button by the investigator when the participant verbally indicated that the position of the handle matched the target (4). The handle was then returned to the initial position for the following trial.</p

Pairwise comparisons of signed errors between each subject and the control group in the 3 directions.

<p>Mean differences are evaluated as Control-S. Significant differences are marked with an asterisk.</p

Proprioceptive assessment in clinical settings: Evaluation of joint position sense in upper limb post-stroke using a robotic manipulator

<div><p>Proprioception is a critical component for motor functions and directly affects motor learning after neurological injuries. Conventional methods for its assessment are generally ordinal in nature and hence lack sensitivity. Robotic devices designed to promote sensorimotor learning can potentially provide quantitative precise, accurate, and reliable assessments of sensory impairments. In this paper, we investigate the clinical applicability and validity of using a planar 2 degrees of freedom robot to quantitatively assess proprioceptive deficits in post-stroke participants. Nine stroke survivors and nine healthy subjects participated in the study. Participants’ hand was passively moved to the target position guided by the H-Man robot (Criterion movement) and were asked to indicate during a second passive movement towards the same target (Matching movement) when they felt that they matched the target position. The assessment was carried out on a planar surface for movements in the forward and oblique directions in the contralateral and ipsilateral sides of the tested arm. The matching performance was evaluated in terms of error magnitude (absolute and signed) and its variability. Stroke patients showed higher variability in the estimation of the target position compared to the healthy participants. Further, an effect of target was found, with lower absolute errors in the contralateral side. Pairwise comparison between individual stroke participant and control participants showed significant proprioceptive deficits in two patients. The proposed assessment of passive joint position sense was inherently simple and all participants, regardless of motor impairment level, could complete it in less than 10 minutes. Therefore, the method can potentially be carried out to detect changes in proprioceptive deficits in clinical settings.</p></div

Pairwise comparisons of absolute errors between each subject and the control group in the 3 directions.

<p>Mean differences are evaluated as Control-S. Significant differences are marked with an asterisk.</p

Absolute errors.

<p>(A) Box-plot of absolute errors for the two groups. (B) Mean absolute errors in the 3 directions for the two groups, where the gray squares represent data from the control group and black circles represent stroke patients. (C) Absolute errors for each patient and mean value of the control subjects.</p

Signed errors.

<p>(A) Box-plot of signed errors for the two groups. (B) Mean signed errors in the 3 directions for the two groups, where the gray squares represent data from the control group and black circles represent stroke patients. (C) Signed errors for each patient and mean value of the control subjects.</p