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Research on soft artificial muscles (SAMs) is rapidly growing, both in developing new actuation ideas and improving existing structures with multifunctionality. The human body has more than 600 muscles that drive organs and joints to achieve desired functions. Inspired by the human muscles, this article presents a new type of SAM fiber formed from twisting and braiding soft hydraulic filament artificial muscles with high aspect ratio, high strain, and high energy efficiency. We systematically investigated the relationship between input pressure and output elongation as well as contraction force of the new muscles using different configurations in terms of an array of single and multiple muscles arranged in nontwisting (or straight), twisting, and braiding variants. Experimental results revealed that the twisting and braiding configurations greatly enhanced the muscle elongation and generated force compared with their nontwisting/braiding counterparts. To demonstrate the new muscles' usability, we implemented several muscle variants to bidirectionally manipulate 3D-printed human fingers and elbow, mimicking the human upper limb with a full range of motion. We also created a bioinspired growing soft tubular muscle that could simultaneously exert longitudinal and radial expansion upon pressurization, similar to that of auxetic metamaterial structures. The new growing soft tubular muscles were experimentally validated and the results showed that they could be potentially implemented in several emerging applications, including smart compression garments, stent-like supporting devices, and tubular grippers for medical use.

Summary Facial nerve paralysis (FNP) is a debilitating condition that leaves those affected with disfigurement and loss of function. The most important function of the facial nerve is protecting the eye through eye closure and blinking. A series of reanimation techniques have been reported to restore dynamic function in FNP, but the lack of a universally accepted method that is reliable and reproducible with immediate effect has led to the introduction of several implantable devices. Most of these devices have been applied to assist blinking; however, the delicate anatomy and unique mechanics of eye closure are difficult to replicate. Lid loading is the most frequently used implant today, which is a passive device that can aid in volitional eye closure but has a limited effect on blinking. Dynamic action can be achieved with active prostheses but achieving successful long-term function remains elusive. Device action must also be coupled with a real-time feedback mechanism in order to capture the natural variation in facial muscle movements. This review discusses all prostheses used for restoring eye closure and blinking to date and explores their relative merits.

Heart failure (HF) is one of the most prevalent life-threatening cardiovascular diseases in which 6.5 million people are suffering in the USA and more than 23 million worldwide. Mechanical circulatory support of HF patients can be achieved by implanting a left ventricular assist device (LVAD) into HF patients as a bridge to transplant, recovery or destination therapy and can be controlled by measurement of normal and abnormal pulmonary arterial wedge pressure (PAWP). While there are no commercial long-term implantable pressure sensors to measure PAWP, real-time non-invasive estimation of abnormal and normal PAWP becomes vital. In this work, first an improved Harris Hawks optimizer algorithm called HHO+ is presented and tested on 24 unimodal and multimodal benchmark functions. Second, a novel fully Elman neural network (FENN) is proposed to improve the classification performance. Finally, four novel 18-layer deep learning methods of convolutional neural networks (CNNs) with multi-layer perceptron (CNN-MLP), CNN with Elman neural networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully Elman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for classification of abnormal and normal PAWP using estimated HVAD pump flow were developed and compared. The estimated pump flow was derived by a non-invasive method embedded into the commercial HVAD controller. The proposed methods are evaluated on an imbalanced clinical dataset using 5-fold cross-validation. The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation with an average sensitivity of 79%, accuracy of 78% and specificity of 76%. The proposed methods can reduce the likelihood of hazardous events like pulmonary congestion and ventricular suction for HF patients and notify identified abnormal cases to the hospital, clinician and cardiologist for emergency action, which can diminish the mortality rate of patients with HF.

Goal: This paper presents an algorithm for estimating pelvis, thigh, shank, and foot kinematics during walking using only two or three wearable inertial sensors. Methods: The algorithm makes novel use of a Lie-group-based extended Kalman filter. The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero-velocity update, and flat-floor assumption), and constraint update (hinged knee and ankle joints, constant leg lengths). Results: The inertial motion capture algorithm was extensively evaluated on two datasets showing its performance against two standard benchmark approaches in optical motion capture (i.e., plug-in gait (commonly used in gait analysis) and a kinematic fit (commonly used in animation, robotics, and musculoskeleton simulation)), giving insight into the similarity and differences between the said approaches used in different application areas. The overall mean body segment position (relative to mid-pelvis origin) and orientation error magnitude of our algorithm ($n=14$ participants) for free walking was $5.93 \pm 1.33$ cm and $13.43 \pm 1.89^\circ$ when using three IMUs placed on the feet and pelvis, and $6.35 \pm 1.20$ cm and $12.71 \pm 1.60^\circ$ when using only two IMUs placed on the feet. Conclusion: The algorithm was able to track the joint angles in the sagittal plane for straight walking well, but requires improvement for unscripted movements (e.g., turning around, side steps), especially for dynamic movements or when considering clinical applications. Significance: This work has brought us closer to comprehensive remote gait monitoring using IMUs on the shoes. The low computational cost also suggests that it can be used in real-time with gait assistive devices., Comment: 10 pages

