A machine learning approach for semi-automatic assessment of IADL dependence in older adults with wearable sensors
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AuthorGarcía Moreno, Francisco Manuel; Bermúdez Edo, María del Campo; Rodríguez García, Estefanía; Pérez Mármol, José Manuel; Garrido Bullejos, José Luis; Rodríguez Fortiz, María José
Dependence assessmentIADLOlder adultsMachine learningWearable sensorsE-healthPrediction
Francisco M. Garcia-Moreno... [et al.]. A machine learning approach for semi-automatic assessment of IADL dependence in older adults with wearable sensors, International Journal of Medical Informatics, Volume 157, 2022, 104625, ISSN 1386-5056, [https://doi.org/10.1016/j.ijmedinf.2021.104625]
SponsorshipSpanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion) PID2019-109644RB-I00/AEI/10.13039/501100011033
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.