Exhaustive Description of the System Architecture and Prototype Implementation of an IoT-Based eHealth Biometric Monitoring System for Elders in Independent Living

An IoT-based cognitive impairment detection device: A newly proposed method in older adults care-choice reaction time-device development and data-driven validation

Background

Research shows that older adults' performance on choice reaction time (CRT) tests can predict cognitive decline. A simple CRT tool could help detect mild cognitive impairment (MCI) and preclinical dementia, allowing for further stratification of cognitive disorders on-site or via telemedicine.

Objective

The primary objective was to develop a CRT testing device and protocol to differentiate between two cognitive impairment categories: (a) subjective cognitive decline (SCD) and non-amnestic mild cognitive impairment (na-MCI), and (b) amnestic mild cognitive impairment (a-MCI) and multiple-domain a-MCI (a-MCI-MD).

Methods

A pilot study in Italy and Romania with 35 older adults (ages 61-85) assessed cognitive function using the Mini-Mental State Examination (MMSE) and a CRT color response task. Reaction time, accuracy, and demographics were recorded, and machine learning classifiers analyzed performance differences to predict preclinical dementia and screen for mild cognitive deficits.

Results

Moderate correlations were found between the MMSE score and both mean reaction time and mean accuracy rate. There was a significant difference between the two groups' reaction time for blue light, but not for any other colors or for mean accuracy rate. SVM and RUSBoosted trees were found to have the best preclinical dementia prediction capabilities among the tested classifier algorithms, both presenting an accuracy rate of 77.1%.

Conclusions

CRT testing with machine learning effectively differentiates cognitive capacities in older adults, facilitating early diagnosis and stratification of neurocognitive diseases and can also identify impairments from stressors like dehydration and sleep deprivation. This study highlights the potential of portable CRT devices for monitoring cognitive function, including SCD and MCI.

A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.