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.
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.
In order to improve the accuracy of blood pressure measurement in wearable devices, this paper presents a method for detecting blood pressure based on multiple parameters of pulse wave. Based on regression analysis between blood pressure and the characteristic parameters of pulse wave, such as the pulse wave transit time (PWTT), cardiac output, coefficient of pulse wave, the average slope of the ascending branch, heart rate, etc. we established a model to calculate blood pressure. For overcoming the application deficiencies caused by measuring ECG in wearable device, such as replacing electrodes and ECG lead sets which are not convenient, we calculated the PWTT with heart sound as reference (PWTTPCG). We experimentally verified the detection of blood pressure based on PWTTPCG and based on multiple parameters of pulse wave. The experiment results showed that it was feasible to calculate the PWTT from PWTTPCG. The mean measurement error of the systolic and diastolic blood pressure calculated by the model based on multiple parameters of pulse wave is 1.62 mm Hg and 1.12 mm Hg, increased by 57% and 53% compared to those of the model based on simple parameter. This method has more measurement accuracy.
Objective To investigate the awareness and clinical needs of wearable artificial kidney among maintenance hemodialysis (MHD) patients, and to analyze the related influencing factors. Methods MHD patients were recruited from 2 tertiary hospitals in Sichuan province between April and June 2021. The convenient sampling method was used to select patients. The factors influencing the awareness and demand of MHD patients for wearable artificial kidney were analyzed. Results A total of 119 MHD patients were included. The awareness of wearable artificial kidney among the patients was mainly “never heard” (61 cases) and “heard” (58 cases). Most MHD patients (60 cases) were willing to use and participate in clinical trials in the future. The results of logistic regression indicated that the cost on household economy and treatment effect on life quality were the influencing factors for MHD patients’ awareness of wearable artificial kidney (P＜0.05). The average duration of single dialysis and the impact of treatment on working or studying were the influencing factors for MHD patients’ needs of wearable artificial kidney (P＜0.05). Conclusions The awareness of wearable artificial kidney is low among MHD patients. However, most MHD patients showed great interest in the wearable artificial kidney after preliminary understanding, suggesting that the future clinical application of wearable artificial kidney has great demand.
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.
According to the development status of wearable technology and the demand of intelligent health monitoring, we studied the multi-function integrated smart watches solution and its key technology. First of all, the sensor technology with high integration density, Bluetooth low energy (BLE) and mobile communication technology were integrated and used in develop practice. Secondly, for the hardware design of the system in this paper, we chose the scheme with high integration density and cost-effective computer modules and chips. Thirdly, we used real-time operating system FreeRTOS to develop the friendly graphical interface interacting with touch screen. At last, the high-performance application software which connected with BLE hardware wirelessly and synchronized data was developed based on android system. The function of this system included real-time calendar clock, telephone message, address book management, step-counting, heart rate and sleep quality monitoring and so on. Experiments showed that the collecting data accuracy of various sensors, system data transmission capacity, the overall power consumption satisfy the production standard. Moreover, the system run stably with low power consumption, which could realize intelligent health monitoring effectively.
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.
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.
Wearable physiological parameter monitoring devices play an increasingly important role in daily health monitoring and disease diagnosis/treatment due to their continuous dynamic and low physiological/psychological load characteristics. After decades of development, wearable technologies have gradually matured, and research has expanded to clinical applications. This paper reviews the research progress of wearable physiological parameter monitoring technology and its clinical applications. Firstly, it introduces wearable physiological monitoring technology’s research progress in terms of sensing technology and data processing and analysis. Then, it analyzes the monitoring physiological parameters and principles of current medical-grade wearable devices and proposes three specific directions of clinical application research: 1) real-time monitoring and predictive warning, 2) disease assessment and differential diagnosis, and 3) rehabilitation training and precision medicine. Finally, the challenges and response strategies of wearable physiological monitoring technology in the biomedical field are discussed, highlighting its clinical application value and clinical application mode to provide helpful reference information for the research of wearable technology-related fields.
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.