Despite the well-documented importance of braille for people who are blind or visually impaired, few studies explore technology for facilitating braille literacy education. Evaluations of the impac...

Background: Robotic devices have been used to quantify function, identify impairment, and rehabilitate motor function extensively in adults, but less-so in younger populations. The ability to perform motor actions improves as children grow. It is important to quantify this rate of change of the neurotypical population before attempting to identify impairment and target rehabilitation techniques. Objectives: For a population of typically developing children, this systematic review identifies and analyzes tools and techniques used with robotic devices to quantify upper-limb motor function. Since most of the papers also used robotic devices to compare function of neurotypical to pathological populations, a secondary objective was introduced to relate clinical outcome measures to identified robotic tools and techniques. Methods: Five databases were searched between February 2019 and August 2020, and 226 articles were found, 19 of which are included in the review. Results: Robotic devices, tasks, outcome measures, and clinical assessments were not consistent among studies from different settings but were consistent within laboratory groups. Fifteen of the 19 articles evaluated both typically developing and pathological populations. Conclusion: To optimize universally comparable outcomes in future work, it is recommended that a standard set of tasks and measures is used to assess upper-limb motor function. Standardized tasks and measures will facilitate effective rehabilitation.

Developmental coordination disorder (DCD) is a chronic neurological impairment that affects motor coordination in 5%-8% of school-aged children. Diagnostic criteria for DCD are vague and focus on ruling out other conditions, leading to a lack of standardization in DCD diagnosis. This lack of standardization makes identification of the impairment, and therefore development of interventional tools, difficult. This study was conducted to investigate a set of potential quantitative models to identify DCD based on a robotic assessment, and to compare the performance of participants with DCD to normative models of typically developed motor function. An exploratory factor analysis achieved strong differentiation between control participants and participants with DCD. This demonstrates the feasibility of a robotic assessment as a diagnostic tool for DCD.

Clinical tasks are often used to differentiate the motor performance of individuals who have impaired function. However, these are not as accurate and repeatable as robotic tasks. Additionally, motor development occurs rapidly at early ages and slows as they reach adulthood, resulting in a non-linear model of performance. There is also evidence that variability in performance changes as children and youth age. Accurate normative models of performance are necessary to identify deficiencies in motor performance and to track the efficacy of therapies. This work aimed to create normative models of motor development based on robotic assessments in typically developing children and youth. Two hundred and eighty-eight participants who are typically developing (ages 5–18) completed a robotic point-to-point reaching task and an object-hitting task using the Kinarm Exoskeleton. Exponential or quadratic curves were fit to performance parameters generated by Kinarm to model typical performance. These models included a linear term to account for changing variabilities with age. Most performance parameters showed improvement with age, and none showed deterioration. Some parameters showed large changes in variability in performance with age, with up to a 74% decrease in the range of typical performance. Reduced variability occurs with age, indicating the need to account for differences in variability when developing models of typical motor performance in children and youth. The models that are used to identify deficits in motor performance should account for changing variability in data and changing repeatability with age to increase the accuracy of identification of deficits.

Performance modeling using robotic tasks can be used to identify motor control impairments. Motor development in children and youth occurs non-linearly, with higher accuracy and decreased variability in performance as they reach adulthood. However, past models have not accounted for these differences. The objective of this research was to create an algorithm that accounts for variability in sample size and age, modifying the curvature and variability of the data to test the accuracy and repeatability. While increased sample size improves the model, data collection is often limited by funding or population interest in participation. The simulations provide models of variability relative to sample size of the typically developing population from 5 to 18 years. The algorithm was evaluated with a sample of two-hundred and eighty-eight children. Using these models based on varying sample size, one can have greater confidence when identifying motor deficits from outlying data. Future researchers can use the model accuracy and repeatability information from this work to assess confidence in their own models based on the dataset size, data variability, and data curvature.