Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

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Surface electromyogram (sEMG) provides a promising means to develop a non-invasive prosthesis control system. In the context of transradial amputees, it allows a limited but functionally useful return of hand function that can significantly improve patients' quality of life. In order to predict users' motion intention, the ability to process multichannel sEMG signals generated by muscle is required. We propose an attention-based Bidirectional Convolutional Gated Recurrent Unit (Bi-CGRU) deep neural network to analyse sEMG signals. The two key novel aspects of our work include: firstly, novel use of a bi-directional sequential GRU to focus on the inter-channel relationship between both the prior time steps and the posterior signals. This enhances the intra-channel features extracted by an initial one-dimensional CNN. Secondly, an attention component is employed at each GRU layer. This mechanism learns different intra-attention weights, enabling focus on vital parts and corresponding dependencies of the signal. This increases robustness to feature noise to further improve accuracy. The attention-based Bi-CGRU is evaluated on the Ninapro benchmark dataset of sEMG hand gestures. The electromyogram signals of 17 hand gestures from 10 subjects from the database are tested. The average accuracy achieved 88.73%, outperforming the state-of-the-art approaches on the same database. This demonstrates that the proposed attention based Bi-CGRU model provides a promising bio-control solution for robotic prostheses.

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