Objective:In order to meet the needs of building Internet of Things(IoT)of medical equipment for mobile deployment,rapid deployment,high-speed and stable data transmission,a wireless IoT acquisition terminal for medical equipment on the basis of Wi-Fi 6 was developed.Methods:Wi-Fi 6 technique was adopted to construct IoT of medical equipment,and the data acquisition terminal included Wi-Fi 6-based customer premises equipment(CPE)and intelligent wireless access point(AP).The CPE adopted a domestic main control chip and Wi-Fi chips,which included two 2.4G and 5G antennas,and was compatible with multiple interfaces such as RS232 and RJ45.The data of medical equipment were converted into wireless transmission through wired communication interfaces.The security access and data traceability of medical equipment were supported through secure secondary authentication with security control enhanced by"white list plus certificate".The intelligent wireless AP was compatible with various RF devices such as Wi-Fi,bluetooth,radio frequency identification,etc.(included 2.4G and 5G antennas).CPE and AP jointly apply dual-transmitter selection technique to ensure stable data transmission.Results:The key performance of wireless IoT acquisition terminals has been tested,and the results indicated that the integrity of acquisition data of intelligent acquisition terminal was consistent with that of output data,with a maximum latency of 9 ms and an average latency of 2 ms.The tested results can meet the expected requirements.Conclusion:The wireless IoT data of medical equipment that based on the acquisition terminal can stably and quickly collect data of equipment to IoT platform,providing paradigm for the construction of wireless IoT of medical equipment.

The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People's Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
Intensive Care Units ; Internet of Things ; Humans ; Internet ; Data Collection

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

Objective:In order to meet the needs of building Internet of Things(IoT)of medical equipment for mobile deployment,rapid deployment,high-speed and stable data transmission,a wireless IoT acquisition terminal for medical equipment on the basis of Wi-Fi 6 was developed.Methods:Wi-Fi 6 technique was adopted to construct IoT of medical equipment,and the data acquisition terminal included Wi-Fi 6-based customer premises equipment(CPE)and intelligent wireless access point(AP).The CPE adopted a domestic main control chip and Wi-Fi chips,which included two 2.4G and 5G antennas,and was compatible with multiple interfaces such as RS232 and RJ45.The data of medical equipment were converted into wireless transmission through wired communication interfaces.The security access and data traceability of medical equipment were supported through secure secondary authentication with security control enhanced by"white list plus certificate".The intelligent wireless AP was compatible with various RF devices such as Wi-Fi,bluetooth,radio frequency identification,etc.(included 2.4G and 5G antennas).CPE and AP jointly apply dual-transmitter selection technique to ensure stable data transmission.Results:The key performance of wireless IoT acquisition terminals has been tested,and the results indicated that the integrity of acquisition data of intelligent acquisition terminal was consistent with that of output data,with a maximum latency of 9 ms and an average latency of 2 ms.The tested results can meet the expected requirements.Conclusion:The wireless IoT data of medical equipment that based on the acquisition terminal can stably and quickly collect data of equipment to IoT platform,providing paradigm for the construction of wireless IoT of medical equipment.

In order to quickly deploy and carry out emergency diagnosis and treatment of gynecological diseases,a portable mobile folding cabin for gynecological diagnosis and treatment in emergency treatment scenarios was developed.The cabin folding cabin was mainly composed of two parts:the folding cabin structure and the folding heated gynecological examination bed.The cabin body was folded and retracted by means of lightweight plate and hinging;the folding bed was designed to realize the emergency diagnosis and treatment of gynecological diseases through the folding of the bed body and key components,the heating system and the disposable antibacterial bed sheet.The portable mobile gynecological diagnosis and treatment folding cabin and supporting examination folding bed can meet the requirements of gynecological disease diagnosis and treatment,meet the design requirements of"integration of storage,transportation,exhibition,collection and transportation",solve the problem that the examination bed can be heated and prevent cross-infection,and at the same time,it can realize the rapid deployment of gynecological diagnosis and treatment activities,which supplements the vacancy of portable mobile equipment required for gynecological diagnosis and treatment in emergency treatment scenarios.

