دانلود رایگان مقاله انگلیسی یک رویکرد اینترنت اشیا برای خواندن احساسات کودکان مبتلا به اختلال طیف اوتیسم - وایلی 2017

20.1 Introduction

Following the discussion on the contributions of Internet of Things (IoT) in health science and management in previous chapters, this chapter describes a prototype of IoT system designed specifically to help children with autism spectrum disorder (ASD) who are notoriously known to lack some social skills. Specifically, the application is designed to help both users with or without ASD to recognize their emotion in order to facilitate social interaction. Since the effectiveness of the prototype has not been clinically tested, readers are advised to consider it as another future IoT application in health and learning management. In this chapter, we describe the design and development of such an IoT prototype.

During a social interaction, it is important for all participants to interpret correctly the emotional nature of the message being communicated in order to avoid misunderstanding. Prior researches in the past decades have pointed out the lack of emotion recognition skills and social interaction skills among children and adults with ASD and the possible causal link between the former and the latter (Harms et al., 2010, Baron-Cohen et al., 1993). Since all participants in a social interaction should be held accountable for correctly interpreting other’s emotional state, the ability of neurotypical (NT) individuals to recognize the emotion of children with ASD should also be evaluated. A small number of recent works began to study these issues by empirically evaluating the emotion expressiveness of individuals with ASD (Brewer et al., 2016; Macdonald et al., 1989; Faso et al., 2015; Stagg et al., 2014). However, the research on the ability of NT individuals in “reading”the emotions of those with ASD is still rare.

20.6 Conclusions

In this chapter, the integration of a naturalistic multisensory IoT play environment has been presented; such an integration aims at capturing emotion-related behavioral, physiological, and ambient environment data for emotion recognition. The long-term goal of such a platform is to help NT individuals “read” the emotions of children with ASD. By adopting IoT, it may reduce direct intervention and interruption during children’s playing moment; hence, children could enjoy their activities more naturally. The chapter presented the system design and offered some early insights derived from the conducted testing sessions on the potential of such environment.

At present, some sensors (including the facial data captured by the Kinect sensor) are being tested separately. Achieving data fusion (i.e., integrating data captured from the different sensors) for the purposes of generating meaningful emotional label is one of our future challenges. Meanwhile, we have also used and tested the Emotion API (part of the Microsoft Cognitive Services4 ) to label user’s emotions: Although the application has been successfully experimented on NT individuals, how or whether it can be applied to children with ASD is unclear. As we have already mentioned, inferring the emotion of those with ASD is very challenging (Tang, 2016, Tang et al., 2017a, 2017b); therefore, it could be very useful if we could have a training data set obtained from children with ASD that includes their indicative emotional labels and behavioral patterns.