Hybrid triboelectric-piezoelectric nanogenerator for long-term load monitoring in total knee replacements

A self-powered and durable pressure sensor for large-scale pressure detection on the knee implant would be highly advantageous for designing long-lasting and reliable knee implants as well as obtaining information about knee function after the operation. The purpose of this study is to develop a robust energy harvester that can convert wide ranges of pressure to electricity to power a load sensor inside the knee implant. To efficiently convert loads to electricity, we design a cuboid-array-structured tribo-pizoelectric nanogenerator (TPENG) in vertical contact mode inside a knee implant package. The proposed TPENG is fabricated with aluminum and cuboid-patterned silicone rubber layers. Using the cuboid-patterned silicone rubber as a dielectric and aluminum as electrodes improves performance compared with previously reported self-powered sensors. The combination of 10w t % dopamine-modified BaTiO3 piezoelectric nanoparticles in the silicone rubber enhanced electrical stability and mechanical durability of the silicone rubber. To examine the output, the package-harvester assemblies are loaded into an MTS machine under different periodic loading. Under different cyclic loading, frequencies, and resistance loads, the harvester's output performance is also theoretically studied and experimentally verified. The proposed cuboid-array-structured TPENG integrated into the knee implant package can generate approximately 15μ W of apparent power under dynamic compressive loading of 2200 N magnitude. In addition, as a result of the TPENG's materials being effectively optimized, it possesses remarkable mechanical durability and signal stability, functioning after more than 30 000 cycles under 2200 N load and producing about 300 V peak to peak. We have also presented a mathematical model and numerical results that closely capture experimental results. We have reported how the TPENG charge density varies with force. This study represents a significant advancement in a better understanding of harvesting mechanical energy for instrumented knee implants to detect a load imbalance or abnormal gait patterns.

A Miniature Biomedical Sensor for Rapid Detection of Schistosoma japonicum Antibodies

Schistosomiasis, typically characterized by chronic infection in endemic regions, has the potential to affect liver tissue and pose a serious threat to human health. Detecting and screening for this disease early on is crucial for its prevention and control. However, existing methods encounter challenges such as low sensitivity, time-consuming processes, and complex sample handling. To address these challenges, we report a soluble egg antigen (SEA)-based functionalized gridless and meander-type AlGaN/GaN high electron mobility transistors (HEMT) sensor for the highly sensitive detection of antibodies to Schistosoma japonicum. Immobilization of the self-assembled membrane on the gate surface was verified using a semiconductor parameter analyzer, scanning electron microscope (SEM), and atomic force microscopy (AFM). The developed biosensor demonstrates remarkable performance in detecting anti-SEA, exhibiting a linear concentration range of 10 ng/mL to 100 μg/mL and a sensitivity of 0.058 mA/log (ng/mL). It also exhibits similar excellent performance in serum systems. With advantages such as rapid detection, high sensitivity, miniaturization, and label-free operation, this biosensor can fulfill the requirements for blood defense.

High-Efficiency Large-Area Printed Multilayer Liquid Metal Wires for Stretchable Biomedical Sensors with Recyclability

Stretchable conductors are essential for soft robots, wearable on-skin electronic technologies, and bioelectronics. The utilization of sophisticated stretchable conductors requires a new, simple, rapid, and large-scale printing process whose features include high stretchability, high precision, multilayers, and recyclability simultaneously for commercial wearable electronics. To address this need, an LM (liquid metal) wire was developed using a simple, rapid, and large-scale soft stamper-based printing process and employed to realize LM wire-based conductors and capacitors, which simultaneously offer high stretchability (>380%), high precision past 50 μm, and electromechanical response stability after stretching for up to an hour. Based on the excellent electromechanical responses, the LM wire-based capacitors, as strain sensors, attached to finger joints resulted in precise gesture detection. Meanwhile, a simple transparent wearable e-skin consisting of a 6 × 6 LM wire-based capacitor array without rigid parts successfully monitored a multi-point touch. At last, a portable noninvasive stretchable multilayer LM wire-based pulse sensor with recyclability is fabricated to monitor the patient's heartbeats. The experimental results reveal that the stretchable biomedical sensors have the potential to help patients to improve their life in healthcare, including replacement prosthetic devices, daily and sports activity tracking, continuous health monitoring, and others.

The Effect of Physiological Incubation on the Properties of Elastic Magnetic Composites for Soft Biomedical Sensors

Magnetic micro- and nanoparticles (MPs)-based composite materials are widely used in various applications in electronics, biotechnology, and medicine. This group of silicone composites have advantageous magnetic and mechanical properties as well as sufficient flexibility and biocompatibility. These composites can be applied in medicine for biological sensing, drug delivery, tissue engineering, and as remote-controlled microrobots operating in vivo. In this work, the properties of polydimethylsiloxane (PDMS)-based composites with different percentages (30 wt.%, 50 wt.%, 70 wt.%) of NdFeB microparticles as a filler were characterized. The novelty of the work was to determine the influence of the percentage of MP content and physiological conditioning on the properties of the PDMS-MP composites after in vitro incubation. An important essence of the work was a comprehensive study of the properties of materials important from the point of view of medical applications. Materials were tested before and after conditioning in 0.9 wt.% NaCl solution at a temperature of 37 °C. Several studies were carried out, including thermal, physicochemical, and rheological tests. The results show that with an increase of the incubation time, most of the measured thermal and physicochemical parameters decreased. The presence of the magnetic filler, especially at a concentration of 70 wt.%, has a positive effect on thermal stability and physicochemical and rheological properties. The performed tests provided important results, which can lead to further research for a broader application of magnetic composites in the biomedical field.