Kidney development is key to the long-term health of the fetus. Renal volume and vascularity assessed by 3D ultrasound (3D-US) are known markers of wellbeing, however, a lack of real-time image segmentation solutions preclude these measures being used in a busy clinical environment. In this work, we aimed to automate kidney segmentation using fully convolutional neural networks (fCNNs). We used multi-parametric input fusion incorporating 3D B-Mode and power Doppler (PD) volumes, aiming to improve segmentation accuracy. Three different fusion strategies and their performance were assessed versus a single input (B-Mode) network. Early input-level fusion provided the best segmentation accuracy with an average Dice similarity coefficient (DSC) of 0.81 and Hausdorff distance (HD) of 8.96 mm, an improvement of 0.06 DSC and reduction of 1.43 mm HD compared to our baseline network. Compared to manual segmentation for all models, repeatability was assessed by intra-class correlation coefficients (ICC) indicating good to excellent reproducibility (ICC $>=$ 0.93). The framework was extended to support multiple graphics processing units (GPUs) to better handle volumetric data, dense fCNN models, batch normalization and complex fusion networks. This work and available source code provides a framework to increase the parameter space of encoder-decoder style fCNNs across multiple GPUs and shows that application of multi-parametric 3D-US in fCNN training improves segmentation accuracy.

Introduction: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings. The onset of the COVID-19 pandemic necessitated several modifications to the trial in order to protect participants from potential exposure to infection. The use of TCC-Cardiac during the pandemic inspired the development of a similar model of care (TCC-COVID), targeted at patients being managed at home with a diagnosis of COVID-19. Methods: Recruitment for the TCC-Cardiac trial was terminated shortly after the World Health Organization announced COVID-19 as a global pandemic. Telephone follow-up was commenced, in order to protect patients from unnecessary exposure to hospital staff and patients. Equipment was returned or collected by a ‘no-contact’ method. The TCC-COVID app and model of care had similar functionality to the original TCC-Cardiac app. Participants were enrolled exclusively by remote methods. Oxygen saturation and pulse rate were measured by a pulse oximeter, and symptomatology measured by questionnaire. Measurement results were manually entered into the app and transmitted to an online server for medical staff to review. Results: A total of 164 patients were involved in the TCC-Cardiac trial, with 102 patients involved after the onset of the pandemic. There were no hospitalisations due to COVID-19 in this cohort. The study was successfully completed, with only three participants lost to follow-up. During the pandemic, 5 of 49 (10%) of patients in the intervention arm were readmitted compared to 12 of 53 (23%) in the control arm. Also, in this period, 28 of 29 (97%) of all clinically significant alerts received by the monitoring team were managed successfully in the outpatient setting, avoiding hospitalisation. Patients found the user experience largely positive, with the average rating for the app being 4.56 out of 5. 26 patients have currently been enrolled for TCC-COVID. Recruitment is ongoing. All patients have been safely and effectively monitored, with no major adverse clinical events or technical malfunctions. Patient satisfaction has been high. Conclusion: The TCC-Cardiac RCT was successfully completed despite the challenges posed by COVID-19. Use of the app had an added benefit during the pandemic as participants could be monitored safely from home. The model of care inspired the development of an app with similar functionality designed for use with patients diagnosed with COVID-19.

The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise, and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, a few automated NIBP measurement methods have been developed based on the auscultatory technique. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification, and feature extraction problems. This paper proposes a novel automated AI-based technique for NIBP estimation from auscultatory waveforms (AWs) based on converting the NIBP estimation problem to a sequence-to-sequence classification problem. To do this, a sequence of segments was first formed by segmenting the AWs, and their corresponding decomposed detail, and approximation parts obtained by wavelet packet decomposition method, and extracting features from each segment. Then, a label was assigned to each segment, i.e. (i) between systolic, and diastolic segments, and (ii) otherwise, and a bidirectional long short term memory recurrent neural network (BiLSTM-RNN) was devised to solve the resulting sequence-to-sequence classification problem. Adopting a 5-fold cross-validation scheme, and using a data base of 350 NIBP recordings gave an average mean absolute error of $1.7\pm 3.7$ mmHg for systolic BP (SBP), and $3.4 \pm 5.0$ mmHg for diastolic BP (DBP) relative to reference values. Based on the results achieved, and comparisons made with the existing literature, it is concluded that the proposed automated BP estimation algorithm based on deep learning methods, and auscultatory waveform brings plausible benefits to the field of BP estimation.

