A protocol to examine vision and gait in Parkinson’s disease: impact of cognition and response to visual cues

Background Cognitive and visual impairments are common in Parkinson's disease (PD) and contribute to gait deficit and falls. To date, cognition and vision in gait in PD have been assessed separately. Impact of both functions (which we term 'visuo-cognition') on gait however is likely interactive and can be tested using visual sampling (specifically saccadic eye movements) to provide an online behavioural measure of performance. Although experiments using static paradigms show saccadic impairment in PD, few studies have quantified visual sampling during dynamic motor tasks such as gait. This article describes a protocol developed for testing visuo-cognition during gait in order to examine the: 1) independent roles of cognition and vision in gait in PD, 2) interaction between both functions, and 3) role of visuo-cognition in gait in PD. Methods Two groups of older adults (≥50 years old) were recruited; non-demented people with PD (n=60) and age-matched controls (n=40). Participants attended one session and a sub-group (n=25) attended two further sessions in order to establish mobile eye-tracker reliability. Participants walked in a gait laboratory under different attentional (single and dual task), environmental (walk straight, through a door and turning), and cueing (no visual cues and visual cues) conditions. Visual sampling was recorded using synchronised mobile eye-tracker and electrooculography systems, and gait was measured using 3D motion analysis. Discussion This exploratory study examined visuo-cognitive processes and their impact on gait in PD. Improved understanding of the influence of cognitive and visual functions on visual sampling during gait and gait in PD will assist in development of interventions to improve gait and reduce falls risk. This study will also help establish robust mobile eye-tracking methods in older adults and people with PD

A protocol to examine vision and gait in Parkinson’s disease: impact of cognition and response to visual cues

Background Cognitive and visual impairments are common in Parkinson's disease (PD) and contribute to gait deficit and falls. To date, cognition and vision in gait in PD have been assessed separately. Impact of both functions (which we term 'visuo-cognition') on gait however is likely interactive and can be tested using visual sampling (specifically saccadic eye movements) to provide an online behavioural measure of performance. Although experiments using static paradigms show saccadic impairment in PD, few studies have quantified visual sampling during dynamic motor tasks such as gait. This article describes a protocol developed for testing visuo-cognition during gait in order to examine the: 1) independent roles of cognition and vision in gait in PD, 2) interaction between both functions, and 3) role of visuo-cognition in gait in PD. Methods Two groups of older adults (≥50 years old) were recruited; non-demented people with PD (n=60) and age-matched controls (n=40). Participants attended one session and a sub-group (n=25) attended two further sessions in order to establish mobile eye-tracker reliability. Participants walked in a gait laboratory under different attentional (single and dual task), environmental (walk straight, through a door and turning), and cueing (no visual cues and visual cues) conditions. Visual sampling was recorded using synchronised mobile eye-tracker and electrooculography systems, and gait was measured using 3D motion analysis. Discussion This exploratory study examined visuo-cognitive processes and their impact on gait in PD. Improved understanding of the influence of cognitive and visual functions on visual sampling during gait and gait in PD will assist in development of interventions to improve gait and reduce falls risk. This study will also help establish robust mobile eye-tracking methods in older adults and people with PD

Eye-tracking assistive technologies for individuals with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research

Analysis of the Usefulness of Mobile Eyetracker for the Recognition of Physical Activities

We investigate the usefulness of information from a wearable eyetracker to detect physical activities during assembly and construction tasks. Large physical activities, like carrying heavy items and walking, are analysed alongside more precise, hand-tool activities like using a screwdriver. Statistical analysis of eye based features like fixation length and frequency of fixations show significant correlations for precise activities. Using this finding, we selected 10, calibration-free eye features to train a classifier for recognising up to 6 different activities. Frame-byframe and event based results are presented using data from an 8-person dataset containing over 600 activity events. We also evaluate the recognition performance when gaze features are combined with data from wearable accelerometers and microphones. Our initial results show a duration-weighted event precision and recall of up to 0.69 & 0.84 for independently trained recognition on precise activities using gaze. This indicates that gaze is suitable for spotting subtle precise activities and can be a useful source for more sophisticated classifier fusion

IEEE Access Special Section Editorial: Advanced Signal Processing Methods in Medical Imaging

Medical Imaging is a technique to create visual representations of the interior of the body, with the aim of making accurate diagnoses and optimized treatments. Many medical imaging techniques are widely used to produce images, such as computer tomography (CT), ultrasound (US), positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI)/functional MRI (fMRI)

Automatic emotion perception using eye movement information for E-Healthcare systems.

Facing the adolescents and detecting their emotional state is vital for promoting rehabilitation therapy within an E-Healthcare system. Focusing on a novel approach for a sensor-based E-Healthcare system, we propose an eye movement information-based emotion perception algorithm by collecting and analyzing electrooculography (EOG) signals and eye movement video synchronously. Specifically, we extract the time-frequency eye movement features by firstly applying the short-time Fourier transform (STFT) to raw multi-channel EOG signals. Subsequently, in order to integrate time domain eye movement features (i.e., saccade duration, fixation duration, and pupil diameter), we investigate two feature fusion strategies: feature level fusion (FLF) and decision level fusion (DLF). Recognition experiments have been also performed according to three emotional states: positive, neutral, and negative. The average accuracies are 88.64% (the FLF method) and 88.35% (the DLF with maximal rule method), respectively. Experimental results reveal that eye movement information can effectively reflect the emotional state of the adolescences, which provides a promising tool to improve the performance of the E-Healthcare system.Anhui Provincial Natural Science Research Project of Colleges and Universities Fund under Grant KJ2018A0008, Open Fund for Discipline Construction under Grant Institute of Physical Science and Information Technology in Anhui University, and National Natural Science Fund of China under Grant 61401002

Digital Oculomotor Biomarkers in Dementia

Dementia is an umbrella term that covers a number of neurodegenerative syndromes featuring gradual disturbance of various cognitive functions that are severe enough to interfere with tasks of daily life. The diagnosis of dementia occurs frequently when pathological changes have been developing for years, symptoms of cognitive impairment are evident and the quality of life of the patients has already been deteriorated significantly. Although brain imaging and fluid biomarkers allow the monitoring of disease progression in vivo, they are expensive, invasive and not necessarily diagnostic in isolation. Recent studies suggest that eye-tracking technology is an innovative tool that holds promise for accelerating early detection of the disease, as well as, supporting the development of strategies that minimise impairment during every day activities. However, the optimal methods for quantitative evaluation of oculomotor behaviour during complex and naturalistic tasks in dementia have yet to be determined. This thesis investigates the development of computational tools and techniques to analyse eye movements of dementia patients and healthy controls under naturalistic and less constrained scenarios to identify novel digital oculomotor biomarkers. Three key contributions are made. First, the evaluation of the role of environment during navigation in patients with typical Alzheimer disease and Posterior Cortical Atrophy compared to a control group using a combination of eye movement and egocentric video analysis. Secondly, the development of a novel method of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test to detect oculomotor biomarkers of dementia-related cognitive dysfunction. Third, the application of unsupervised anomaly detection techniques for visualisation of oculomotor anomalies during various cognitive tasks. The work presented in this thesis furthers our understanding of dementia-related oculomotor dysfunction and gives future research direction for the development of computerised cognitive tests and ecological interventions

Algorithm for automatic analysis of electro-oculographic data

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