Data-driven systems to detect physical weakening from daily routine: A pilot study on elderly over 80 years old.

The use of telemonitoring solutions via wearable sensors is believed to play a major role in the prevention and therapy of physical weakening in older adults. Despite the various studies found in the literature, some elements are still not well addressed, such as the study cohort, the experimental protocol, the type of research design, as well as the relevant features in this context. To this end, the objective of this pilot study was to investigate the efficacy of data-driven systems to characterize older individuals over 80 years of age with impaired physical function, during their daily routine and under unsupervised conditions. We propose a fully automated process which extracts a set of heterogeneous time-domain features from 24-hour files of acceleration and barometric data. After being statistically tested, the most discriminant features fed a group of machine learning classifiers to distinguish frail from non-frail subjects, achieving an accuracy up to 93.51%. Our analysis, conducted over 570 days of recordings, shows that a longitudinal study is important while using the proposed features, in order to ensure a highly specific diagnosis. This work may serve as a basis for the paradigm of future monitoring systems.

A Reliable Fall Detection System Based on Analyzing the Physical Activities of Older Adults Living in Long-Term Care Facilities.

Fall detection systems are designed in view to reduce the serious consequences of falls thanks to the early automatic detection that enables a timely medical intervention. The majority of the state-of-the-art fall detection systems are based on machine learning (ML). For training and performance evaluation, they use some datasets that are collected following predefined simulation protocols i.e. subjects are asked to perform different types of activities and to repeat them several times. Apart from the quality of simulating the activities, protocol-based data collection results in big differences between the distribution of the activities of daily living (ADLs) in these datasets in comparison with the actual distribution in real life. In this work, we first show the effects of this problem on the sensitivity of the ML algorithms and on the interpretability of the reported specificity. Then, we propose a reliable design of an ML-based fall detection system that aims at discriminating falls from the ambiguous ADLs. The latter are extracted from 400 days of recorded activities of older adults experiencing their daily life. The proposed system can be used in neck- and wrist-worn fall detectors. In addition, it is invariant to the rotation of the wearable device. The proposed system shows 100% of sensitivity while it generates an average of one false positive every 25 days for the neck-worn device and an average of one false positive every 3 days for the wrist-worn device.

D-SORM: A digital solution for remote monitoring based on the attitude of wearable devices.

BACKGROUND AND OBJECTIVE: E-health is a growing research topic, especially with the expansion of the Internet of Things (IoT). Miniaturized wearable sensors are auspicious tools for biomedicine and healthcare systems. In this paper, we present D-SORM, a sensor fusion-based digital solution intended to assist clinicians and improve their diagnosis by providing objective measurements and automatic recognition. The aim is to supply an interface for remote monitoring to the medical staff. METHODS: D-SORM platform estimates the wearable device attitude based on its acquired data, and visualizes it in real-time using a graphical user interface (GUI). It also integrates two modules which serve two different medical applications. The first one is arm tele-rehabilitation, where sessions are done online. The practitioner gives the instructions while wearing the device, and the patient has to reproduce the gestures. A processing unit is dedicated to compute statistical features and calculate the success rate. The second one is human motion tracking for elderly care. A novel machine learning architecture is proposed, based on feature fusion, to predict the activities of daily living. RESULTS: The rehabilitation mechanism was tested under supervised conditions, by performing a set of movements. D-SORM provides extra information and objective measurements, thus facilitates the diagnosis of clinicians. The human activity recognition is also validated using a public dataset. With D-SORM, an efficiency ranging from 97.7% to 99.65% is ensured under unsupervised conditions. CONCLUSIONS: The proposed design constitutes a digital clinical tool for medical teams allowing remote health monitoring. It overcomes geographical barriers while providing faster and highly accurate assessment.

Machine Learning-Based Physical Activity Tracking with a view to Frailty Analysis.

Frailty in old age is defined as the individual intrinsic susceptibility of having bad outcomes following a health problem. It relies on sarcopenia, mobility and activity. Recognizing and monitoring a range of physical activities is a necessary step which precedes the analysis of this syndrome. This paper investigates the optimal tools for this recognition in terms of type and placement of wearable sensors. Two machine learning procedures are proposed and compared on a public dataset. The first one is based on deep learning, where feature extraction is done manually, by constructing activity images from raw signals and applying convolutional neural networks to learn optimal features from these images. The second one is based on shallow learning, where hundreds of handcrafted features are extracted manually, followed by a novel feature selection approach to retain the most discriminant subset.Clinical relevance- This analysis is an indispensable prerequisite to develop efficacious way in order to identify people with frailty using sensors and moreover, to take on the challenge of frailty prevention, an actual world health organization priority.