Summary Introduction The quantitative assessment of facial appearance and function is critical in the process of restoring normality and thus minimising morbidity in patients with facial deformities. Three-dimensional (3D) scanners have increasingly been applied in clinical settings to circumvent the issues associated with standard approaches, namely, subjectivity. This study aimed to summarise the current literature on the accuracy, reliability, and usability of 3D scanning technologies for soft-tissue facial assessment. Methodology Medline, EMBASE, and Web of Science were searched for studies assessing the accuracy, reliability, and/or clinical usability of 3D scanners in assessing facial morphology. All results were filtered by title, abstract, and finally by full text for relevance. Results Eight hundred and thirty-seven results were filtered down to 41 articles that were included in this review. Articles were categorised depending on the 3D visualising principle of the scanner being tested: laser-based scanning, stereophotogrammetry, structured-light scanning, or RGB-D (red, green, blue-depth) sensors. Discussion Of the traditional 3D scanners evaluated in the literature, stereophotogrammetric systems most consistently demonstrate excellent accuracy and reliability in the collection of 3D facial scans. Due to their cost, size, and complexity, these systems are often unsuitable for incorporation into clinical environments with limited availability of resources, space, and time. Recently developed RGB-D sensors can collect accurate static and dynamic 3D facial scans without many of these disadvantages. Still, further improvements in their technical specifications and a greater focus on the development of automated facial assessment software is needed before RGB-D sensors can be universally accepted as a new gold-standard for soft-tissue facial assessment.

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and spectral information, provides more wealthy information for recognizing cells. In this paper, a novel blood cell classification framework, which combines a modulated Gabor wavelet and deep convolutional neural network (CNN) kernels, named as MGCNN, is proposed based on medical hyperspectral imaging. For each convolutional layer, multi-scale and orientation Gabor operators are taken dot product with initial CNN kernels. The essence is to transform the convolutional kernels into the frequency domain to learn features. By combining characteristics of Gabor wavelets, the features learned by modulated kernels at different frequencies and orientations are more representative and discriminative. Experimental results demonstrate that the proposed model can achieve better classification performance than traditional CNNs and widely used support vector machine approaches, especially as training small-sample-size situations.

The use of soft artificial muscles (SAMs) is rapidly increasing in various domains such as haptics, robotics, and medicine. There is a huge need for a SAM that is highly compliant and facile to fabricate with performance characteristics similar to human muscles. This paper introduces bio-inspired soft hydraulic filament artificial muscles (HFAMs) that can be extended and contracted under fluid pressures. The HFAMs, which have a high aspect ratio of at least 5000, use a simple and low-cost fabrication method of insertion, enabling scalability and mass-production while increasing its generated force via a stiff constrained helical layer and an adjustable stretch ratio of their inner silicone microtube. The developed muscles can produce a high elongation of 246.8% and a high energy efficiency of 62.7%. In addition, the HFAMs can generate a higher contraction force compared to existing state-of-the-art devices via their constrained hollow layer and the adjustable stretch ratio of the inner microtube, enabling a tunable force capability. Experiments are carried out to validate the HFAM performance including durability, lifting, frequency response and energy efficiency tests. The HFAM capabilities are demonstrated via various experiments, offering a potential substitute for the conventional tendon-driven mechanisms with less friction loss and stable energy efficiency while working against long and tortuous paths. A HFAMs-driven soft exoskeleton glove that could assist in grasping multiple objects is developed and evaluated. The new muscles open great opportunities for research and commercial sectors including emerging applications such as soft wearable devices and flexible surgical robots.

Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.

Recording and monitoring electrically-excitable cells is critical to understanding the complex cellular networking within organs as well as the processes underlying many electro-physiological pathologies. Biopotential recording using an optical-electrode (optrode) is a novel approach which has potential to significantly improve interface-instrumentation impedance mismatching as recording contact-sizes become smaller and smaller. Optrodes incorporate a conductive interface that can sense extracellular potential and an underlying layer of liquid crystals that passively transduces electrical signals into measurable optical signals. This study investigates the impedance properties of this optical technology by varying the diameter of recording sites and observing the corresponding changes in the impedance values. The results show that the liquid crystals in this optrode platform exhibit input impedance values (1 MΩ - 100 GΩ) that are three orders of magnitude higher than the corresponding interface impedance, which is appropriate for voltage sensing. The automatic scaling of the input impedance enabled within the optrode system maintains a relatively constant ratio between input and total system impedance of about one for sensing areas with diameters ranging from 40 µm to 1 mm, at which the calculated signal loss is predicted to be1%. This feature preserves the interface-transducer impedance ratio, regardless of the size of the recording site, allowing development of passive optrode arrays capable of very high spatial-resolution recordings.

