Sleep-related breathing disorder (SRBD) is a sleep disease with high incidence and many complications. However, patients are often unaware of their sickness. Therefore, SRBD harms health seriously. At present, home SRBD monitoring equipment is a popular research topic to help people get aware of their health conditions. This article fully compares recent state-of-art research results about home SRBD monitors to clarify the advantages and limitations of various sensing techniques. Furthermore, the direction of future research and commercialization is pointed out. According to the system design, novel home SRBD monitors can be divided into two types: wearable and unconstrained. The two types of monitors have their own advantages and disadvantages. The wearable devices are simple and portable, but they are not comfortable and durable enough. Meanwhile, the unconstrained devices are more unobtrusive and comfortable, but the supporting algorithms are complex to develop. At present, researches are mainly focused on system design and performance evaluation, while high performance algorithm and large-scale clinical trial need further research. This article can help researchers understand state-of-art research progresses on SRBD monitoring quickly and comprehensively and inspire their research and innovation ideas. Additionally, this article also summarizes the existing commercial sleep respiratory monitors, so as to promote the commercialization of novel home SRBD monitors that are still under research.
The aging population and the increasing prevalence of chronic diseases in the elderly have brought a significant economic burden to families and society. The non-invasive wearable sensing system can continuously and real-time monitor important physiological signs of the human body and evaluate health status. In addition, it can provide efficient and convenient information feedback, thereby reducing the health risks caused by chronic diseases in the elderly. A wearable system for detecting physiological and behavioral signals was developed in this study. We explored the design of flexible wearable sensing technology and its application in sensing systems. The wearable system included smart hats, smart clothes, smart gloves, and smart insoles, achieving long-term continuous monitoring of physiological and motion signals. The performance of the system was verified, and the new sensing system was compared with commercial equipment. The evaluation results demonstrated that the proposed system presented a comparable performance with the existing system. In summary, the proposed flexible sensor system provides an accurate, detachable, expandable, user-friendly and comfortable solution for physiological and motion signal monitoring. It is expected to be used in remote healthcare monitoring and provide personalized information monitoring, disease prediction, and diagnosis for doctors/patients.
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.
The dynamic electrocardiogram (ECG) collected by wearable devices is often corrupted by motion interference due to human activities. The frequency of the interference and the frequency of the ECG signal overlap with each other, which distorts and deforms the ECG signal, and then affects the accuracy of heart rate detection. In this paper, a heart rate detection method that using coarse graining technique was proposed. First, the ECG signal was preprocessed to remove the baseline drift and the high-frequency interference. Second, the motion-related high amplitude interference exceeding the preset threshold was suppressed by signal compression method. Third, the signal was coarse-grained by adaptive peak dilation and waveform reconstruction. Heart rate was calculated based on the frequency spectrum obtained from fast Fourier transformation. The performance of the method was compared with a wavelet transform based QRS feature extraction algorithm using ECG collected from 30 volunteers at rest and in different motion states. The results showed that the correlation coefficient between the calculated heart rate and the standard heart rate was 0.999, which was higher than the result of the wavelet transform method (r = 0.971). The accuracy of the proposed method was significantly higher than the wavelet transform method in all states, including resting (99.95% vs. 99.14%, P < 0.01), walking (100% vs. 97.26%, P < 0.01) and running (100% vs. 90.89%, P < 0.01). The absolute error [0 (0, 1) vs. 1 (0, 1), P < 0.05] and relative error [0 (0, 0.59) vs. 0.52 (0, 0.72), P < 0.05] of the proposed method were significantly lower than the wavelet transform method during running state. The method presented in this paper shows high accuracy and strong anti-interference ability, and is potentially used in wearable devices to realize real-time continuous heart rate monitoring in daily activities and exercise conditions.
Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors’ laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.
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.
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.
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.
The gait acquisition system can be used for gait analysis. The traditional wearable gait acquisition system will lead to large errors in gait parameters due to different wearing positions of sensors. The gait acquisition system based on marker method is expensive and needs to be used by combining with the force measurement system under the guidance of rehabilitation doctors. Due to the complex operation, it is inconvenient for clinical application. In this paper, a gait signal acquisition system that combines foot pressure detection and Azure Kinect system is designed. Fifteen subjects are organized to participate in gait test, and relevant data are collected. The calculation method of gait spatiotemporal parameters and joint angle parameters is proposed, and the consistency analysis and error analysis of the gait parameters of proposed system and camera marking method are carried out. The results show that the parameters obtained by the two systems have good consistency (Pearson correlation coefficient r ≥ 0.9, P < 0.05) and have small error (root mean square error of gait parameters is less than 0.1, root mean square error of joint angle parameters is less than 6). In conclusion, the gait acquisition system and its parameter extraction method proposed in this paper can provide reliable data acquisition results as a theoretical basis for gait feature analysis in clinical medicine.
