Automation of the Timed Up and Go Test Using a Doppler Radar System for Gait and Balance Analysis in Elderly People.

The timed up and go (TUG) test is a simple, valid, and reliable clinical tool that is widely used to assess mobility in elderly people. Several research studies have been conducted to automate the TUG test using wearable sensors or motion-tracking systems. Despite their promising results, the adopted technological systems present inconveniences in terms of acceptability and privacy protection. In this work, we propose to overcome these problems by using a Doppler radar system set into the backrest of a chair in order to automate the TUG test and extract additional information from its phases (i.e., transfer, walk, and turn). We intend to segment its phases and extract spatiotemporal gait parameters automatically. Our methodology is mainly based on a multiresolution analysis of radar signals. We proposed a segmentation technique based on the extraction of limbs oscillations signals through a semisupervised machine learning approach, on the one hand, and the application of the DARC algorithm on the other hand. Once the speed signals of torso and limbs oscillations were detected, we suggested estimating 14 gait parameters. All our approaches were validated by comparing outcomes to those obtained from a reference Vicon system. High correlation coefficients were obtained by comparing the speed signals of the torso (ρ=0.8), the speed signals of limbs oscillations (ρ=0.91), the initial and final indices of TUG phases (ρ=0.95), and the extracted parameters (percentage error < 4.8) obtained after radar signal processing to those obtained from the Vicon system.

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach.

Early detection of physical frailty and infectious diseases in seniors is important to avoid any fatal drawback and promptly provide them with the necessary healthcare. One of the major symptoms of viral infections is elevated body temperature. In this work, preparation and implementation of multi-age thermal faces dataset is done to train different "You Only Look Once" (YOLO) object detection models (YOLOv5,6 and 7) for eye detection. Eye detection allows scanning for the most accurate temperature in the face, which is the inner canthus temperature. An approach using an elderly thermal dataset is performed in order to produce an eye detection model specifically for elderly people. An application of transfer learning is applied from a multi-age YOLOv7 model to an elderly YOLOv7 model. The comparison of speed, accuracy, and size between the trained models shows that the YOLOv7 model performed the best (Mean average precision at Intersection over Union of 0.5 (mAP@.5) = 0.996 and Frames per Seconds (FPS) = 150). The bounding box of eyes is scanned for the highest temperature, resulting in a normalized error distance of 0.03. This work presents a fast and reliable temperature detection model generated using non-contact infrared camera and a deep learning approach.

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals.

Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart's rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.

Analysis of Saturation in the Emergency Department: A Data-Driven Queuing Model Using Machine Learning.

Emergency department is a key component of the health system where the management of crowding situations is crucial to the well-being of patients. This study proposes a new machine learning methodology and a queuing network model to measure and optimize crowding through a congestion indicator, which indicates a real-time level saturation.

Multimorbidity clustering of the emergency department patient flow: Impact analysis of new unscheduled care clinics.

BACKGROUND: In France, the number of emergency department (ED) admissions doubled between 1996 and 2016. To cope with the resulting crowding situation, redirecting patients to new healthcare services was considered a viable solution which would spread demand more evenly across available healthcare delivery points and render care more efficient. The objective of this study was to analyze the impact of opening new on-demand care services based on variations in patient flow at a large hospital emergency department. METHODS: We performed a before-and-after study investigating the use of unscheduled care services in the Aube region in eastern France, that focused on ED attendance at Troyes Hospital. A hierarchical clustering based on co-occurrence of diagnoses was applied which divided the population into different multimorbidity profiles. Temporal trends of the resultant clusters were also studied empirically and using regression models. A multivariate logistic regression model was constructed to adjust the periodic effect for appropriate confounders and therefore confirm its presence. RESULTS: In total, 120,722 visits to the ED were recorded over a 24-month period (2018-2019) and 16 clusters were identified, accounting for 94.76% of all visits. There was a decrease of 56.77 visits per week in seven specific clusters and an increase of use of unscheduled health care services by 328.12 visits per week. CONCLUSIONS: Using an innovative and reliable methodology to evaluate changes in patient flow through the ED, these findings may help inform public health policy experts on the implementation of unscheduled care services to ease pressure on hospital EDs.

Effect of the COVID-19 pandemic lockdown on non-COVID-19 emergency department visits in Eastern France: Reduced risk or avoidance behavior?

OBJECTIVE: To study the impact of COVID-19 pandemic lockdown on avoided emergency department visits and consequent hospitalizations. STUDY DESIGN: An observational retrospective design was used to investigate avoided visits and hospitalizations of an departmental emergency department combined with a clustering approach on multimorbidity patterns. METHODS: A multimorbidity clustering technique was applied on the emergency department diagnostics to segment the population in diseases clusters. Global visits and hospitalizations from an emergency department during the 2020 lockdown were put in perspective with the same period during 2019. Using a comparison with the five previous years, avoided hospitalizations per inhabitants during the lockdown were estimated for each diseases cluster. RESULTS: During the 8 weeks of lockdown, the number of emergency department visits have been reduced by 41.47% and resultant hospitalizations by 28.50% compared to 2019. The retrospective study showed that 14 of 17 diseases clusters had a statistically significant reduction in hospitalizations with a pronounced effect on lower acuity diagnoses and middle-aged patient, leading to 293 avoided hospitalizations per 100,000 inhabitants compared to the 5 previous years and to the 85.8 COVID-19 hospitalizations per 100,000 inhabitants. CONCLUSION: Although specific to a regional context of pandemic containment, the study suggest that COVID-19 lockdown had beneficial effects on the crowding situation of the emergency departments and hospitals with avoidance effects primarily link to reduced risks.

An Evidential Framework for Localization of Sensors in Indoor Environments.

Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster-Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor's zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

Doppler Radar System for In-Home Gait Characterization using Wavelet Transform Analysis.

The evaluation of walking speed plays an important role in gerontology as it reflects the health and functional status of older people. In this paper, we propose the use of a Doppler radar system with continuous wavelet transform (CWT) analysis for in-home gait characterization. A methodology based on an accurate 3D motion-capture camera system (Vicon) has been developed in order to validate the suitable mother wavelet. The CWT analysis with several mother wavelets has been applied to our experimental Doppler radar signals. The Pearson Correlation coefficient (ρ) has been computed between the gait speed signals obtained from the radar and those obtained from the Vicon system. Our outcomes suggest the use of Daubechies5 and Symlets7 wavelets giving a ρ values of 0.86 and 0.85 respectively with a mean square error value less than 0.05 m/s in comparison with the correct gait speed value.

Early-warning of ARDS using novelty detection and data fusion.

Acute respiratory distress syndrome (ARDS) is a critical condition that disturbs the respiratory system and may lead to death. Early identification of this syndrome is crucial for the implementation of preventive measures. The present paper focuses on the prediction of the onset of this syndrome using physiological records of patients. Heart rate, respiratory rate, peripheral arterial oxygen saturation and mean airway blood pressure were considered. The method proposed in this paper uses first distance-based novelty detection that allows detecting deviations from normal states for each signal. Then, linear and nonlinear kernel-based data fusion algorithms are introduced to combine the individual signal decisions. The proposed method is evaluated using the MIMIC II physiological database. As a result, ARDS is detected in the early phases of occurrence with sensitivity and specificity of 65% and 100% respectively for the combination of all the signals in study. Moreover, the proposed method outperforms current state-of-the-art methods in real-time surveillance of ARDS using only physiological data with an average prediction before 39 h of onset.