Introduction Mobile health (mHealth) interventions have grown in popularity, particularly for chronic disease management. Uptake of these interventions depends on patient smartphone ownership. Purpose To examine the smartphone ownership rate among cardiac inpatients and identify the associated demographic factors. Methods Between February 2019 and March 2020, 565 patients were screened for potential enrolment in the TeleClinical Care (TCC) pilot study at two hospitals in Australia. All patients had an admission diagnosis of acute coronary syndrome or heart failure. Mobile phone ownership was documented at the time of screening. Retrospectively, each patient's electronic medical record was examined for: age, sex, primary diagnosis, suburb of residence, private health insurance subscription, smoking status and occupation. Continuous variables were analysed using a multinomial logistic regression model. Categorical variables were analysed using a generalised linear model. Results Mobile phone ownership was documented for 523 patients (92.6%). 60.6% of all patients owned smartphones, and 14.9% owned basic mobile phones. 24.5% of patients did not own any mobile phone. The average age of participants was 70.8 years. Smartphone ownership rates were high among patients in the 18–49 (96%), 50–59 (89%) and 60–69 (85%) year groups. The differences between these groups were not statistically significant. In the age group 70–79 years, however, smartphone ownership fell to 56.5% (p Conclusion Smartphone ownership was common in this inpatient population. Patients who are older, female and of lower socioeconomic background are less likely to own smartphones, and future mHealth programs should be cognizant of this. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Prince of Wales Hospital, Department of Cardiology Figure 1. Smartphone ownership by ageFigure 2. Insurance status

Background Acute coronary syndrome (ACS) and heart failure (HF) are frequent causes of hospitalisation and readmissions. A novel smartphone app-based model of care (TeleClinical Care – TCC) was developed to support patients after ACS or HF admission. Purpose This randomised control trial aimed to characterise both the intervention and clinical outcomes. The primary endpoint was the incidence of 30-day readmissions. Secondary endpoints included six-month cardiac and all-cause readmissions, mortality, major adverse cardiovascular events (MACE), cardiac rehabilitation (CR) completion, medication adherence, serum low-density lipoprotein (LDL-C), quality of life, blood pressure, body mass index, waist circumference and six-minute walk distance. Additionally, cost-effectiveness and user satisfaction were evaluated. Methods Patients were randomised 1:1 to either TCC plus usual care or usual care alone and were followed-up at six months. Intervention arm participants received the TCC app and were asked to use Bluetooth-enabled devices for measuring weight, heart rate, blood pressure and physical activity daily. Readings were automatically transmitted to the patient's smartphone and a secure web-server (KIOLA). Customisable thresholds for each parameter were defined at discharge. Abnormal readings were flagged by email to a monitoring team, who discussed management with the patient's usual healthcare providers. The app also provided educational push notifications. Results 164 patients from two hospitals in Sydney, Australia were enrolled between February 2019 and March 2020 (TCC n=81, control n=83). Recruitment ceased during the COVID-19 pandemic. The mean age was 61.5 years. 79% of patients were male. The per-patient mean percentage of days with data transmission was 64.2±27.5%. 565 alerts were received, 16% of which resulted in additional investigations, healthcare consultation or a change in management. There was no difference in 30-day readmission rate (11 readmissions in each arm). There was a significant difference in six-month readmissions, favouring the intervention (21 vs. 41 readmissions, HR=0.40, 95% CI 0.16–0.95, P=0.03), driven by a reduction in cardiac readmissions (11 vs. 25, HR=0.51, 95% CI 0.27–0.94, P=0.03). Use of TCC was associated with improved CR completion (39% vs. 18%, P=0.025) and medication adherence (75% vs. 50%, P=0.002). There was no significant difference in mortality, MACE, LDL-C, quality of life or any of the physical parameters. The average user rating was 4.56 out of 5. The study cost EUR 4015 per readmission saved. Upon modelling, it was calculated that if the number of enrolled patients exceeds 243, total expenditure will be overcome by cost savings from reducing readmissions. Conclusion The TCC model of care was feasible and safe. In this study, clinical benefits were demonstrated including a reduction in six-month readmissions, improved CR completion and improved medication adherence. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Department of Cardiology, Prince of Wales HospitalPrince of Wales Hospital Foundation Figure 1. TCC interfaceFigure 2. Cumulative readmissions over the course of the trial

