Feasibility of Automated Mobility Assessment of Older Adults via an Instrumented Cane.

This study explored the feasibility of automated characterization of functional mobility via an Instrumented Cane System (ICS) within an older adult sample of cane users. An off-the-shelf offset cane was instrumented with inertial, force, and ultrasound sensors for noninvasive data collection. Eighteen patients from a neurological out-patient rehabilitation clinic and nine independently mobile controls participated in standard clinical evaluations of mobility using the ICS while under the care of an attending physical therapist. Feasibility of the ICS was gauged through two studies. The first demonstrated the capability of the ICS to reliably collect meaningful usage metrics, and the second provided preliminary support for the discriminability of high and low falls risk from system-reported metrics. Specifically, the cane significantly differentiated patients and controls (p < 0.05), and a measure of the variation in rotational velocity was associated with total scores on the Functional Gait Assessment (partial r = 0.61, p < 0.01). These findings may ultimately serve to complement and even extend current clinical assessment practices.

A new measure of child vocal reciprocity in children with autism spectrum disorder.

Children's vocal development occurs in the context of reciprocal exchanges with a communication partner who models "speechlike" productions. We propose a new measure of child vocal reciprocity, which we define as the degree to which an adult vocal response increases the probability of an immediately following child vocal response. Vocal reciprocity is likely to be associated with the speechlikeness of vocal communication in young children with autism spectrum disorder (ASD). Two studies were conducted to test the utility of the new measure. The first used simulated vocal samples with randomly sequenced child and adult vocalizations to test the accuracy of the proposed index of child vocal reciprocity. The second was an empirical study of 21 children with ASD who were preverbal or in the early stages of language development. Daylong vocal samples collected in the natural environment were computer analyzed to derive the proposed index of child vocal reciprocity, which was highly stable when derived from two daylong vocal samples and was associated with speechlikeness of vocal communication. This association was significant even when controlling for chance probability of child vocalizations to adult vocal responses, probability of adult vocalizations, or probability of child vocalizations. A valid measure of children's vocal reciprocity might eventually improve our ability to predict which children are on track to develop useful speech and/or are most likely to respond to language intervention. A link to a free, publicly-available software program to derive the new measure of child vocal reciprocity is provided. Autism Res 2018, 11: 903-915. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Children and adults often engage in back-and-forth vocal exchanges. The extent to which they do so is believed to support children's early speech and language development. Two studies tested a new measure of child vocal reciprocity using computer-generated and real-life vocal samples of young children with autism collected in natural settings. The results provide initial evidence of accuracy, test-retest reliability, and validity of the new measure of child vocal reciprocity. A sound measure of children's vocal reciprocity might improve our ability to predict which children are on track to develop useful speech and/or are most likely to respond to language intervention. A free, publicly-available software program and manuals are provided.

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention.

OBJECTIVE: To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. METHODS: Twenty adolescents with ASD participated in a six-session virtual reality driving simulator-based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist's rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins () were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. RESULTS: The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). CONCLUSION: Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. SIGNIFICANCE: The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.

A Step towards EEG-based brain computer interface for autism intervention.

Autism Spectrum Disorder (ASD) is a prevalent and costly neurodevelopmental disorder. Individuals with ASD often have deficits in social communication skills as well as adaptive behavior skills related to daily activities. We have recently designed a novel virtual reality (VR) based driving simulator for driving skill training for individuals with ASD. In this paper, we explored the feasibility of detecting engagement level, emotional states, and mental workload during VR-based driving using EEG as a first step towards a potential EEG-based Brain Computer Interface (BCI) for assisting autism intervention. We used spectral features of EEG signals from a 14-channel EEG neuroheadset, together with therapist ratings of behavioral engagement, enjoyment, frustration, boredom, and difficulty to train a group of classification models. Seven classification methods were applied and compared including Bayes network, naïve Bayes, Support Vector Machine (SVM), multilayer perceptron, K-nearest neighbors (KNN), random forest, and J48. The classification results were promising, with over 80% accuracy in classifying engagement and mental workload, and over 75% accuracy in classifying emotional states. Such results may lead to an adaptive closed-loop VR-based skill training system for use in autism intervention.