Using GPS Tracking to Investigate Outdoor Navigation Patterns in Patients With Alzheimer Disease: Cross-sectional Study

A cognitive-motor framework for spatial navigation in aging and early-stage Alzheimer's disease

Spatial navigation is essential for wellbeing and independence and shows significant declines as part of age-related neurodegenerative disorders, such as Alzheimer's disease. Navigation is also one of the earliest behaviors impacted by this devastating disease. Neurobiological models of aging and spatial navigation have focused primarily on the cognitive factors that account for impaired navigation abilities during the course of healthy aging and early stages of preclinical and prodromal Alzheimer's disease. The contributions of physical factors that are essential to planning and executing movements during successful navigation, such as gait and dynamic balance, are often overlooked despite also being vulnerable to early stages of neurodegenerative disease. We review emerging evidence that spatial navigation and functional mobility each draw on highly overlapping sensory systems, cognitive processes, and brain structures that are susceptible to healthy and pathological aging processes. Based on this evidence, we provide an alternative to models that have focused primarily on spatial navigation as a higher order cognitive function dependent on brain areas such as the hippocampus and entorhinal cortex. Instead, we argue that spatial navigation may offer an ecologically valid cognitive-motor phenotype of age-related cognitive dysfunction. We propose that dual cognitive-motor deficits in spatial navigation may arise from early changes in neuromodulatory and peripheral sensory systems that precede changes in regions such as the entorhinal cortex.

An experimental framework for conjoint measures of olfaction, navigation, and motion as pre-clinical biomarkers of Alzheimer's disease

Elucidating Alzheimer's disease (AD) prodromal symptoms can resolve the outstanding challenge of early diagnosis. Based on intrinsically related substrates of olfaction and spatial navigation, we propose a novel experimental framework for their conjoint study. Artificial intelligence-driven multimodal study combining self-collected olfactory and motion data with available big clinical datasets can potentially promote high-precision early clinical screenings to facilitate timely interventions targeting neurodegenerative progression.

Lattice 123 pattern for automated Alzheimer's detection using EEG signal

This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.

The Role of Assistive Technology in Enabling Older Adults to Achieve Independent Living: Past and Future

In this viewpoint, we present evidence of a marked increase in the use of assistive technology (AT) by older adults over the last 25 years. We also explain the way in which this use has expanded not only as an increase in terms of the total number of users but also by going beyond the typical scopes of use from its inception in 1999 to reach new categories of users. We outline our opinions on some of the key driving forces behind this expansion, such as population demographic changes, technological advances, and the promotion of AT as a means to enable older adults to achieve independent living. As well as our review of the evolution of AT over the past 25 years, we also discuss the future of AT research as a field and the need for harmonization of terminology in AT research. Finally, we outline how our experience in North Norfolk (notably the United Kingdom's most old age-dependent district) suggests that cocreation may be the key to not only successful research trials in the field of AT but also to the successful sustained adoption of AT beyond its original scope of use.

The role of geographic information system and global positioning system in dementia care and research: a scoping review

BACKGROUND

Geographic Information System (GIS) and Global Positioning System (GPS), vital tools for supporting public health research, provide a framework to collect, analyze and visualize the interaction between different levels of the health care system. The extent to which GIS and GPS applications have been used in dementia care and research is not yet investigated. This scoping review aims to elaborate on the role and types of GIS and GPS applications in dementia care and research.

METHODS

A scoping review was conducted based on Arksey and O'Malley's framework. All published articles in peer-reviewed journals were searched in PubMed, Scopus, and Web of Science, subject to involving at least one GIS/GPS approach focused on dementia. Eligible studies were reviewed, grouped, and synthesized to identify GIS and GPS applications. The PRISMA standard was used to report the study.

RESULTS

Ninety-two studies met our inclusion criteria, and their data were extracted. Six types of GIS/GPS applications had been reported in dementia literature including mapping and surveillance (n = 59), data preparation (n = 26), dementia care provision (n = 18), basic research (n = 18), contextual and risk factor analysis (n = 4), and planning (n = 1). Thematic mapping and GPS were most frequently used techniques in the dementia field.

CONCLUSIONS

Even though the applications of GIS/GPS methodologies in dementia care and research are growing, there is limited research on GIS/GPS utilization in dementia care, risk factor analysis, and dementia policy planning. GIS and GPS are space-based systems, so they have a strong capacity for developing innovative research based on spatial analysis in the area of dementia. The existing research has been summarized in this review which could help researchers to know the GIS/GPS capabilities in dementia research.

Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients

Impairment of navigation is one of the earliest symptoms of Alzheimer's disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behaviour in AD. The aim was to use data-driven machine learning approaches to explore spatial metrics within real life navigational traces that discriminate AD patients from controls. 15 AD patients and 18 controls underwent tracking of their outdoor navigation over two weeks. Three kinds of spatiotemporal features of segments were extracted, characterising the mobility domain (entropy, segment similarity, distance from home), spatial shape (total turning angle, segment complexity), and temporal characteristics (stop duration). Patients significantly differed from controls on entropy (p-value 0.008), segment similarity (p-value [Formula: see text]), and distance from home (p-value [Formula: see text]). Graph-based analyses yielded preliminary data indicating that topological features assessing the connectivity of visited locations may also differentiate patients from controls. In conclusion, our results show that specific outdoor navigation features discriminate AD patients from controls, which has significant implication for future AD diagnostics, outcome measures and interventions. Furthermore, this work illustrates how wearables-based sensing of everyday behaviour may be used to deliver ecologically-valid digital biomarkers of AD pathophysiology.