"Just move!": Detección temprana de deterioro cognitivo leve mediante el análisis del movimiento humano en la vida cotidiana (JUST-MOVE)

Contribute to the discovery of mild cognitive impairment thought human motion analysis (including gait, limb movements, balance and human posture) in everyday life through a minimal and non-intrusive body sensor infrastructure (in both, men and women). 

 Information and Communication Technology (ICT) and particularly Artificial intelligence (AI) are increasingly prevalent in society and are already being employed to healthcare. These technologies have the potential to transform diagnosis, treatment, and patient care. There are already research studies suggesting that AI can support clinicians at diagnosing disease [1], resulting in a change for the better in the healthcare model [2], but also considering all those raising issues related with ethics, accountability, transparency, and privacy [3]. Current healthcare services are focused on preventative, predictive and personalized health, relying heavily on ICTs [4]. Beyond understanding people’s health itself more effectively through digital disruption, AI being an essential part of this process, another of the main reasons for promoting this change is to reduce the rising financial stress that current healthcare systems have to face to support their ever-increasing demand. 

 One of the greatest challenges to AI in healthcare is not only that technologies will be useful, but also ensuring their integration in daily clinical practice [5]. For this reason, it is critical to extract knowledge from everyday life (out-of-lab) to enhance those AI-based supportive systems. Although these systems are currently being studied to different diseases, one of the most challenging objectives is the application to pre-dementia diagnosis. Particularly, Mild Cognitive Impairment (MCI) is the most common pre-dementia stage [6]. It is defined as “cognitive decline greater than expected for an individual’s age and education level, but that does not interfere, at first glance, with activities of daily life [7]. The main motivation of this proposal is the increasing prevalence of MCI, with epidemiological studies estimating it to be between 3% to 19% in adults older than 65 years, and with a risk of it progressing to dementia (11–33% cases) over 2 years. The prevalence increases over time, being up to 50% for those which finally progress to dementia within 5 years [8]. These statistics show that dementia is one of the most common disorders among older adults, and the number of people affected will increase up to three times by 2050 [9]. 

 This project aims to provide a useful tool for supporting the early detection of Mild Cognitive Impairment from the acquisition of human motion in everyday life (by gathering underlying motor actions such as gait, limb movements, balance, and human posture) through a minimal and non-intrusive sensing infrastructure. The project will emphasize progressively reducing the device infrastructure to the minimum possible (consisting of a minimal number of wearables, body-worn sensors, and the smartphone itself). A data processing AI platform, capable of dealing with the uncertainty and heterogeneity of data, will be developed and trained to detect early signs of cognitive impairment for further analysis and contrast by specialists.