Stroke survivors directly link stroke education with their ability to access appropriate treatments and reduce their risk of future strokes. However, with such a diverse population a universal mode of delivering education must be sought. This paper places the artist at the heart of educating patients and carers about their disease by developing a technical process of delivering 3D CT patient stroke data on the Virtual Reality (VR) platform Oculus Rift DK2. VR has already been proven as an efficacious rehabilitation tool for this population but its use in education has not yet been established. This work is being piloted in a new collaboration between the Art & Design Faculty at the University of New South Wales, Australia and the Stroke Rehabilitation Service at St Vincent's Hospital in Sydney. Importantly, this paper places the artist's at the center of the process -- from developing the concept, to prototyping and creating the final visual aesthetic. The different stages of the technical design process are also described in this paper. These provide an early framework for working with VR in a clinical context.

We propose a novel pervasive system to recognise human daily activities from a wearable device. The system is designed in a form of reading glasses, named `Smart Glasses', integrating a 3-axis accelerometer and a first-person view camera. Our aim is to classify user's activities of daily living (ADLs) based on both vision and head motion data. This ego-activity recognition system not only allows caretakers to track on a specific person (such as patient or elderly people), but also has the potential to remind/warn people with cognitive impairments of hazardous situations. We present the following contributions in this paper: a feature extraction method from accelerometer and video; a classification algorithm integrating both locomotive (body motions) and stationary activities (without or with small motions); a novel multi-scale dynamic graphical model structure for structured classification over time. We collect, train and validate our system on a large dataset containing 20 hours of ADLs data, including 12 daily activities under different environmental settings. Our method improves the classification performance (F-Score) of conventional approaches from 43.32%(video features) and 66.02%(acceleration features) by an average of 20-40% to 84.45%, with an overall accuracy of 90.04% in realistic ADLs.

This paper proposes a novel activity recognition approach from video data obtained with a wearable camera. The objective is to recognise the user's activities from a tiny front-facing camera embedded in his/her glasses. Our system allows carers to remotely access the current status of a specified person, which can be broadly applied to those living with disabilities including the elderly who require cognitive assistance or guidance for daily activities. We collected, trained and tested our system on videos collected from different environmental settings. Sequences of four basic activities (drinking, walking, going upstairs and downstairs) are tested and evaluated in challenging real-world scenarios. An optical flow procedure is used as our primary feature extraction method, from which we downsize, reformat and classify sequence of activities using k-Nearest Neighbour algorithm (k-NN), LogitBoost (on Decision Stumps) and Support Vector Machine (SVM). We suggest the optimal settings of these classifiers through cross-validations and achieve an accuracy of 54.2% to 71.9%. Further smoothing using Hidden Markov Model (HMM) improves the result to 68.5%-82.1%.