Intelligent Sensing Technologies for the Diagnosis, Monitoring and Therapy of Alzheimer’s Disease: A Systematic Review

Abstract

Alzheimer’s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer’s patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer’s disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer’s. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer’s disease.

Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges

“…Integration of these portable devices with cloud databases will be beneficial for post-processing and diagnosis using cloud analytics. Ongoing future work, as part of the COG-MHEAR project [182], aims to develop and evaluate the use of emerging portable non-invasive sensing technologies [183] for the detection of cognitive load within a hearing aid device. This device could be used by people with hearing loss in smart care home settings to enhance their quality of life.…”

The use of home-based conversations for detecting early dementia: Protocol for the CUBOId TV task

“…These limitations have driven recent interest in passive technologies for longitudinal monitoring of behavioural dementia biomarkers in naturalistic settings [15][16][17]. Preclinical disruptions in activities such as speaking [18], environmental navigation [19], sleep [20] and walking [21] can betray distal biomarkers such as wandering, circadian disturbances, apathy and agitation [22], which may be useful both in diagnosing MCI and predicting which patients with MCI may go on to develop AD.…”

“…Preclinical disruptions in activities such as speaking [18], environmental navigation [19], sleep [20] and walking [21] can betray distal biomarkers such as wandering, circadian disturbances, apathy and agitation [22], which may be useful both in diagnosing MCI and predicting which patients with MCI may go on to develop AD. These ecologically valid, everyday behaviours can be measured continuously and unobtrusively in the home, using networked fixed sensors, or portable sensors in wearables, smartphones and tablets [16]. Fusion across sensing modalities permits fine-grained contextual analysis of how engagement in different ADL covaries over days, weeks and months, and automated machine learning (ML) techniques can leverage intraindividual variability reflective of early cognitive changes [15,23].…”

A longitudinal observational study of home-based conversations for detecting early dementia: protocol for the CUBOId TV task

“…These ecologically valid, everyday behaviours can be measured continuously and unobtrusively in the home, using networked fixed sensors, or portable sensors in wearables, smartphones and tablets. 16 Fusion across sensing modalities permits fine-grained contextual analysis of how engagement in different ADL covaries over days, weeks and months, and automated machine learning (ML) techniques can leverage intraindividual variability reflective of early cognitive changes. 15 24 These technologies have potential to help clinicians build personalised and holistic digital phenotypes for prediction of neurodegeneration.…”

“…These limitations have driven recent interest in passive technologies for longitudinal monitoring of behavioural dementia biomarkers in naturalistic settings. [15][16][17][18] Preclinical disruptions in activities such as speaking, 19 environmental navigation, 20 sleep 21 and walking 22 can betray distal biomarkers such as wandering, circadian disturbances, apathy and agitation, 23 which may be useful both in diagnosing MCI and predicting which patients with MCI may go on to develop AD. These ecologically valid, everyday behaviours can be measured continuously and unobtrusively in the home, using networked fixed sensors, or portable sensors in wearables, smartphones and tablets.…”