Reduction of Assessment Time for Stroke-Related Impairments Using Robotic Evaluation

Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person's abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately 76.6% in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a 5% improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen's Kappa ( κlw = 0.733 ). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at 0.95 , we attain an impressive estimation accuracy of 88% . Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.

Robotics-Based Characterization of Sensorimotor Integration in Parkinson's Disease and the Effect of Medication

Integration of multi-modal sensory inputs and modulation of motor outputs based on perceptual estimates is called Sensorimotor Integration (SMI). Optimal functioning of SMI is essential for perceiving the environment, modulating the motor outputs, and learning or modifying motor skills to suit the demands of the environment. Growing evidence suggests that patients diagnosed with Parkinson's Disease (PD) may suffer from an impairment in SMI that contributes to perceptual deficits, leading to motor abnormalities. However, the exact nature of the SMI impairment is still unclear. This study uses a robot-assisted assessment tool to quantitatively characterize SMI impairments in PD patients and how they affect voluntary movements. A set of assessment tasks was developed using a robotic manipulandum equipped with a virtual-reality system. The sensory conditions of the virtual environment were varied to facilitate the assessment of SMI. A hundred PD patients (before and after medication) and forty-three control subjects completed the tasks under varying sensory conditions. The kinematic measures obtained from the robotic device were used to evaluate SMI. The findings reveal that across all sensory conditions, PD patients had 36% higher endpoint error, 38% higher direction error in reaching tasks, and 43% higher number of violations in tracing tasks than control subjects due to impairment in integrating sensory inputs. However, they still retained motor learning ability and the ability to modulate motor outputs. The medication worsened the SMI deficits as PD patients, after medication, performed worse than before medication when encountering dynamic sensory environments and exhibited impaired motor learning ability.

Home-based upper limb stroke rehabilitation mechatronics: challenges and opportunities

Interest in home-based stroke rehabilitation mechatronics, which includes both robots and sensor mechanisms, has increased over the past 12 years. The COVID-19 pandemic has exacerbated the existing lack of access to rehabilitation for stroke survivors post-discharge. Home-based stroke rehabilitation devices could improve access to rehabilitation for stroke survivors, but the home environment presents unique challenges compared to clinics. The present study undertakes a scoping review of designs for at-home upper limb stroke rehabilitation mechatronic devices to identify important design principles and areas for improvement. Online databases were used to identify papers published 2010-2021 describing novel rehabilitation device designs, from which 59 publications were selected describing 38 unique designs. The devices were categorized and listed according to their target anatomy, possible therapy tasks, structure, and features. Twenty-two devices targeted proximal (shoulder and elbow) anatomy, 13 targeted distal (wrist and hand) anatomy, and three targeted the whole arm and hand. Devices with a greater number of actuators in the design were more expensive, with a small number of devices using a mix of actuated and unactuated degrees of freedom to target more complex anatomy while reducing the cost. Twenty-six of the device designs did not specify their target users' function or impairment, nor did they specify a target therapy activity, task, or exercise. Twenty-three of the devices were capable of reaching tasks, 6 of which included grasping capabilities. Compliant structures were the most common approach of including safety features in the design. Only three devices were designed to detect compensation, or undesirable posture, during therapy activities. Six of the 38 device designs mention consulting stakeholders during the design process, only two of which consulted patients specifically. Without stakeholder involvement, these designs risk being disconnected from user needs and rehabilitation best practices. Devices that combine actuated and unactuated degrees of freedom allow a greater variety and complexity of tasks while not significantly increasing their cost. Future home-based upper limb stroke rehabilitation mechatronic designs should provide information on patient posture during task execution, design with specific patient capabilities and needs in mind, and clearly link the features of the design to users' needs.

Can we trust computers to assess the cognition of stroke patients? A systematic review

Purpose

To summarize the classification of computerized cognitive assessment (CCA) tools for assessing stroke patients, to clarify their benefits and limitations, and to reveal strategies for future studies on CCA tools.

Methods

A literature review was performed using PubMed, Embase, Scopus, JAMA Network, Cochrane Library and PsycINFO databases from January 1st, 2010, to August 1st, 2022. Two authors independently screened the literature following the same criteria, evaluated the study quality, and collected data from the articles.

Results

A total of 8,697 papers were acquired from the six databases. A total of 74 potentially eligible articles were selected for review. Of these, 29 articles were not relevant to this research, 3 were reviews, 2 were not written in English, and 1 was on an ongoing trial. By screening the references of the reviews, 3 additional articles were included in this study. Thus, a total of 42 articles met the criteria for the review. In terms of the CCA tools analyzed in these studies, they included five types: virtual reality (VR)-based, robot-based, telephone-based, smartphone-based, and computer-based cognitive assessments. Patients' stages of the disease ranged from the subacute phase and rehabilitation phase to the community phase. A total of 27 studies supported the effectiveness of CCA tools, while 22 out of 42 articles mentioned their benefits and 32 revealed areas for future improvement of CCA tools.

