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.
Cartilage surface fibrosis is an early sign of osteoarthritis and cartilage surface damage is closely related to load. The purpose of this study was to study the relationship between cartilage surface roughness and load. By applying impact, compression and fatigue loads on fresh porcine articular cartilage, the rough value of cartilage surface was measured at an interval of 10 min each time and the change rule of roughness before and after loading was obtained. It was found that the load increased the roughness of cartilage surface and the increased value was related to the load size. The time of roughness returning to the initial condition was related to the load type and the load size. The impact load had the greatest influence on the roughness of cartilage surface, followed by the severe fatigue load, compression load and mild fatigue load. This article provides reference data for revealing the pathogenesis of early osteoarthritis and preventing and treating articular cartilage diseases.
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.
Objective To observe expression of Caspase-3 and apoptosis around the prosthesis and explore the relationship of the expression and the apoptosis with the periimplant osteolysis. Methods From April 2001 to August 2006, 16 patients (10 males, 6 females) underwent the revision total hip arthroplasty surgery, who had the primary total hip arthroplasty at the ages of 45-67 years and had the revision total hip arthroplasty at the ages of 55-78 years, with the implantation duration of 7-13 years. According to their preoperative X-ray films andthe findings during the operation, the patients were divided into two groups: theloose/osteolytic group (n=8) and the loose/non-osteolytic group (n=8). The interface tissues were obtained from the peri-implant region in the patients. The synovial samples were taken from another 6 patients (2 males, 4 females; age, 54-68years; illness course, 9-15 years), who underwent the primary total hip arthroplasty for osteoarthritis. These 6 patients were used as controls. The tissues were prepared for the immunohistochemical assays to determine the expression of Caspase-3. The TUNEL assays were performed to quantify the apoptotic cells. The quantitative analysis on the positive cells and the correlation with the presence of the particulate wear debris and the severity of osteolysis were also performed. Results The level of the expression for Caspase-3 and the apoptosis index inthe loose/osteolytic group were significantly increased when compared with those in the loose/non-osteolytic group and the control group (P＜0.01). The polyethylene particles were surrounded by more positive cells than the metal particles. The positive cells were present at a higher level in the tissue sections where the high-wear status was present when compared with the areas where the low-wear status was present (P＜0.05). Conclusion There is a statistical correlation of the Caspase-3 expression to the apoptosis index and to the presence of the particulate wear debris and the severity of osteolysis, which may be one of the key points for the bone reconstruction inhibition and the bone resorption at the boneimplant interface under the stimulation of the wear debris. The apoptosis is involved in the pathogenesis of the aseptic loosening, which is closely related to the signal transportation of Caspase-3.
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 performance and failure issues are significantly influenced by prosthetic malposition in unicompartmental knee arthroplasty (UKA). Uncertainty exists about the impact of the prosthetic joint line height in UKA on tibial insert wear. In this study, we combined the UKA musculoskeletal multibody dynamics model, finite element model and wear model to investigate the effects of seven joint line height cases of fixed UKA implant on postoperative insert contact mechanics, cumulative sliding distance, linear wear depth and volumetric wear. As the elevation of the joint line height in UKA, the medial contact force and the joint anterior-posterior translation during swing phase were increased, and further the maximum von Mises stress, contact stress, linear wear depth, cumulative sliding distance, and the volumetric wear also were increased. Furthermore, the wear area of the insert gradually shifted from the middle region to the rear. Compared to 0 mm joint line height, the maximum linear wear depth and volumetric wear were decreased by 7.9% and 6.8% at –2 mm joint line height, and by 23.7% and 20.6% at –6 mm joint line height, the maximum linear wear depth and volumetric wear increased by 10.7% and 5.9% at +2 mm joint line height, and by 24.1% and 35.7% at +6 mm joint line height, respectively. UKA prosthetic joint line installation errors can significantly affect the wear life of the polyethylene inserted articular surfaces. Therefore, it is conservatively recommended that clinicians limit intraoperative UKA joint line height errors to –2−+2 mm.
The gait acquisition system can be used for gait analysis. The traditional wearable gait acquisition system will lead to large errors in gait parameters due to different wearing positions of sensors. The gait acquisition system based on marker method is expensive and needs to be used by combining with the force measurement system under the guidance of rehabilitation doctors. Due to the complex operation, it is inconvenient for clinical application. In this paper, a gait signal acquisition system that combines foot pressure detection and Azure Kinect system is designed. Fifteen subjects are organized to participate in gait test, and relevant data are collected. The calculation method of gait spatiotemporal parameters and joint angle parameters is proposed, and the consistency analysis and error analysis of the gait parameters of proposed system and camera marking method are carried out. The results show that the parameters obtained by the two systems have good consistency (Pearson correlation coefficient r ≥ 0.9, P < 0.05) and have small error (root mean square error of gait parameters is less than 0.1, root mean square error of joint angle parameters is less than 6). In conclusion, the gait acquisition system and its parameter extraction method proposed in this paper can provide reliable data acquisition results as a theoretical basis for gait feature analysis in clinical medicine.
Wearable devices, as an important component of digital health, are gradually penetrating into the clinical nursing field. This paper explores the current applications of wearable devices in the field of clinical nursing, with a focus on their significant roles in real-time monitoring of physiological parameters, disease management, functional rehabilitation exercises. Additionally, it analyzes the challenges these devices face, such as the need for standardized development, data security and privacy protection, and cost-benefit analysis. This paper also proposes measures to address these challenges, including enhancing policy formulation, promoting standardization, and fostering technological innovation, with the aim of providing valuable insights for the advancement of high-quality clinical nursing practices.
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.
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.