A Phenomenologically Sensitized Analysis of Images Depicting Stressed Embodiment in an Adolescent Male with Autism Spectrum Disorder (ASD)

Each person with Autism Spectrum Disorder (ASD) comes with unique characteristics (idiosyncratic) that give clues to the world they know (Connolly, 2008). It is through their body that they (a) know the world they are experiencing, (b) make meaning, and (c) express certain behaviours. I used Laban’s Movement Analysis (LMA) to practice an attuned and appreciative approach to describing and understanding the body movement in one severe manifestation of autism in an adolescent male. LMA observes human movement across many disciplines and can be applied in many contexts providing a body honoring discourse for description (Connolly, 2008). The framework examines movement in body, space, quality, and relation. Each theme provides a detailed description of the individual’s movement, thus, giving us a richer understanding of patterns and possible triggers to self-injurious behaviours (SIB). During the summer of August 2013, I participated in Brock University’s annual Autism Camp and worked with a 15 year old male named “Aaron” who manifests with low functioning autism. The purpose of my research project was to code and analyze a series of photos taken to help gain insight into movement patterns associated with stressed embodiment and self-injury in “Aaron”. As I understood more about these embodied expressions, I uncovered valuable information on how to read patterns and discover what triggers these events, thus providing strategies on how to help people do more refined observations and make meaning of the behaviour. Laban’s movement analysis provided a sensitized discourse appropriate to the embodied expressions depicted in the photos.

L-Fuzzy Structured Query Language

Lattice valued fuzziness is more general than crispness or fuzziness based on the unit interval. In this work, we present a query language for a lattice based fuzzy database. We define a Lattice Fuzzy Structured Query Language (LFSQL) taking its membership values from an arbitrary lattice L. LFSQL can handle, manage and represent crisp values, linear ordered membership degrees and also allows membership degrees from lattices with non-comparable values. This gives richer membership degrees, and hence makes LFSQL more flexible than FSQL or SQL. In order to handle vagueness or imprecise information, every entry into an L-fuzzy database is an L-fuzzy set instead of crisp values. All of this makes LFSQL an ideal query language to handle imprecise data where some factors are non-comparable. After defining the syntax of the language formally, we provide its semantics using L-fuzzy sets and relations. The semantics can be used in future work to investigate concepts such as functional dependencies. Last but not least, we present a parser for LFSQL implemented in Haskell.