Smart wearable devices play an increasingly important role in physiological monitoring and disease prevention because they are portable, real-time, dynamic and continuous.The popularization of smart wearable devices among people under high-altitude environment would be beneficial for the prevention for heart and brain diseases related to high altitude. The current review comprehensively elucidates the effects of high-altitude environment on the heart and brain of different population and experimental subjects, the characteristics and applications of different types of wearable devices, and the limitations and challenges for their application. By emphasizing their application values, this review provides practical reference information for the prevention of high-altitude disease and the protection of life and health.
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.
Breathing pattern parameters refer to the characteristic pattern parameters of respiratory movements, including the breathing amplitude and cycle, chest and abdomen contribution, coordination, etc. It is of great importance to analyze the breathing pattern parameters quantificationally when exploring the pathophysiological variations of breathing and providing instructions on pulmonary rehabilitation training. Our study provided detailed method to quantify breathing pattern parameters including respiratory rate, inspiratory time, expiratory time, inspiratory time proportion, tidal volume, chest respiratory contribution ratio, thoracoabdominal phase difference and peak inspiratory flow. We also brought in “respiratory signal quality index” to deal with the quality evaluation and quantification analysis of long-term thoracic-abdominal respiratory movement signal recorded, and proposed the way of analyzing the variance of breathing pattern parameters. On this basis, we collected chest and abdomen respiratory movement signals in 23 chronic obstructive pulmonary disease (COPD) patients and 22 normal pulmonary function subjects under spontaneous state in a 15 minute-interval using portable cardio-pulmonary monitoring system. We then quantified subjects’ breathing pattern parameters and variability. The results showed great difference between the COPD patients and the controls in terms of respiratory rate, inspiratory time, expiratory time, thoracoabdominal phase difference and peak inspiratory flow. COPD patients also showed greater variance of breathing pattern parameters than the controls, and unsynchronized thoracic-abdominal movements were even observed among several patients. Therefore, the quantification and analyzing method of breathing pattern parameters based on the portable cardiopulmonary parameters monitoring system might assist the diagnosis and assessment of respiratory system diseases and hopefully provide new parameters and indexes for monitoring the physical status of patients with cardiopulmonary disease.
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.
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 devices are used in the new design of the maternal health care system to detect electrocardiogram and oxygen saturation signal while smart terminals are used to achieve assessments and input maternal clinical information. All the results combined with biochemical analysis from hospital are uploaded to cloud server by mobile Internet. Machine learning algorithms are used for data mining of all information of subjects. This system can achieve the assessment and care of maternal physical health as well as mental health. Moreover, the system can send the results and health guidance to smart terminals.
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.
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.
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.
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.