Wearable Sensor Systems to Detect Biomarkers of Personality Traits for Healthy Aging: A Review

Wearable sensor systems (WSS) have garnered substantial attention as they showcase their versatility not only in the development of automated healthcare systems and shaping smart cities but also in extending their applications into fields such as personalized fitness monitoring and seamless human-computer interaction. Wearable technologies have evolved into more sophisticated forms, significantly improving their capacity to capture multimodal physiological signals from individuals. The recorded physiological signals, including electroencephalography (EEG), electrocardiogram (ECG), galvanic skin response (GSR), photoplethysmography (PPG), and electromyogram (EMG), contain significant and compelling information about the health conditions of individuals. This information has the potential to contribute to and enhance longevity and subjective well-being, aspects that remain mostly unexplored to date. This review delves into the contemporary landscape of research, aiming to unravel the multifaceted interplay among personality traits, physiological signals, and biomarkers that collectively contribute to active and healthy aging. Specifically, we focus on sensing methods and techniques to identify particular personality traits and their connection with health outcomes. The review also outlines key studies that involve the physiological parameters used for health control and age-related diseases. The work also highlights the various kinds of physiological signals containing different useful identifiable bioindicators for healthy aging across five personality dimensions. Finally, we address technical challenges observed in wearable sensor systems, encompassing data integration, sample size limitations, and privacy concerns, while also presenting a roadmap for future research directions and opportunities.