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.
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.
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.
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.
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.
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.
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.
To achieve continuously physiological monitoring on hospital inpatients, a ubiquitous and wearable physiological monitoring system SensEcho was developed. The whole system consists of three parts: a wearable physiological monitoring unit, a wireless network and communication unit and a central monitoring system. The wearable physiological monitoring unit is an elastic shirt with respiratory inductive plethysmography sensor and textile electrocardiogram (ECG) electrodes embedded in, to collect physiological signals of ECG, respiration and posture/activity continuously and ubiquitously. The wireless network and communication unit is based on WiFi networking technology to transmit data from each physiological monitoring unit to the central monitoring system. A protocol of multiple data re-transmission and data integrity verification was implemented to reduce packet dropouts during the wireless communication. The central monitoring system displays data collected by the wearable system from each inpatient and monitors the status of each patient. An architecture of data server and algorithm server was established, supporting further data mining and analysis for big medical data. The performance of the whole system was validated. Three kinds of tests were conducted: validation of physiological monitoring algorithms, reliability of the monitoring system on volunteers, and reliability of data transmission. The results show that the whole system can achieve good performance in both physiological monitoring and wireless data transmission. The application of this system in clinical settings has the potential to establish a new model for individualized hospital inpatients monitoring, and provide more precision medicine to the patients with information derived from the continuously collected physiological parameters.
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.