SMART HOME SENSORS FOR AMBIENT ASSISTED LIVING IN THE GERIATRIC POPULATION: CLINICAL EFFICACY, ETHICAL CONSIDERATIONS, AND SOCIOECONOMIC DISPARITIES
TL;DR
Clinical evidence indicates AAL networks effectively identify early digital biomarkers of cognitive and physiological decline, mitigating injurious falls and reducing 30-day hospital readmissions, but their unchecked commercial deployment risks institutionalizing diagnostic inequalities.
Abstract
Background: The global demographic shift toward an aging population exacerbates the clinical burden of multi-morbidity, falls, and functional decline, alongside a persistent shortage of geriatric healthcare professionals. Ambient Assisted Living (AAL) technologies utilizing edge-AI passive sensors offer a scalable, unobtrusive approach to continuous remote monitoring. Objectives: This narrative review aims to synthesize recent clinical data regarding the efficacy of AAL systems in geriatric care while critically evaluating the ethical tensions and socioeconomic barriers impeding equitable adoption. Methods: A comprehensive synthesis of peer-reviewed literature published between 2020 and 2026 was conducted, focusing on the biomechanical tracking of falls, longitudinal monitoring of Activities of Daily Living (ADLs), dynamic consent, privacy concerns, and digital health equity. Results: Clinical evidence indicates AAL networks effectively identify early digital biomarkers of cognitive and physiological decline, mitigating injurious falls and reducing 30-day hospital readmissions. However, continuous domestic surveillance introduces significant ethical dilemmas concerning patient autonomy and the medicalization of the home environment. Furthermore, formidable financial constraints, algorithmic bias, and inadequate broadband infrastructure restrict AAL adoption to affluent demographics, exacerbating systemic health disparities. Conclusions: While AAL technologies present substantial clinical utility for proactive geriatric care, their unchecked commercial deployment risks institutionalizing diagnostic inequalities. Equitable implementation requires formal public health subsidization, equity-by-design algorithmic training, and rigorous bioethical frameworks to balance clinical beneficence with patient privacy.