Elderly monitoring using decision trees under domain shifts and computational resource constraints

Tarkett is a global flooring company that developed a piezo-electric sensor encapsulated in the flooring and an embedded system meant to be equipped in nursing home patient rooms. The objective through this industrial project is to build reliable machine learning models able to work in real-time in the embedded system, based on piezo-electric signals, to provide useful information for medical staff to monitor their patients health. Considering different measurement technologies we describe how they affect the original physical signal, as well as different data gathering environments in which several dataset have been recorded. To be able to monitor elderly health state some important recurrent events like walk and some anomalies like falls need to be recognized from floor sensor signals.To this end, the way to process signals into adequate data representation, according to these detection purpose, is also a major challenge. We use a wide feature set based on time series from various signal representations such as Fourier transform, autocorrelation and spectrograms. Using predictive models based on random forests on different experimental datasets we show Tarkett system ability to achieve various monitoring tasks, as well as the relevance of each signal representation and associated features regarding these detection tasks. Nevertheless for these experimental studies to be deployed industrially in FIM Care real installations, machine learning models need to fulfill two crucial requirements. Firstly they have to be confronted with real environment data, meaning to be able to adapt to real installations variability and to activity signal differences between people. In this context we deal with the problem of adapting a predictive model initially trained on experimental data to real data with different empirical distribution. This particular situation in machine learning is known as transfer learning or domain adaptation. We address it by confronting simulated events data to real data on the fall detection task that presents the particularity of extreme class imbalance in real conditions. We investigate the drawbacks of this class imbalance on existing transfer learning methods on decision trees and propose some adaptations to handle this problem. Our contribution is a robust model-based transfer learning algorithm on random forests able to deal with class imbalance and that can also be used to interpret relations between two different domains. Secondly, most of the prediction tasks for elderly monitoring have to work in real time being embedded in an electronic device with limited computational capabilities. Taking into account this kind of constraints while designing a predictive model belongs to a branch of machine learning, known as cost sensitive or budget learning, that became an increasingly active research topic in the past years.We translate embedded system computational resource constraints into a budgeted prediction time framework compatible with decision tree based models and propose an efficient and scalable genetic algorithm considering both feature acquisition cost and evaluation cost allowing to pass from an experimental random forest model to a new simplified one that fits in embedded system resource limits. This algorithm takes advantage of the notion of equivalence between classifiers, meaning models sharing the same decision function but with different structures, to favor feature acquisition cost reduction by exploiting structural variety on decision trees.