Overview
This project is a single-center clinical evaluation study designed to validate a non-invasive mattress-based smart monitoring system. The system is intended for installation on standard hospital beds to provide continuous, contact-free monitoring of vital signs, including heart rate, respiration, body temperature, and posture, in patients with cardiovascular diseases. The system will integrate monitoring data with hospital information systems and personal health platforms to support clinical management and remote health monitoring.
Description
In the diagnosis, treatment, and rehabilitation of cardiovascular diseases, accurate monitoring of key physiological signs provides the fundamental basis for clinicians to assess patient status and formulate treatment strategies. Traditional monitoring instruments used to acquire physiological data, such as electrocardiogram (ECG) monitors and polysomnography systems, require physical connections through electrode leads or ECG patches, which may increase patient discomfort and the risk of adverse events. Therefore, the development of monitoring devices with non-invasive and multi-parameter sensing capabilities is of significant importance for clinical care, nursing practice, and long-term health management.
This project focuses on the development and clinical evaluation of a non-invasive intelligent monitoring device for human physiological signals. The device is designed in the form of a mattress installed on a conventional hospital bed to enable real-time, accurate, continuous, and non-invasive monitoring of important physiological parameters in cardiovascular patients, including heart rate, respiration, body temperature, and body posture. Monitoring data will be integrated with hospital information systems and patient-side digital platforms to support clinical decision-making, disease management, and remote health monitoring.
A clinical evaluation will be conducted prior to product commercialization to determine whether the monitoring performance of the mattress-based system is non-inferior to that of conventional bedside ECG monitoring systems. Large-scale, long-term, and continuous physiological data from cardiovascular patients will be collected and analyzed in real-world clinical settings. Advanced technologies, including artificial intelligence and cloud-based data processing, will be applied to store, analyze, and interpret physiological signals. These data will enable the generation of personalized health monitoring reports and corresponding clinical recommendations.
The results of this study are expected to provide essential clinical evidence supporting the development and industrialization of the non-invasive intelligent monitoring system. In addition, the study will generate key data required for device research and development, quality control standards, regulatory evaluation, and medical device registration certification, ultimately facilitating the stepwise translation of the technology into clinical practice.
Eligibility
Inclusion Criteria:
- Adult inpatients (aged ≥18 years) admitted to the cardiovascular disease ward.
- Willing to sign the informed consent form in person.
- Capable of understanding and following the research instructions, as well as having the language ability to communicate and fill out the relevant questionnaires.
Exclusion Criteria:
- The patient has an allergic reaction to any materials related to the research equipment or test materials (such as traditional monitoring instruments, mattress fibers, etc.).
- There are serious diseases that require urgent medical intervention, such as hypertensive emergencies, acute myocardial infarction, acute heart failure, acute stroke, severe ventricular arrhythmias, and shock, etc.
- The patient cannot cooperate with bed rest due to their condition or mental factors.
- Severe respiratory diseases requiring mechanical assisted ventilation.
- Patients requiring assistance from other medical electronic devices or instruments (such as temporary cardiac pacemakers, bedside hemodialysis, etc.).
- The research team, after assessment, considers that there are other patients who do not meet the inclusion criteria.