Because of the rapid and serious nature of acute cardiovascular disease (CVD) especially ST segment elevation myocardial infarction (STEMI), a leading cause of death worldwide, prompt diagnosis and treatment is of crucial importance to reduce both mortality and morbidity. During a pandemic such as coronavirus disease-2019 (COVID-19), it is critical to balance cardiovascular emergencies with infectious risk. In this work, we recommend using wearable device based mobile health (mHealth) as an early screening and real-time monitoring tool to address this balance and facilitate remote monitoring to tackle this unprecedented challenge. This recommendation may help to improve the efficiency and effectiveness of acute CVD patient management while reducing infection risk.

The traditional differential diagnosis of membranous nephropathy (MN) mainly relies on clinical symptoms, serological examination and optical renal biopsy. However, there is a probability of false positives in the optical inspection results, and it is unable to detect the change of biochemical components, which poses an obstacle to pathogenic mechanism analysis. Microscopic hyperspectral imaging can reveal detailed component information of immune complexes, but the high dimensionality of microscopic hyperspectral image brings difficulties and challenges to image processing and disease diagnosis. In this paper, a novel classification framework, including spatial-spectral density peaks-based discriminant analysis (SSDP), is proposed for intelligent diagnosis of MN using a microscopic hyperspectral pathological dataset. SSDP constructs a set of graphs describing intrinsic structure of MHSI in both spatial and spectral domains by employing density peak clustering. In the process of graph embedding, low-dimensional features with important diagnostic information in the immune complex are obtained by compacting the spatial-spectral local intra-class pixels while separating the spectral inter-class pixels. For the MN recognition task, a support vector machine (SVM) is used to classify pixels in the low-dimensional space. Experimental validation data employ two types of MN that are difficult to distinguish with optical microscope, including primary MN and hepatitis B virus-associated MN. Experimental results show that the proposed SSDP achieves a sensitivity of 99.36%, which has potential clinical value for automatic diagnosis of MN.

Activity recognition can provide useful information about an older individual&rsquo, s activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphone accelerometry data of older people. Deep learning algorithms, including convolutional neural network (CNN) and long short-term memory (LSTM), were evaluated in this study. Smartphone accelerometry data of free-living activities, performed by 53 older people (83.8 &plusmn, 3.8 years, 38 male) under standardized circumstances, were classified into lying, sitting, standing, transition, walking, walking upstairs, and walking downstairs. A 1D CNN, a multichannel CNN, a CNN-LSTM, and a multichannel CNN-LSTM model were tested. The models were compared on accuracy and computational efficiency. Results show that the multichannel CNN-LSTM model achieved the best classification results, with an 81.1% accuracy and an acceptable model and time complexity. Specifically, the accuracy was 67.0% for lying, 70.7% for sitting, 88.4% for standing, 78.2% for transitions, 88.7% for walking, 65.7% for walking downstairs, and 68.7% for walking upstairs. The findings indicated that the multichannel CNN-LSTM model was feasible for smartphone-based activity recognition in older people.

Hypertension affects an estimated 1.4 billion people and is a major cause of morbidity and mortality worldwide. Early diagnosis and intervention can potentially decrease cardiovascular events later in life. However, blood pressure (BP) measurements take time and require training for health care professionals. The measurements are also inconvenient for patients to access, numerous daily variables affect BP values, and only a few BP readings can be collected per session. This leads to an unmet need for an accurate, 24-h continuous, and portable BP measurement system. Electrocardiograms (ECGs) have been considered as an alternative way to measure BP and may meet this need. This review summarizes the literature published from January 1, 2010, to January 1, 2020, on the use of only ECG wave morphology to monitor BP or identify hypertension. From 35 articles analyzed (9 of those with no listed comorbidities and confounders), the P wave, QTc intervals and TpTe intervals may be promising for this purpose. Unfortunately, with the limited number of articles and the variety of participant populations, we are unable to make conclusions about the effectiveness of ECG-only BP monitoring. We provide 13 recommendations for future ECG-only BP monitoring studies and highlight the limited findings in pregnant and pediatric populations. With the advent of convenient and portable ECG signal recording in smart devices and wearables such as watches, understanding how to apply ECG-only findings to identify hypertension early is crucial to improving health outcomes worldwide.