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.
Humans ; Artificial Intelligence ; Internet of Things ; Wearable Electronic Devices ; Monitoring, Physiologic/methods* ; Electrocardiography ; Internet

Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.
Internet of Things ; Reproducibility of Results ; Internet ; Machine Learning ; Technology

Breathing pattern parameters refer to the characteristic pattern parameters of respiratory movements, including the breathing amplitude and cycle, chest and abdomen contribution, coordination, etc. It is of great importance to analyze the breathing pattern parameters quantificationally when exploring the pathophysiological variations of breathing and providing instructions on pulmonary rehabilitation training. Our study provided detailed method to quantify breathing pattern parameters including respiratory rate, inspiratory time, expiratory time, inspiratory time proportion, tidal volume, chest respiratory contribution ratio, thoracoabdominal phase difference and peak inspiratory flow. We also brought in "respiratory signal quality index" to deal with the quality evaluation and quantification analysis of long-term thoracic-abdominal respiratory movement signal recorded, and proposed the way of analyzing the variance of breathing pattern parameters. On this basis, we collected chest and abdomen respiratory movement signals in 23 chronic obstructive pulmonary disease (COPD) patients and 22 normal pulmonary function subjects under spontaneous state in a 15 minute-interval using portable cardio-pulmonary monitoring system. We then quantified subjects' breathing pattern parameters and variability. The results showed great difference between the COPD patients and the controls in terms of respiratory rate, inspiratory time, expiratory time, thoracoabdominal phase difference and peak inspiratory flow. COPD patients also showed greater variance of breathing pattern parameters than the controls, and unsynchronized thoracic-abdominal movements were even observed among several patients. Therefore, the quantification and analyzing method of breathing pattern parameters based on the portable cardiopulmonary parameters monitoring system might assist the diagnosis and assessment of respiratory system diseases and hopefully provide new parameters and indexes for monitoring the physical status of patients with cardiopulmonary disease.
Humans ; Lung ; Pulmonary Disease, Chronic Obstructive ; Respiration ; Tidal Volume ; Wearable Electronic Devices

As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.
Heart Rate ; Humans ; Monitoring, Physiologic ; Movement ; Sleep Apnea Syndromes ; Wearable Electronic Devices

This paper aims to study the accuracy of cardiopulmonary physiological parameters measurement under different exercise intensity in the accompanying (wearable) physiological parameter monitoring system. SensEcho, an accompanying physiological parameter monitoring system, and CORTEX METALYZER 3B, a cardiopulmonary function testing system, were used to simultaneously collect the cardiopulmonary physiological parameters of 28 healthy volunteers (17 males and 11 females) in various exercise states, such as standing, lying down and Bruce treadmill exercise. Bland-Altman analysis, correlation analysis and other methods, from the perspective of group and individual, were used to contrast and analyze the two types of equipment to measure parameters of heart rate and breathing rate. The results of group analysis showed that the heart rate and respiratory rate data box charts collected by the two devices were highly consistent. The heart rate difference was (-0.407 ± 3.380) times/min, and the respiratory rate difference was (-0.560 ± 7.047) times/min. The difference was very small. The Bland-Altman plot of the heart rate and respiratory rate in each experimental stage showed that the proportion of mean ± 2SD was 96.86% and 95.29%, respectively. The results of individual analysis showed that the correlation coefficients of the whole-process heart rate and respiratory rate data were all greater than 0.9. In conclusion, SensEcho, as an accompanying physiological parameter monitoring system, can accurately measure the human heart rate, respiration rate and other key cardiopulmonary physiological parameters under various sports conditions. It can maintain good stability under various sports conditions and meet the requirements of continuous physiological signal collection and analysis application under sports conditions.