Effect of Dielectric Material and Package Stiffness on the Power Generation in a Packaged Triboelectric Energy Harvesting System for Total Knee Replacement

The objectives of this study are to experimentally investigate the effects of the dielectric material and the package stiffness on the durability and the efficiency of a previously developed triboelectric-based instrumented knee implant prototype. The proposed smart knee implant may provide useful information about prosthesis health and its functionality after a total knee replacement (TKR) by routine monitoring of tibiofemoral load transfer without the need for any external power source. The triboelectric powered load sensing by the proposed TKR system needs to be functional throughout the entire life of a knee replacement. The power output of the triboelectric system depends on the surface charge generations and accumulations on its dielectric material, and the force that transmits through its housing into the tribo-materials. The properties of the dielectric material and the package stiffness can significantly influence the reliability of the proposed device. For such a TKR system, a compliant mechanism with the ideal material selection can improve its state of the art. We investigated the performance of three vertical contact mode triboelectric generators made with three different dielectric materials: polydimethylsiloxane (PDMS), fluorinated ethylene propylene (FEP), and polytetrafluoroethylene (PTFE). To investigate the effect of package stiffness, we tested two Ti-PDMS-Ti harvesters inside a polyethylene and a Ti6Al4V package. At 1500 N of sinusoidal loads, the harvesters could generate 67.73 μW and 19.81 μW of mean apparent power in parallel and single connections in the polyethylene package, which was 32 and 17 times greater than the power recorded in the Ti assembly, respectively.

Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.

Development of a Smart Splint to Monitor Different Parameters during the Treatment Process

For certain musculoskeletal complex rupture injuries, the only treatment available is the use of immobilization splints. This type of treatment usually causes discomfort and certain setbacks in patients. In addition, other complications are usually generated at the vascular, muscular, or articular level. Currently, there is a really possible alternative that would solve these problems and even allows a faster and better recovery. This is possible thanks to the application of engineering on additive manufacturing techniques and the use of biocompatible materials available in the market. This study proposes the use of these materials and techniques, including sensor integration inside the splints. The main parameters considered to be studied are pressure, humidity, and temperature. These aspects are combined and analyzed to determine any kind of unexpected evolution of the treatment. This way, it will be possible to monitor some signals that would be studied to detect problems that are associated to the very initial stage of the treatment. The goal of this study is to generate a smart splint by using biomaterials and engineering techniques based on the advanced manufacturing and sensor system, for clinical purposes. The results show that the prototype of the smart splint allows to get data when it is placed over the arm of a patient. Two temperatures are read during the treatment: in contact with the skin and between skin and splint. The humidity variations due to sweat inside the splint are also read by a humidity sensor. A pressure sensor detects slight changes of pressure inside the splint. In addition, an infrared sensor has been included as a presence detector.

Feasibility of a Wearable Reflectometric System for Sensing Skin Hydration

One of the major goals of Health 4.0 is to offer personalized care to patients, also through real-time, remote monitoring of their biomedical parameters. In this regard, wearable monitoring systems are crucial to deliver continuous appropriate care. For some biomedical parameters, there are a number of well established systems that offer adequate solutions for real-time, continuous patient monitoring. On the other hand, monitoring skin hydration still remains a challenging task. The continuous monitoring of this physiological parameter is extremely important in several contexts, for example for athletes, sick people, workers in hostile environments or for the elderly. State-of-the-art systems, however, exhibit some limitations, especially related with the possibility of continuous, real-time monitoring. Starting from these considerations, in this work, the feasibility of an innovative time-domain reflectometry (TDR)-based wearable, skin hydration sensing system for real-time, continuous monitoring of skin hydration level was investigated. The applicability of the proposed system was demonstrated, first, through experimental tests on reference substances, then, directly on human skin. The obtained results demonstrate the TDR technique and the proposed system holds unexplored potential for the aforementioned purposes.

