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The research conducted in this publication was funded by the Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion) under grant number: PID2019-109644RB-I00/AEI/10.13039/501100011033. Funding for open access charge: Uni-versidad de Granada/CBUA., Background and Objective: The assessment of dependence in older adults currently requires a manual collection of data taken from questionnaires. This process is time consuming for the clinicians and intrudes the daily life of the elderly. This paper aims to semi-automate the acquisition and analysis of health data to assess and predict the dependence in older adults while executing one instrumental activity of daily living (IADL). Methods: In a mobile-health (m-health) scenario, we analyze whether the acquisition of data through wearables during the performance of IADLs, and with the help of machine learning techniques could replace the traditional questionnaires to evaluate dependence. To that end, we collected data from wearables, while older adults do the shopping activity. A trial supervisor (TS) labelled the different shopping stages (SS) in the collected data. We performed data pre-processing techniques over those SS and analyzed them with three machine learning algorithms: k-Nearest Neighbors (k-NN), Random Forest (RF) and Support Vector Machines (SVM). Results: Our results confirm that it is possible to replace the traditional questionnaires with wearable data. In particular, the best learning algorithm we tried reported an accuracy of 97% in the assessment of dependence. We tuned the hyperparameters of this algorithm and used embedded feature selection technique to get the best performance with a subset of only 10 features out of the initial 85. This model considers only features extracted from four sensors of a single wearable: accelerometer, heart rate, electrodermal activity and temperature. Although these features are not observational, our current proposal is semi-automatic, because it needs a TS labelling the SS (with a smartphone application). In the future, this labelling process could be automatic as well. Conclusions: Our method can semi-automatically assess the dependence, without disturbing daily activities of elderly people. This method can save clinicians’ time in the evaluation of dependence in older adults and reduce healthcare costs., Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion) PID2019-109644RB-I00/AEI/10.13039/501100011033

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty., Ministry of Economy and Competitiveness from Spain MINECO/FEDER MAT2017-85999P, European Union (EU) MINECO/FEDER MAT2017-85999P, Regional Government of Andalusia Research Fund from Spain A-BIO-157-UGR-18

The increasing adoption of mobile computing technology in the health and social domains offers new possibilities, for instance, promoting active aging. Health deterioration in elderly people could be successfully assessed by monitoring activities of daily living (ADLs) through mobile technology. In particular, frailty affects several dimensions (physical, psychological, and social) of human functioning, which are required to perform instrumental ADLs (IADLs). Starting from the definition of a model, this paper proposes the design of an intelligent mobile health system to assess frailty in an ecological way: to automatize the frailty assessment through wearable sensors, unobtrusively in free-living environments, and using machine learning in order to reduce the traditional efforts of clinicians assessing frailty. It supports automatic data collection from sensors and artificial intelligence analysis during the performance of real IADLs by elderly. The proposed system uses mobile/wearable devices, follows a microservices software architecture, and implements machine learning algorithms. A technical validation of the proposal is shown.