Introduction Cardiovascular disease (CVD) remains the leading cause of death in the world. Mobile phones have become ubiquitous in most developed societies. Smartphone applications, telemonitoring and clinician-driven short message service (SMS) allow for novel methods in managing chronic cardiovascular conditions such as ischaemic heart disease, heart failure and hypertension. Purpose To evaluate the impact of mobile phone-based interventions (MPIs) on mortality, hospitalisations and blood pressure and body mass index (BMI) in patients diagnosed with either acute coronary syndrome, heart failure or hypertension. Methods A systematic review was conducted using seven electronic databases, identifying all randomised control trials (RCTs) featuring an MPI in the management of these conditions. Meta-analysis was performed by using standard analytical techniques. The odds ratio (OR) was used as a summary statistic. Results Twenty-six RCTs including 6,713 patients were identified. Of these 26 studies, 13 examined text messaging intereventions, 10 studied telemonitoring interventions and three described smartphone applications with other functions. Twelve studies were included for meta-analysis. In patients with heart failure (n=1683), MPIs were associated with a significantly lower rate of all-cause hospital admissions at six months (31% vs. 36%, OR 0.77, 95% CI 0.62–0.97, p=0.03, I2 = 0). A significant difference was also demonstrated for heart-failure admissions (14.0% vs. 18.5%, OR 0.69, 95% CI 0.48 to 0.98, p=0.04, I2 = 26%). There was no difference in mortality (10.4% vs. 11.6% p=0.45). In patients with hypertension, the difference in systolic BP was 4.3mmHg less in the intervention group (95% CI: −7.8 to −0.78 mmHg, p=0.02). Four studies examined medication compliance as an endpoint in patients with ischaemic heart disease, and all four demonstrated a significant difference favouring the MPI group (see table 1). However, due to variable quantification of compliance, meta-analysis was not possible. There was no significant difference in the change in BMI from four studies after six or more months (mean difference −0.46, 95% CI: −1.44 to 0.52, P=0.36). Conclusions The available data suggests MPIs may have a role as valuable adjuncts in the management of chronic CVD. Figure 1 Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Health and Medical Research Council (NHMRC)

Cervical vagus nerve stimulation (VNS) is a neuromodulation therapy used in the treatment of several chronic disorders. In order to maximize the therapeutic effectiveness of VNS, it has become increasingly important to deliver fiber-specific neurostimulation, so that undesired effects can be minimized. Assessing the activation of different vagal fiber types through electrical stimulation is therefore essential for developing fiber-selective VNS therapies. Towards this goal, we conducted in silico investigations using a generic model of functionally distinct nerve fibers and clinically relevant cuff electrodes using COMSOL. Our model is constrained by histological observations from rat cervical vagus nerves and its outputs are validated against averaged compound nerve action potentials (CNAPs) obtained from rat vagus nerve recordings. We propose this model as an effective tool to design fiber-specific stimulation protocols before testing them in experimental animals.

Surgical robots have had clinical use since the mid‐1990s. Robot‐assisted surgeries offer many benefits over the conventional approach including lower risk of infection and blood loss, shorter recovery, and an overall safer procedure for patients. The past few decades have shown many emerging surgical robotic platforms that can work in complex and confined channels of the internal human organs and improve the cognitive and physical skills of the surgeons during the operation. Advanced technologies for sensing, actuation, and intelligent control have enabled multiple surgical devices to simultaneously operate within the human body at low cost and with more efficiency. Despite advances, current surgical intervention systems are not able to execute autonomous tasks and make cognitive decisions that are analogous to those of humans. Herein, the historical development of surgery from conventional open to robotic‐assisted approaches with discussion on the capabilities of advanced intelligent systems and devices that are currently implemented in existing surgical robotic systems is reviewed. Also, available autonomous surgical platforms are comprehensively discussed with comments on the essential technologies, existing challenges, and suggestions for the future development of intelligent robotic‐assisted surgical systems toward the achievement of fully autonomous operation.

Background Cardiovascular disease (CVD) remains the leading cause of death worldwide. Mobile phones have become ubiquitous in most developed societies. Smartphone apps, telemonitoring, and clinician-driven SMS allow for novel opportunities and methods in managing chronic CVD, such as ischemic heart disease, heart failure, and hypertension, and in the conduct and support of cardiac rehabilitation. Objective A systematic review was conducted using seven electronic databases, identifying all relevant randomized control trials (RCTs) featuring a mobile phone intervention (MPI) used in the management of chronic CVD. Outcomes assessed included mortality, hospitalizations, blood pressure (BP), and BMI. Methods Electronic data searches were performed using seven databases from January 2000 to June 2019. Relevant articles were reviewed and analyzed. Meta-analysis was performed using standard techniques. The odds ratio (OR) was used as a summary statistic for dichotomous variables. A random effect model was used. Results A total of 26 RCTs including 6713 patients were identified and are described in this review, and 12 RCTs were included in the meta-analysis. In patients with heart failure, MPIs were associated with a significantly lower rate of hospitalizations (244/792, 30.8% vs 287/803, 35.7%; n=1595; OR 0.77, 95% CI 0.62 to 0.97; P=.03; I2=0%). In patients with hypertension, patients exposed to MPIs had a significantly lower systolic BP (mean difference 4.3 mm Hg; 95% CI −7.8 to −0.78 mm Hg; n=2023; P=.02). Conclusions The available data suggest that MPIs may have a role as a valuable adjunct in the management of chronic CVD.

