Self-powered wearable piezoelectric sensing devices demand flexibility and high voltage electrical properties to meet personalized health and safety management needs. Aiming at the characteristics of piezoceramics with high piezoelectricity and low flexibility, this study designs a high-performance piezoelectric sensor based on multi-phase barium titanate (BTO) flexible piezoceramic film, namely multi-phase BTO sensor. The substrate-less self-supported multi-phase BTO films had excellent flexibility and could be bent 180° at a thickness of 33 μm, and exhibited good bending fatigue resistance in 1 × 104 bending cycles at a thickness of 5 μm. The prepared multi-phase BTO sensor could maintain good piezoelectric stability after 1.2 × 104 piezoelectric cycle tests. Based on the flexibility, high piezoelectricity, wearability, portability and battery-free self-powered characteristics of this sensor, the developed smart mask could monitor the respiratory signals of different frequencies and amplitudes in real time. In addition, by mounting the sensor on the hand or shoulder, different gestures and arm movements could also be detected. In summary, the multi-phase BTO sensor developed in this paper is expected to develop convenient and efficient wearable sensing devices for physiological health and behavioral activity monitoring applications.
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.
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 goal of this paper is to solve the problems of large volume, slow dynamic response and poor intelligent controllability of traditional gait rehabilitation training equipment by using the characteristic that the shear yield strength of magnetorheological fluid changes with the applied magnetic field strength. Based on the extended Bingham model, the main structural parameters of the magnetorheological fluid damper and its output force were simulated and optimized by using scientific computing software, and the three-dimensional modeling of the damper was carried out after the size was determined. On this basis and according to the design and use requirements of the damper, the finite element analysis software was used for force analysis, strength check and topology optimization of the main force components. Finally, a micro magnetorheological fluid damper suitable for wearable rehabilitation training system was designed, which has reference value for the design of lightweight, portable and intelligent rehabilitation training equipment.
Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.
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.
Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system’s performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.
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.
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.
This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis—dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.