Cardiovascular disease has caused a huge burden of disease worldwide, and the rapid advancement of smart wearable devices has provided new means for early diagnosis, real-time monitoring, and event prevention of cardiovascular disease. Smart wearable devices can be classified into various categories based on detection signals and physical carrier types. Based on an overview of the composition of such devices, this article further introduces the current cutting-edge research and related market products related to smart blood pressure monitoring, electrocardiogram monitoring, and ultrasound monitoring. It also discusses the future development and challenges of such devices, aiming to provide evidence support for the research and development of smart wearable devices in the diagnosis and treatment of cardiovascular diseases in the future.
Wearable devices, as an important component of digital health, are gradually penetrating into the clinical nursing field. This paper explores the current applications of wearable devices in the field of clinical nursing, with a focus on their significant roles in real-time monitoring of physiological parameters, disease management, functional rehabilitation exercises. Additionally, it analyzes the challenges these devices face, such as the need for standardized development, data security and privacy protection, and cost-benefit analysis. This paper also proposes measures to address these challenges, including enhancing policy formulation, promoting standardization, and fostering technological innovation, with the aim of providing valuable insights for the advancement of high-quality clinical nursing practices.
The clinical electroencephalogram (EEG) monitoring systems based on personal computer system can not meet the requirements of portability and home usage. The epilepsy patients have to be monitored in hospital for an extended period of time, which imposes a heavy burden on hospitals. In the present study, we designed a portable 16-lead networked monitoring system based on the Android smart phone. The system uses some technologies including the active electrode, the WiFi wireless transmission, the multi-scale permutation entropy (MPE) algorithm, the back-propagation (BP) neural network algorithm, etc. Moreover, the software of Android mobile application can realize the processing and analysis of EEG data, the display of EEG waveform and the alarm of epileptic seizure. The system has been tested on the mobile phones with Android 2.3 operating system or higher version and the results showed that this software ran accurately and steadily in the detection of epileptic seizure. In conclusion, this paper provides a portable and reliable solution for epileptic seizure monitoring in clinical and home applications.
ObjectiveTo explore the reliability and safety of continuous monitoring of vital signs in patients using wireless wearable monitoring devices after video-assisted thoracoscopic surgery (VATS) for lung cancer. MethodsThe patients undergoing VATS for lung cancer in West China Hospital, Sichuan University from May to August 2023 were prospectively enrolled. Both wireless wearable and traditional wired devices were used to monitor the vital signs of patients after surgery. Spearman correlation analysis, paired sample t test and ratio Bland-Altman method were used to test the correlation, difference and consistency of monitoring data measured by the two devices. The effective monitoring rate of the wireless wearable device within 12 hours was calculated to test the reliability of its continuous monitoring. ResultsA total of 20 patients were enrolled, including 15 females and 5 males with an average age of 46.20±11.52 years. Data collected by the two monitoring devices were significantly correlated (P<0.001). Respiratory rate and blood oxygen saturation data collected by the two devices showed no statistical difference (P>0.05), while heart rate measured by wireless wearable device was slightly lower (\begin{document}$ \bar{d} $\end{document}=?0.307±1.073, P<0.001), and the blood pressure (\begin{document}$ \bar{d} $\end{document}=1.259±5.354, P<0.001) and body temperature(\begin{document}$ \bar{d} $\end{document}=0.115±0.231, P<0.001) were slightly higher. The mean ratios of heart rate, respiratory rate, blood oxygen saturation, blood pressure and body temperature collected by the two devices were 0.996, 1.004, 1.000, 1.014, and 1.003, respectively. The 95% limits of agreement (LoA) and 95% confidence interval of 95%LoA of each indicator were within the clinically acceptable limit. The effective monitoring rate of each vital signs within 12 hours was above 98%. ConclusionThe wireless wearable device has a high accuracy and reliability for continuous monitoring vital signs of patients after VATS for lung cancer, which provides a security guarantee for subsequent large-scale clinical application and further research.
In order to address the problem of traditional dolphin adjuvant therapy such as high cost and its limitation in time and place, this paper introduces a three-dimensional virtual dolphin adjuvant therapy system based on virtual reality technology. By adopting Oculus wearable three-dimensional display, the system combined natural human-computer interaction based on Leap Motion with high-precision gesture recognition and cognitive training, and achieved immersive three-dimensional interactive game for child rehabilitation training purposes. The experimental data showed that the system can effectively improve the cognitive and social abilities of those children with autism spectrum disorder, providing a useful exploration for the rehabilitation of those children.