Wearable biosensors for sweat-based analysis are gaining wide attention due to their potential use in personal health monitoring. Flexible wearable devices enable sweat analysis at the molecular level, facilitating noninvasive monitoring of physiological states via real-time monitoring of chemical biomarkers. Advances in sweat extraction technology, real-time biosensors, stretchable materials, device integration, and wireless digital technologies have led to the development of wearable sweat-biosensing devices that are light, flexible, comfortable, aesthetic, affordable, and informative. Herein, we summarize recent advances of sweat wearables from the aspects of sweat extraction, fabrication of stretchable biomaterials, and design of biosensing modules to enable continuous biochemical monitoring, which are essential for a biosensing device. Key chemical components of sweat, sweat capture methodologies, and considerations of flexible substrates for integrating real-time biosensors with electronics to bring innovations in the art of wearables are elaborated. The strategies and challenges involved in improving the wearable biosensing performance and the perspectives for designing sweat-based wearable biosensing devices are discussed.

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths in the world. It has been reported that ∼10%-15% of individuals with colorectal cancer experience a causative mutation in the known susceptibility genes, highlighting the importance of identifying mutations for early detection in high risk individuals. Through extensive sequencing projects such as the International Cancer Genome Consortium (ICGC), a large number of somatic point mutations have been identified that can be used to identify cancer-associated genes, as well as the signature of mutational processes defined by the tri-nucleotide sequence context (motif) of mutated sites. Mutation is the hallmark of cancer genome, and many studies have reported cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes, however, none of these cancer subtyping methods consider these features simultaneously. This highlights the need for a better and more inclusive subtype classification approach to enable biomarker discovery and thus inform drug development for CRC. In this study, we developed a statistical pipeline based on a novel concept ‘gene-motif’, which merges mutated gene information with tri-nucleotide motif of mutated sites, to identify cancer subtypes, in this case CRCs. Our analysis identified for the first time, 3,131 gene-motif combinations that were significantly mutated in 536 ICGC colorectal cancer samples compared to other cancer types, identifying seven CRC subtypes with distinguishable phenotypes and biomarkers. Interestingly, we identified several genes that were mutated in multiple subtypes but with unique sequence contexts. Taken together, our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers.

Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010–2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology.

One of the goals of the Neural Engineering System Design (NESD) program in the United States and of similar programs around the world is to develop an interface able to read from one million neurons in parallel. This is well beyond the capabilities of traditional multi-electrode arrays (MEAs), which are inherently limited in both spatial resolution and number of channels, due to issues with power dissipation and wiring.(1,2) To overcome these roadblocks our group has proposed a novel optrode array that measures electrical activity and uses light for both signal transduction and transmission, thus decoupling the bio-potentials from the signal acquisition circuitry.(3) The technology relies on the sensitivity of a particular class of liquid crystals (LCs) to small electric fields and is analogous to a LC display, where the intensity of each pixel (optrode, in our case) is controlled by the electrical activity of the biological tissue. Here, we present the first use of such a transduction mechanism to record from cardiac tissue and investigate stimulus artifact suppression in rabbit sciatic nerve. Our results pave the way to the development of high-density high-channel-count optrode arrays for electrophysiology studies and brain-machine interfaces.

One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure (BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010–2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.

Nerve conduction and activity is a marker of disease and wellness and provides insight into the complex way the nervous system encodes information. We propose an electro-optical detection system and show the recordings from an electrically stimulated in-vitro nerve preparation. The system converts the action potential at the probing position to light intensity before any amplification and detection. Thence the light signal is detected by a photodetector. The new detection system has the ability of isolating the probing point and the amplification circuits, which reduces the electrical interference from the circuit. Moreover, the sampled signal transmitted via optical fibres rather than cables or wires makes it more robust to environmental noise. From the experiment, we demonstrated that the electro-optical detection system is able to detect and amplify the nerve response. By analysing the data, we can distinguish the response from the stimulus artifact and calculate CAP (compound action potential) propagation speed.