Conclusions

Although the use of CCA tools for assessing the cognition of post-stroke patients is becoming popular, there are still some limitations and challenges of using such tools in stroke survivors. More evidence is thus needed to verify the value and specific role of these tools in assessing the cognitive impairment of stroke patients.

Assessing visually guided reaching in people with multiple sclerosis with and without self-reported upper limb impairment

The ability to accurately complete goal-directed actions, such as reaching for a glass of water, requires coordination between sensory, cognitive and motor systems. When these systems are impaired, like in people with multiple sclerosis (PwMS), deficits in movement arise. To date, the characterization of upper limb performance in PwMS has typically been limited to results attained from self-reported questionnaires or clinical tools. Our aim was to characterize visually guided reaching performance in PwMS. Thirty-six participants (12 PwMS who reported upper limb impairment (MS-R), 12 PwMS who reported not experiencing upper limb impairment (MS-NR), and 12 age- and sex-matched control participants without MS (CTL)) reached to 8 targets in a virtual environment while seeing a visual representation of their hand in the form of a cursor on the screen. Reaches were completed with both the dominant and non-dominant hands. All participants were able to complete the visually guided reaching task, such that their hand landed on the target. However, PwMS showed noticeably more atypical reaching profiles when compared to control participants. In accordance with these observations, analyses of reaching performance revealed that the MS-R group was more variable with respect to the time it took to initiate and complete their movements compared to the CTL group. While performance of the MS-NR group did not differ significantly from either the CTL or MS-R groups, individuals in the MS-NR group were less consistent in their performance compared to the CTL group. Together these findings suggest that PwMS with and without self-reported upper limb impairment have deficits in the planning and/or control of their movements. We further argue that deficits observed during movement in PwMS who report upper limb impairment may arise due to participants compensating for impaired movement planning processes.

The Step Response in Isometric Grip Force Tracking: A Model to Characterize Aging- and Stroke-Induced Changes

This paper aimed to construct a model to represent dynamic motor behavior to quantitatively investigate aging- and stroke-induced changes and, thus, to explore the underlying mechanisms of grip control. Grip force tracking tasks were conducted by stroke patients, age-matched healthy controls, and healthy young adults at 25%, 50%, and 75% maximum voluntary contractions (MVC), respectively. Sensorimotor control of the tracking task was modeled as the step response of a second-order system. The results revealed that aging had no significant effect on the parameters of the model, whereas significant differences were found between the age-matched control and stroke groups. Target force level significantly affected the damping ratio and natural frequency in the young group, and significantly affected the damping ratio in the stroke group. Significant correlations were found between the wolf motor function test score and damping ratio, natural frequency, and settling time at 25% MVC. The model could describe the stroke-induced oscillation and slow response in dynamic grip force control and has the potential to be a quantitative evaluation of motor disabilities in clinic.

The feasibility of using robotic technology to quantify sensory, motor, and cognitive impairments associated with ALS

OBJECTIVE

We used the KINARM robot to quantify impairments in cognitive and upper-limb sensorimotor performance in a cohort of people with amyotrophic lateral sclerosis (ALS). We sought to study the feasibility of using this technology for ALS research, to quantify patterns of impairments in individuals living with ALS, and elucidate correlations between robotic and traditional clinical behavioral measures.

METHODS

Participants completed robot-based behavioral tasks testing sensorimotor, cognitive, and proprioceptive performance. Performance on robotic tasks was normalized to a large healthy control cohort (no neurological impairments), adjusted for age. Task impairment was defined as performance outside the 95% range of controls. Traditional clinical tests included: Frontal Assessment Battery (FAB), ALS Functional Rating Scale-Revised (ALSFRS-R), and Montreal Cognitive Assessment (MoCA).

RESULTS

Seventeen people with ALS were assessed. Two participants reported pain or discomfort from the robot's seat and 2 others reported discomfort from arm position during the assessment (both rectified and did not affect exam completion). Participants were able to perform the majority of the robotic tasks, although 9 participants were unable to complete 1 or more tasks. Between 20 and 69% of participants displayed sensorimotor impairments; 19 and 69% displayed cognitive task impairments; 25% displayed proprioceptive impairments. MoCA was impaired in 9/17 participants; 10/17 had impaired performance on FAB. MoCA and FAB correlated well with robot-based measures of cognition.

CONCLUSION

Use of robotic assessment is generally feasible for people with ALS. Individuals with ALS have sensorimotor impairments as expected, and some demonstrate substantial cognitive impairments.