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.
Cardiovascular disease has caused a huge burden of disease worldwide, and the rapid advancement of smart wearable devices has provided new means for early diagnosis, real-time monitoring, and event prevention of cardiovascular disease. Smart wearable devices can be classified into various categories based on detection signals and physical carrier types. Based on an overview of the composition of such devices, this article further introduces the current cutting-edge research and related market products related to smart blood pressure monitoring, electrocardiogram monitoring, and ultrasound monitoring. It also discusses the future development and challenges of such devices, aiming to provide evidence support for the research and development of smart wearable devices in the diagnosis and treatment of cardiovascular diseases in the future.
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.
Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%–104.28)% vs. 58.48% (45.34%–65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.
Lower limb ankle exoskeletons have been used to improve walking efficiency and assist the elderly and patients with motor dysfunction in daily activities or rehabilitation training, while the assistance patterns may influence the wearer’s lower limb muscle activities and coordination patterns. In this paper, we aim to evaluate the effects of different ankle exoskeleton assistance patterns on wearer’s lower limb muscle activities and coordination patterns. A tethered ankle exoskeleton with nine assistance patterns that combined with differenet actuation timing values and torque magnitude levels was used to assist human walking. Lower limb muscle surface electromyography signals were collected from 7 participants walking on a treadmill at a speed of 1.25 m/s. Results showed that the soleus muscle activities were significantly reduced during assisted walking. In one assistance pattern with peak time in 49% of stride and peak torque at 0.7 N·m/kg, the soleus muscle activity was decreased by (38.5 ± 10.8)%. Compared with actuation timing, the assistance torque magnitude had a more significant influence on soleus muscle activity. In all assistance patterns, the eight lower limb muscle activities could be decomposed to five basic muscle synergies. The muscle synergies changed little under assistance with appropriate actuation timing and torque magnitude. Besides, co-contraction indexs of soleus and tibialis anterior, rectus femoris and semitendinosus under exoskeleton assistance were higher than normal walking. Our results are expected to help to understand how healthy wearers adjust their neuromuscular control mechanisms to adapt to different exoskeleton assistance patterns, and provide reference to select appropriate assistance to improve walking efficiency.
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 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.
ObjectiveWearable devices refer to a class of monitoring devices that can be tightly integrated with the human body and are designed to continuously monitor individual's activity without impeding or restricting the user's normal activities in the process. With the rapid advancement of chips, sensors, and artificial intelligence technologies, such devices have been widely used for patients with cardiovascular diseases who require continuous health monitoring. These patients require continuous monitoring of a number of physiological indicators to assess disease progression, treatment efficacy, and recovery in the early stages of the disease, during the treatment, and in the recovery period. Traditional monitoring methods require patients to see a doctor on a regular basis with the help of fixed devices and analysis by doctors, which not only increases the financial burden of patients, but also consumes medical resources and time. However, wearable devices can collect data in real time and transmit it directly to doctors via the network, thus providing an efficient and cost-effective monitoring solution for patients. In this paper, we will review the applications, advantages and challenges of wearable devices in the treatment of cardiovascular diseases, as well as the outlook for their future applications.
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.