Taken for granted: Material relations between disability and codes/guidelines

This paper provides a critical examination of the taken for granted nature of the codes/guidelines used towards the creation of designed spaces, their social relations with designers, and their agency in designing for people with disabilities. We conducted case studies at three national museums in Canada where we began by questioning societal representations of disability within and through material culture through the potential of actor-network theory where non-human actors have considerable agency. Specifically, our exploration looks into how representations of disability for designing, are interpreted through mediums such as codes, standards and guidelines. We accomplish this through: deep analyses of the museums’ built environments (outdoors and indoors); interviewed curators, architects and designers involved in the creation of the spaces/displays; completed dialoguing while in motion interviews with people who have disabilities within the spaces; and analyzed available documents relating to the creation of the museums. Through analyses of our rich data set involving the mapping of codes/guidelines in their ‘representation’ of disability and their contributions in ‘fixing’ disability, this paper takes an alternative approach to designing for/with disability by aiming to question societal representations of disability within and through material culture.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.