With the rapid advancement of artificial intelligence (AI), its application in the rehabilitation of patients undergoing hip and knee arthroplasty has been increasingly emphasized. AI has the potential to enhance the precision and individualization of rehabilitation training, improve patient adherence, and optimize overall outcomes. This review summarizes the current progress of AI in postoperative rehabilitation following hip and knee arthroplasty, focusing on its roles in rehabilitation assessment, intelligent training, and remote rehabilitation. Furthermore, the advantages of AI in improving efficiency, accuracy, and patient engagement are highlighted, while existing challenges, including insufficient clinical evidence, high technological costs, and ethical concerns, are critically discussed. Finally, potential future directions, such as the integration of AI with virtual reality and wearable devices, are proposed. This review aims to provide valuable insights for clinical practice and future research in the rehabilitation of hip and knee arthroplasty.
As a novel technology, wearable physiological parameter monitoring technology represents the future of monitoring technology. However, there are still many problems in the application of this kind of technology. In this paper, a pilot study was conducted to evaluate the quality of electrocardiogram (ECG) signals of the wearable physiological monitoring system (SensEcho-5B). Firstly, an evaluation algorithm of ECG signal quality was developed based on template matching method, which was used for automatic and quantitative evaluation of ECG signals. The algorithm performance was tested on a randomly selected 100 h dataset of ECG signals from 100 subjects (15 healthy subjects and 85 patients with cardiovascular diseases). On this basis, 24-hour ECG data of 30 subjects (7 healthy subjects and 23 patients with cardiovascular diseases) were collected synchronously by SensEcho-5B and ECG Holter. The evaluation algorithm was used to evaluate the quality of ECG signals recorded synchronously by the two systems. Algorithm validation results: sensitivity was 100%, specificity was 99.51%, and accuracy was 99.99%. Results of controlled test of 30 subjects: the median (Q1, Q3) of ECG signal detected by SensEcho-5B with poor signal quality time was 8.93 (0.84, 32.53) minutes, and the median (Q1, Q3) of ECG signal detected by Holter with poor signal quality time was 14.75 (4.39, 35.98) minutes (Rank sum test, P=0.133). The results show that the ECG signal quality algorithm proposed in this paper can effectively evaluate the ECG signal quality of the wearable physiological monitoring system. Compared with signal measured by Holter, the ECG signal measured by SensEcho-5B has the same ECG signal quality. Follow-up studies will further collect physiological data of large samples in real clinical environment, analyze and evaluate the quality of ECG signals, so as to continuously optimize the performance of the monitoring system.
Objective To review the research progress of intelligent remote follow-up modes in the application after hip and knee arthroplasty. Methods Extensive literature on this topic published in recent years both domestically and internationally was reviewed, and the application of intelligent remote follow-up modes after hip and knee arthroplasty was summarized and analyzed. Results The intelligent remote follow-up mode is a novel follow-up method based on network information technology. Patients who undergo hip and knee arthroplasty require long-term follow-up and rehabilitation guidance after operation. Traditional outpatient follow-up is relatively time-consuming and inconvenient for some patients in terms of travel and transportation, which makes the application of intelligent remote follow-up modes increasingly widespread worldwide. The inherent attributes of remote interaction and instant feedback of this mode make it particularly valued in the field of hip and knee arthroplasty. Artificial intelligence (AI)-based voice follow-up systems and virtual clinics have significant advantages in improving follow-up efficiency, reducing human resource costs, and enhancing patient satisfaction. Conclusion The existing intelligent follow-up system has formed a standardized protocol in remote follow-up and rehabilitation guidance. However, there are still shortcomings in the formulation of personalized rehabilitation plans and the gerontechnological adaptation of human-computer interaction. In the future, it is necessary to construct a multimodal data fusion platform and establish technical application guidelines for different rehabilitation stages.
Wearable devices bring us an innovative human-computer interaction which plays an irreplaceable role in enhancing the users’ ability in environmental awareness, acquirements of their own state and “ubiquitous” computing power. Since 2013, wearable devices have quickly appeared around us. In this article we classify most of the wearable devices which have been appeared in the markets or reported in the literature according to their functions and the positions where they are worn. Furthermore, we review the technologies related to wearable devices, such as sensing technology, wireless communication, power manager, display technology and big data. At last, we analyze the challenges which the wearable devices will face in near future, and look forward to development trends of wearable 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.