Considering the importance of the human respiratory signal detection and based on the Cole-Cole bio-impedance model, we developed a wearable device for detecting human respiratory signal. The device can be used to analyze the impedance characteristics of human body at different frequencies based on the bio-impedance theory. The device is also based on the method of proportion measurement to design a high signal to noise ratio (SNR) circuit to get human respiratory signal. In order to obtain the waveform of the respiratory signal and the value of the respiration rate, we used the techniques of discrete Fourier transform (DFT) and dynamic difference threshold peak detection. Experiments showed that this system was valid, and we could see that it could accurately detect the waveform of respiration and the detection accuracy rate of respiratory wave peak point detection results was over 98%. So it can meet the needs of the actual breath test.
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.
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.
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.
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.
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.
Motor dysfunction is the main clinical symptom and diagnosis basis of patients with Parkinson’s disease (PD). A total of 30 subjects were recruited in this study, including 15 PD patients (PD group) and 15 healthy subjects (control group). Then 5 wearable inertial sensor nodes were worn on the bilateral upper limbs, lower limbs and waist of subjects. When completing the 6 paradigm tasks, the acceleration and angular velocity signals from different parts of the body were acquired and analyzed to obtain 20 quantitative parameters which contain information about the amplitude, frequency, and fatigue degree of movements to assess the motor function. The clinical data of the two groups were statistically analyzed and compared, and then Back Propagation (BP) Neural Network was used to classify the two groups and predict the clinical score. The final results showed that most of the parameters had significant difference between the two groups, ten times of 5-fold cross validation showed that the classification accuracy of the BP Neural Network for the two groups was 90%, and the predictive accuracy of Hoehn-Yahr (H-Y) staging and unified PD rating scale (UPDRS) Ⅲ score of the patients were 72.80% and 68.64%, respectively. This study shows the feasibility of quantitative assessment of motor function in PD patients using wearable sensors, and the quantitative parameters obtained in this paper may have reference value for future related 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.
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.