Brook Galna

Wearable technology allows an in-depth analysis of gait behaviour in free-living environments. This investigation aimed to use Alzheimer's disease as an example to apply the time series analysis technique of Statistical Parametric Mapping (SPM) to create daily gait profiles and test if they differed from cognitively intact controls. A framework of macro (habitual walking behaviours) and micro characteristics (spatiotemporal gait variables) characteristics were calculated on an hourly basis. SPM showed that select micro gait characteristics differed from controls at specific hours of the day. Therefore, the application of SPM may provide a more in-depth reflection of activity and gait time-dependent fluctuations than commonly used whole day values. Considering macro and micro gait hour-by- hour may have applications towards disease management, personalized care, monitoring medication and targeted interventions for people with a range of neurodegenerative diseases.

Parkinson's is a complex and multifaceted condition with a myriad of symptoms, thus, designing for and with this user group requires careful consideration. We reflect upon two studies, employing different design methodologies, relating to the design of rehabilitative exergames in Parkinson's. The first explored the concept of designing 'for' People with Parkinson's (PwP) and focused on specifications outlined by clinical stakeholders. The second used a designing 'with' approach and modified a pre-established participatory design method for use with PwP. We call attention to the importance of carrying out design work with PwP and contribute; an empathic understanding of living with Parkinson's, a set of recommendations for how to design with PwP and a set of wider considerations for developing rehabilitative exergames for PwP.

We validate a novel algorithm to detect saccades from raw data obtained during walking from a mobile infra-red eye-tracking device. The algorithm was based on a velocity threshold detection method, which excluded artefacts such as blinks and flickers using specific criteria. Mobile infra-red eye-tracking was performed with a group of healthy older adults (n=5) and Parkinson's disease (n=5) subjects. Saccades determined from raw eye tracker data obtained during walking using the algorithm were compared to a ground truth dataset defined as frame-by-frame visual inspection of raw eye-tracking videos. 100 trials from 10 subjects were analyzed and compared. The algorithm was highly reliable when compared to the ground truth (ICC(2,1) = 0.94), with an overall correct saccade detection percentage of 85%. This provides a simple yet robust algorithm for the analysis of mobile eye-tracking data.