Community-based postural control assessment in autistic individuals indicates a similar but delayed trajectory compared to neurotypical individuals

Balance and motor skills differences between children and teenagers with autism spectrum disorder and neurotypically developing

This study examined the differences between children and adolescents with autism spectrum disorder (ASD) and neurotypically developing (NTD) in terms of balance, postural control, and motor skills. It also examined which motor skills are most affected and whether scores on different assessment tests in ASD children are correlated. A cross-sectional observational study with two research groups was conducted. Timed up and go test (TUG), short form of Bruininks-Oseretsky test of Motor Proficiency version 2 (SFBOT-2), and pediatric balance scale (PBS) were used. A total of 100 participants 50 with ASD and 50 with NTD engaged in the research. Statistically significant differences were obtained between control group and ASD group in TUG test and in SFBOT-2 standard score and total score (p-value = <0.01). A statistically significant difference (p-value = <0.01) was seen between ASD group's and control group's PBS scores. Poor correlation was noted between TUG and SFBOT-2, as well as between PBS and TUG. A moderate correlation was also found between SFBOT-2 and PBS. Children with ASD present difficulties in motor skills and in static and dynamic balance compared to children with NTD. Differences were observed in the motor skills of strength followed by manual dexterity, running speed and agility, fine motor precision, fine motor integration, and balance. The PBS item that showed the greatest difference between the ASD group and control group was maintaining monopodial support with hands on hips. Finally, poor to moderate correlations were obtained between the different tests with statistically significant differences.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Intervention Based on Psychomotor Rehabilitation in Children with Autism Spectrum Disorder ASD: Effect on Postural Control and Sensory Integration

Postural stability and control are essential motor skills for successfully performing various activities of daily living. However, children with autism spectrum disorder (ASD) exhibit significant sensorimotor impairments. The aim of this study was to investigate the efficacy of psychomotricity training on postural control (PC) of children with ASD. We recruited thirty children (age = 8.01 ± 1.2; weight = 31.66 ± 8.1 kg; height = 129.7 ± 10.8 cm) diagnosed with ASD (intellectual quotient > 50) to participate in this study. They were divided into two groups: the experimental group (n = 16) and control group (n = 14). Children in the experimental group were trained with psychomotor activities two times a week for nine weeks. Statistic postural balance was assessed before and after intervention and on different vision conditions. The results showed that the psychomotor training significantly improved PC in standing position under different conditions when compared to the control group, in all parameters (CoPA; CoPLX; CoPLy) (p < 0.01). Our preliminary findings suggest the usefulness of the psychomotor training in children with ASD on static PC.

Motor differences in autism during a human-robot imitative gesturing task

BACKGROUND

Difficulty with imitative gesturing is frequently observed as a clinical feature of autism. Current practices for assessment of imitative gesturing ability-behavioral observation and parent report-do not allow precise measurement of specific components of imitative gesturing performance, instead relying on subjective judgments. Advances in technology allow researchers to objectively quantify the nature of these movement differences, and to use less socially stressful interaction partners (e.g., robots). In this study, we aimed to quantify differences in imitative gesturing between autistic and neurotypical development during human-robot interaction.

METHODS

Thirty-five autistic (n = 19) and neurotypical (n = 16) participants imitated social gestures of an interactive robot (e.g., wave). The movements of the participants and the robot were recorded using an infrared motion-capture system with reflective markers on corresponding head and body locations. We used dynamic time warping to quantify the degree to which the participant's and robot's movement were aligned across the movement cycle and work contribution to determine how each joint angle was producing the movements.

FINDINGS

Results revealed differences between autistic and neurotypical participants in imitative accuracy and work contribution, primarily in the movements requiring unilateral extension of the arm. Autistic individuals imitated the robot less accurately and used less work at the shoulder compared to neurotypical individuals.

INTERPRETATION

These findings indicate differences in autistic participants' ability to imitate an interactive robot. These findings build on our understanding of the underlying motor control and sensorimotor integration mechanisms that support imitative gesturing in autism which may aid in identifying appropriate intervention targets.