Toward Impedimetric Measurement of Acidosis with a pH-Responsive Hydrogel Sensor

A pH-responsive, poly(2-hydroxyethyl methacrylate) [poly(HEMA)]-based hydrogel has been fashioned into an impedimetric pH sensor for the continual measurement and monitoring of tissue acidosis that can arise due to hemorrhaging trauma. Four hydrogel systems molecularly engineered to influence water distribution and ionic abundance were studied: a cationogenic primary amine, N-(2-aminoethyl) methacrylate (AEMA), a tertiary amine moiety, N,N-(2-dimethylamino)ethyl methacrylate (DMAEMA), and a combined AEMA-DMAEMA formulation. Electrochemical impedance spectroscopy (EIS) of hydrogel discs held between platinized Type 304 stainless steel mesh electrodes in pH-adjusted 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid sodium salt (HEPES) buffer and equivalent circuit modeling indicated that the AEMA hydrogel had the highest sensitivity containing the relevant pathophysiological range (pH 7.0-8.0). Thus, the AEMA formulation was studied at 0, 1, 3, 4.4, and 30 mol % AEMA. The 1 mol % AEMA was found to significantly (p < 0.05) discern nominal pH (7.35, 7.40, 7.45). The Taguchi Design of Experiments approach was employed and confirmed composition as a factor and 1 mol % AEMA to be the most robust. DMAEMA (0, 4.4, 14, 30 mol %) and AEMA-DMAEMA (0, 4.4, 14, 30 mol %) allowed the use of the one-factor Response Surface Methodology optimizer to confirm the AEMA 1 mol % system to be most robust, sensitive, and possessing optimal sensitivity in the pathophysiological pH sensing range (7.35-7.45) for hemorrhagic trauma. This composition was fashioned as a responsive membrane on a microlithographically fabricated interdigitated microsensor electrode and the sensitivity was determined using R(QR)(QR) analysis. Water distribution within the AEMA (0, 1, 4.4, 30 mol %), determined by gravimetric analysis and differential scanning calorimetry, revealed a strong anticorrelation between nonfreezable bound water and pH sensitivity (-0.82) and was in good agreement with the total hydration (-0.70). Nonfreezable bound water was found to be the most strongly correlated factor that governs the pH response of hydrogels.

Ultrathin, Biocompatible, and Flexible Pressure Sensor with a Wide Pressure Range and Its Biomedical Application

In this research, an ultrathin, biocompatible, and flexible pressure sensor with a wide pressure range has been developed and applied in biomedical applications. The pressure sensing mechanism is based on the variation of contact resistance between an electrode and a three-dimensional microstructured polyimide/carbon nanotube composite film. The sensor has a thickness of about 31.3 μm, a maximum sensitivity of 41.0 MPa^-1, and a sensing range of 10-500 kPa. Moreover, in situ temperature measurement by an integrated resistive temperature detector enables data correction for varying temperature conditions. In order to show the advantages of the fabricated sensor, it is attached to the human body and integrated with the surface of a radiofrequency ablation (RFA) needle with small radius of curvature. In the experiments, the proposed pressure sensor measured subtle pressure levels (pulse pressure) and high pressure levels (fingertip pressure) without losing conformal contact with the skin. In addition, when the pressure-sensor-integrated RFA needle was inserted into a bovine liver, successful detection of steam popping phenomenon was observed.

A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring.

Diaphragm-based optical fiber sensor for pulse wave monitoring and cardiovascular diseases diagnosis

Arterial pulse wave has been considered as a vital sign in assessment of cardiovascular diseases. Noninvasive pulse sensor with compact structure, immunity to electro-magnetic interference and high sensitivity is the research focus in recent years. While, optical fiber biosensor is a competitive option to meet these needs. Here, a diaphragm-based optical fiber pulse sensor was proposed to achieve high-precision radial pulse wave monitoring. A wearable device was developed, composed of a sports wristband and an aluminum diaphragm-based optical fiber sensor tip of only 1 cm in diameter, which was highly sensitive to the weak acoustic signal. In particular, coherent phase detection was adopted to improve detection signal-to-noise ratio, so as to recover the high-fidelity pulse waveforms. A clinical experiment was carried out to detect and morphological analyze the pulse waveforms of four subjects, the results of which preliminarily demonstrated the feasibility of pulse diagnosis method. The proposed pulse fiber sensor provides a comfortable way for pulse diagnosis, which is promising in early cardiovascular diseases indicating.

Neck sensor-supported hyoid bone movement tracking during swallowing

Hyoid bone movement is an important physiological event during swallowing that contributes to normal swallowing function. In order to determine the adequate hyoid bone movement, clinicians conduct an X-ray videofluoroscopic swallowing study, which even though it is the gold-standard technique, has limitations such as radiation exposure and cost. Here, we demonstrated the ability to track the hyoid bone movement using a non-invasive accelerometry sensor attached to the surface of the human neck. Specifically, deep neural networks were used to mathematically describe the relationship between hyoid bone movement and sensor signals. Training and validation of the system were conducted on a dataset of 400 swallows from 114 patients. Our experiments indicated the computer-aided hyoid bone movement prediction has a promising performance when compared with human experts' judgements, revealing that the universal pattern of the hyoid bone movement is acquirable by the highly nonlinear algorithm. Such a sensor-supported strategy offers an alternative and widely available method for online hyoid bone movement tracking without any radiation side-effects and provides a pronounced and flexible approach for identifying dysphagia and other swallowing disorders.