Wearable Cardiopulmonary Function Evaluation System for Six-Minute Walking Test

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

[A wearable six-minute walk-based system to predict postoperative pulmonary complications after cardiac valve surgery: an exploratory study]

In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.

Use of the speed achieved on the 6MWT for programming aerobic training in patients recovering from severe COVID-19: an observational study

INTRODUCTION

Patients who suffered severe COVID-19 need pulmonary rehabilitation. Training may be prescribed objectively based on the maximum speed in the six-minute walk test. The objective of this study was to determine the effects of a personalized pulmonary rehabilitation program based on the six-minute walk test speed for post-COVID-19 patients.

METHODS

Observational quasi-experimental study. The pulmonary rehabilitation program consisted of 8 weeks of training, twice a week for 60 minutes per session of supervised exercise. Additionally, the patients carried out home respiratory training. Patients were evaluated by exercise test, spirometry and the Fatigue Assessment Scale before and after the eight-week pulmonary rehabilitation program.

RESULTS

After the pulmonary rehabilitation program, forced vital capacity increased from 2.47 ± 0.60 to 3.06 ± 0.77 L (p < .001) and the six-minute walk test result increased from 363.50 ± 88.87 to 480.9 ± 59.25 m (p < .001). In fatigue perception, a significant decrease was observed, from 24.92 ± 7.01 to 19.10 ± 7.07 points (p < .01). Isotime evaluation of the Incremental Test and the Continuous Test showed a significant reduction in heart rate, dyspnoea and fatigue.

CONCLUSION

The eight-week personalized pulmonary rehabilitation program prescribed on the basis of the six-minute walk test speed improved respiratory function, fatigue perception and the six-minute walk test result in post-COVID-19 patients.KEY MESSAGESCOVID-19 is a multisystem disease with common complications affecting the respiratory, cardiac and musculoskeletal systems.The 6MWT speed-based training plan allowed for increased speed and incline during the eight-week RP program.Aerobic, strength and flexibility training reduced HR, dyspnoea and fatigue in severe post-COVID-19 patients.

Non-invasive devices for respiratory sound monitoring

Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.

Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.

We present the literature review of non-invasive sound acquisition devices and techniques.

The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.

Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.

Smart Multimodal Telehealth-IoT System for COVID-19 Patients

The COVID-19 pandemic has highlighted how the healthcare system could be overwhelmed. Telehealth stands out to be an effective solution, where patients can be monitored remotely without packing hospitals and exposing healthcare givers to the deadly virus. This article presents our Intel award winning solution for diagnosing COVID-19 related symptoms and similar contagious diseases. Our solution realizes an Internet of Things system with multimodal physiological sensing capabilities. The sensor nodes are integrated in a wearable shirt (vest) to enable continuous monitoring in a noninvasive manner; the data are collected and analyzed using advanced machine learning techniques at a gateway for remote access by a healthcare provider. Our system can be used by both patients and quarantined individuals. The article presents an overview of the system and briefly describes some novel techniques for increased resource efficiency and assessment fidelity. Preliminary results are provided and the roadmap for full clinical trials is discussed.

Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR.