A machine learning approach for semi-automatic assessment of IADL dependence in older adults with wearable sensors. (January 2022) - Garcia-Moreno, Francisco M.; Bermudez-Edo, Maria; Rodríguez-García, Estefanía; Pérez-Mármol, José Manuel; Garrido, José Luis; Rodríguez-Fórtiz, María José
Graphical abstract: Highlights: Novel method to automate IADLs dependence assessment with sensors. The use of wearables to collect sensory data of elderly people. Holistic and ecological approach to save clinicians' time and reduce healthcare costs. Comparison between different Machine learning algorithms. k-Nearest Neighbours (k-NN), Support Vector Machines (SVM) and Random Forest (RF). Abstract: 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.