ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also conducted to obtain an impression of the usability and potential limitations

Leveling the Playing Field: Supporting Neurodiversity via Virtual Realities

Neurodiversity is a term that encapsulates the diverse expression of human neurology. By thinking in broad terms about neurological development, we can become focused on delivering a diverse set of design features to meet the needs of the human condition. In this work, we move toward developing virtual environments that support variations in sensory processing. If we understand that people have differences in sensory perception that result in their own unique sensory traits, many of which are clustered by diagnostic labels such as Autism Spectrum Disorder (ASD), Sensory Processing Disorder, Attention-Deficit/Hyperactivity Disorder, Rett syndrome, dyslexia, and so on, then we can leverage that knowledge to create new input modalities for accessible and assistive technologies. In an effort to translate differences in sensory perception into new variations of input modalities, we focus this work on ASD. ASD has been characterized by a complex sensory signature that can impact social, cognitive, and communication skills. By providing assistance for these diverse sensory perceptual abilities, we create an opportunity to improve the interactions people have with technology and the world. In this paper, we describe, through a variety of examples, the ways to address sensory differences to support neurologically diverse individuals by leveraging advances in virtual reality

Paper Prototyping Comfortable VR Play for Diverse Sensory Needs

We co-designed paper prototype dashboards for virtual environments for three children with diverse sensory needs. Our goal was to determine individual interaction styles in order to enable comfortable and inclusive play. As a first step towards an inclusive virtual world, we began with designing for three sensory-diverse children who have labels of neurotypical, ADHD, and autism respectively. We focused on their leisure interests and their individual sensory profiles. We present the results of co-design with family members and paper prototyping sessions conducted by family members with the children. The results contribute preliminary empirical findings for accommodating different levels of engagement and empowering users to adjust environmental thresholds through interaction design

vrSensory: Designing Inclusive Virtual Games with Neurodiverse Children

We explore virtual environments and accompanying interaction styles to enable inclusive play. In designing games for three neurodiverse children, we explore how designing for sensory diversity can be understood through a formal game design framework. Our process reveals that by using sensory processing needs as requirements we can make sensory and social accessible play spaces. We contribute empirical findings for accommodating sensory differences for neurodiverse children in a way that supports inclusive play. Specifically, we detail the sensory driven design choices that not only support the enjoyability of the leisure activities, but that also support the social inclusion of sensory-diverse participants. The participants displayed behaviors in the multi-user version consistent with their behaviors in the single user version with the addition of social behavior. We tie these techniques to game design mechanics to iterate on our efforts to support inclusive game development. Preliminary results are discussed