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The objective of this work is to develop an understanding of the relationship between mobility metrics obtained outside of the clinic or laboratory and the context of the external environment. Ten subjects walked with an inertial sensor on each shank and a wearable camera around their neck. They were taken on a thirty minute walk in which they mobilized over the following conditions; normal path, busy hallway, rough ground, blind folded and on a hill. Stride time, stride time variability, stance time and peak shank rotation rate during swing were calculated using previously published algorithms. Stride time was significantly different between several of the conditions. Technological advances mean that gait variables can now be captured as patients go about their daily lives. The results of this study show that the external environment has a significant impact on the quality of gait metrics. Thus, context of external walking environment is an important consideration when analyzing ambulatory gait metrics from the unsupervised home and community setting.

Typically BCI (Brain Computer Interfaces) are found in rehabilitative or restorative applications, often allowing users a medium of communication that is otherwise unavailable through conventional means. Recently, however, there is growing interest in using BCI to assist users in searching for images. A class of neural signals often leveraged in common BCI paradigms are ERPs (Event Related Potentials), which are present in the EEG (Electroencephalograph) signals from users in response to various sensory events. One such ERP is the P300, and is typically elicited in an oddball experiment where a subject's attention is orientated towards a deviant stimulus among a stream of presented images. It has been shown that these types of neural responses can be used to drive an image search or labeling task, where we can rank images by examining the presence of such ERP signals in response to the display of images. To date, systems like these have been demonstrated when presenting sequences of images containing targets at up to 10 Hz, however, the target images in these tasks do not necessitate any kind of eye movement for their detection because the targets in the images are quite salient. In this paper we analyse the presence of discriminating EEG signals when they are offset to the time of eye fixations in a visual search task where detection of target images does require eye fixations.