The real-time monitoring of cerebral hemorrhage can reduce its disability and fatality rates greatly. On the basis of magnetic induction phase shift, we in this study used filter and amplifier hardware module, NI-PXI data-acquisition system and LabVIEW software to set up an experiment system. We used Band-pass sample method and correlation phase demodulation algorithm in the system. In order to test and evaluate the performance of the system, we carried out saline simulation experiments of brain hemorrhage. We also carried out rabbit cerebral hemorrhage experiments. The results of both saline simulation and animal experiments suggested that our monitoring system had a high phase detection precision, and it needed only about 0.030 4s to finish a single phase shift measurement, and the change of phase shift was directly proportional to the volume of saline or blood. The experimental results were consistent with theory. As a result, this system has the ability of real-time monitoring the progression of cerebral hemorrhage precisely, with many distinguished features, such as low cost, high phase detection precision, high sensitivity of response so that it has showed a good application prospect.
ObjectiveTo develop a smart orthosis personalized management system for the treatment of patients with adolescent idiopathic scoliosis (AIS) and to evaluate the feasibility and efficiency through clinical preliminary applications.MethodsThe smart orthosis personalized management system consists of a wireless force monitor, a WeChat Mini Program, a cloud-based storage system, and a website backstage management system. Twenty-two patients with AIS who underwent orthosis treatment and met the selection criteria between March 2020 and December 2020 were enrolled. The follow-up time was 4 months. The parameters used to evaluate patients’ compliance were as follows (back and lumbar): baseline force value, measured force value, force compliance (measured force value/baseline force value×100%), measured wearing time (wearing time of force value was more than 0 N), and time compliance (measured wearing time/prescribed wearing time×100%), in which the prescribed wearing time was 23 hours/day. The baseline force values were measured at initiation, while the measured force value, measured wearing time, force compliance, and time compliance were measured during follow-up. The differences of these parameters between back and lumbar, and the differences among these parameters at 1, 2, 3, and 4 months after orthosis wearing were analyzed.ResultsThe average measured force value of 22 patients (back and lumbar) was (0.83±0.34) N, the average force compliance was 68.5%±17.9%, the average measured wearing time was (15.4±1.7) hours, and the average time compliance was 66.9%±7.7%. The baseline force value and measured force value of back were significantly higher than those of lumbar (P<0.05); the measured wearing time, force compliance, and time compliance between back and lumbar showed no significant difference (P>0.05). The measured force value, measured wearing time, force compliance, and time compliance at 1 month after wearing were significantly lower than those at 2, 3, and 4 months after orthosis wearing (P<0.05), no significant difference was found among 2, 3, and 4 months after orthosis wearing (P>0.05). At different time points after wearing, the measured force value of back were significantly higher than that of lumbar (P<0.05), while there was no significant difference between back and lumbar on the other parameters (P>0.05).ConclusionThe smart orthosis personalized management system has high feasibility to treat AIS, and can improve the compliance of such patients with orthosis wearing.
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.