Irbm最新文献

Introduction

Staying hydrated is an essential aspect of good health for people of all ages. Tracking fluid intake is important to ensure proper hydration and prompt users to drink as needed. Previous literature has attempted to measure the amount of fluid consumption, often using wearables or sensors embedded in containers.

Objective

In this paper, we introduce a novel vision-based method to estimate the amount of fluid consumed.

Methods

We trained different 3D Convolutional Neural Networks on data from 8 participants drinking from multiple containers and engaging in other activities in a simulated home environment.

Results

We show that it is possible to perform both drinking detection and volume intake estimation in a single algorithm with a Mean Absolute Percent Error (MAPE) of 28.5% and a Mean Percent Error (MPE) of 2.6% with 10-Fold and a MAPE of 42.4% and MPE of 25.4% for Leave-One-Subject-Out cross validation.

Conclusion

This shows that using video inputs does have the potential to detect and estimate the amount of fluid consumed throughout the day.

Background

Monitoring COVID-19 symptoms has become a critical task in controlling the spread of the virus and preventing hospitalizations. Aiming to contribute to efficient monitoring solutions, this article presents the development and testing of a wearable device capable of continuous monitoring biometric signals associated with the presence of COVID-19, such as the heart rate, the blood oxygen saturation, and the body temperature.

Methods

To ensure continuous monitoring the device is designed to be worn in the ear. Here, the temperature is measured through a non-contact infrared temperature sensor placed inside the ear canal while the heart rate and the pulse oximetry signals are monitored through a photoplethysmography reflective sensor positioned at the earlobe.

The proposed device's performance was evaluated by comparing it against a medical certified station. Usability and ergonomics were assessed through users' questionnaires. Additionally, experiments were performed to evaluate the hearing loss when the proposed device is in use. Data was acquired from 30 individuals of different sex, aged between 20 and 43 years old. In relation to usability and ergonomics the variation in ear dimensions was accessed and related to the device's comfort limitations.

Results

The temperature measurement produced a moderate correlation ($R=0.42$), despite a higher standard deviation was found in the proposed solution. This is due to the limited variability in temperature data, creating a short measuring range, as only healthy people were tested. The heart rate measurement also showed good correlation ($R=0.96$), with the proposed solution showing good repeatability with a standard deviation of 6.06 BPM, however, the SpO2 measurement was suboptimal ($R=0.14$).

The ergonomic evaluation revealed that most participants found the device shape comfortable, but some found the dimensions not adequate.

Additionally, the device was found to be user-friendly, with most participants reporting that they found it to be intuitive, and none reported a major loss in hearing in a normal conversation, however, there's a negligible loss of approximately 0.56 dB.

Conclusions

During this study, it was possible to develop and evaluate a wearable device that was suggested for monitoring biometric signals. The device demonstrated great reliability in temperature and heart rate measurement but showed limitations in the accuracy of pulse oximetry. The main contribution of this work is the evaluation of a continuous non-invasive monitoring concept for COVID-19 related biometric signals, which indicates good applicability in the case study.

Objective

Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures.

Methods

We designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics.

Results

After balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09).

Conclusion

This study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stability.

Background

Stair walking requires to elevate or lower the body center of mass and results in increased muscle contractions and consumed energy compared to level walking. Mechanical work produced by the body can be quantified through Individual Limb Method and the summed lower limb joint work but there does not exist normative data of these works in stair ascent and descent compared to slope ascent and descent of the same individuals.

Methods

Upstair and downstair walking were investigated at 0%, 5% and 12% inclinations and compared to upslope and downslope walking for thirteen able-bodied volunteers. Lower limb joint and individual limb powers and works were compared across walking conditions.

Findings

Work production and absorption required to elevate or lower the center of mass directly depend on the inclination to be crossed (about 0.35 J/kg for 5% slope, 0.9 J/kg for 12% slope and 1.6 J/kg for stair). However, the distribution among joints and between gait phases is different when considering stair versus slope walking. In particular, the role of the knee is exacerbated for work production in stair ascent (45% of total work) as well as for work absorption in stair descent (61% of total work). Also, more work production/absorption is performed during the swing phase for stair walking then for slope walking.

Interpretation

This study provides reference data of the Individual Limb mechanical work performed during stair walking and show that this method can substitute to summed lower limb joint one during the stance phase of stair walking.

Objectives

The temporo-mandibular joint (TMJ) has implications in vital functions and its disorder prevalence is between 5% and 12%. The mandible motions rely on two joints where mandibular condyles are generally asymmetric and highly individual. They rotate during jaw opening and closing and translate vertically and anteroposteriorly. Quantitative motion analysis tools are of interest to better understand normal and abnormal TMJ behavior. Previous studies have reported the asymmetrical behavior of the mandible compared to the skull as well as the synchronism of rotation and translation during its motions. But none of them has developed an experimental protocol using in vivo motion data fused with a tridimensional (3D) model. Therefore, we aim to provide the detailed kinematic parameters of the mandible compared to the skull, of the 2 condyles compared to their sockets and the instantaneous helicoidal axis (IHA) calculation through a clearly described new technology: in vivo data motion fused with virtual palpation on 3D models. We also compare the accuracy and the consistency of our results with the existing literature.

Material and methods

Five healthy subjects fitted with a tailor-made dental and head clusters performed mouth opening/closing, diduction and chewing motions. 15 anatomical landmarks (ALs) were palpated on their skull and their mandible. The trajectory of the markers and ALs was recorded by opto-electronic cameras. 3D models created from magnetic resonance imaging (MRI) from the 5 subjects were processed through a segmentation procedure and imported into a musculo-skeletal data processing software. Virtual palpation was used to locate specific ALs and to build coordinate systems following the ISB recommendations. The ALs coordinates, the motion files and the morphological model were fused. Motion cycles were normalized from 1 to 100% of rotations and translations duration in coordinate systems, instantaneous helical axis (IHA) parameters were computed for the 3 motions.

Results

Median RMSE between manually and virtually palpated ALs was 8,0 mm.

During opening motion, rotation around the Z-axis (median 24,9°), translations along the X-axis and the Y-axis (median 9,7 mm and 6,3 mm respectively) were happening all at once. The IHA was obliquely orientated.

During diduction motion, rotations around the Y-axis and the X-axis (median 10,7° and 3.3° respectively), translation on the Z-axis is (median −9.4 mm) occurred simultaneously. The IHA orientation was oblique and changed accordingly to the diduction side.

During chewing motion, median rotation around the Z-axis was −2.2° and median translation on the Y-axis −1.0 mm. The IHA pathway high asymmetry coincided with typical movements of working and balancing condyles.

Conclusion

Complete 3D kinematics parameters of the TMJs, corresponding to the ISB reco