This paper presents an investigation into a novel electro-optic device for bi-directional brain-machine interface (BMI) by using both a chiral smectic C* liquid crystal to sense neuronal signals and the photovoltaic effect to stimulate neuronal tissues. By leveraging both the optical and electrical domains, this new electro-optic device can achieve high density of channel count and we have so far demonstrated up to 323 such channels. We focus here on tissue stimulation by adding a photovoltaic PN junction into the LC sensing structure described elsewhere to achieve a full bi-directional neuronal interface.

We report on the latest development of our photonics-based brain-machine interface. This work done in collaboration between UNSW and Macquarie University – and supported by the US Office of Naval Research – directly addresses the long-term DARPA challenge of producing implantable chips with 1 million neural connections. To the best of our knowledge, no technology has demonstrated the potential so far to scale up to such a massive number of channels.

We propose an optical electrode ’optrode’ sensor array for biopotential measurements. The transduction mechanism is based on deformed helix ferroelectric liquid crystals which realign, altering the optrode’s light reflectance properties, relative to applied potential fields of biological cells and tissue. A computational model of extracellular potential recording by the optrode including the electro-optical transduction mechanism is presented, using a combination of time-domain and frequency-domain finite element analysis. Simulations indicate that the device has appropriate temporal response to faithfully transduce neuronal spikes, and spatial resolution to capture impulse propagation along a single neuron. These simulations contribute to the development of multi-channel optrode arrays for spatio-temporal mapping of electric events in excitable biological tissue.

The data presented here are related and supplementary data to the research article “Implantation and long-term assessment of the stability and biocompatibility of a novel 98 channel suprachoroidal visual prosthesis in sheep” [1] . In Eggenberger et al., nine sheep of the Suffolk (N=2) and Dorper (N=7) breeds were implanted in the left eye with an electrically inactive, suprachoroidal retinal stimulator (Bionic Eye) for durations of up to 100 days. The surgical safety, implant stability and device biocompatibility were assessed. Intraocular pressure measurements, indirect and infrared ophthalmoscopy and optical coherence tomography were performed at fixed time points to evaluate the clinical effects of the surgery and device implantation. Post-mortem eye tissue collection and histology was performed to measure the effects of the intervention at the cellular level. The data, including a comprehensive collection of fundus, infrared, optical coherence tomography and histology images can be used as a reference for comparison with other research, for example, active retinal stimulators. Furthermore, these data can be used to evaluate the suitability of the sheep model, in particular Dorper sheep, for future research.

Soft actuators (SAs) have been used in many compliant robotic structure and wearable devices, due to their safe interaction with the wearers. Despite advances, the capability of current SAs is limited by scalability, high hysteresis, and slow responses. In this paper, a new class of soft, scalable, and high-aspect ratio fiber-reinforced hydraulic SAs is introduced. The new SA uses a simple fabrication process of insertion where a hollow elastic rubber tube is directly inserted into a constrained hollow coil, eliminating the need for the manual wrapping of an inextensible fiber around a long elastic structure. To provide high adaptation to the user skin for wearable applications, the new SAs are integrated into flexible fabrics to form a wearable fabric sleeve. To monitor the SA elongation, a soft liquid metal-based fabric piezoresistive sensor is also developed. To capture the nonlinear hysteresis of the SA, a novel asymmetric hysteresis model which only requires five model parameters in its structure is developed and experimentally validated. The new SAs-driven wearable robotic sleeve is scalable, highly flexible, and lightweight. It can also produce a large amount of force of around 23 N per muscle at around 30% elongation, to provide useful assistance to the human upper limbs. Experimental results show that the soft fabric sleeve can augment a user’s performance when working against a load, evidenced by a significant reduction on the muscular effort, as monitored by electromyogram (EMG) signals. The performance of the developed SAs, soft fabric sleeve, soft liquid metal fabric sensor, and nonlinear hysteresis model reveal that they can effectively modulate the level of assistance for the wearer. The new technologies obtained from this work can be potentially implemented in emerging assistive applications, such as rehabilitation, defense, and industry., Sensors, 21 (22), ISSN:1424-8220

Electrical stimulation of excitable cells provides therapeutic benefits for a variety of medical conditions, including restoration of partial vision to those blinded via some types of retinal degeneration. To improve visual percepts elicited by the current technology, researchers are conducting acute electrophysiology experiments, mainly in cats. However, the rat can provide a model of a range of retinal diseases and possesses a sufficiently large eye to be used in this field. This article presents a long-term anesthetic protocol to enable electrophysiology experiments to further the development of visual prostheses. Six Long-Evans rats (aged between 14 and 16 weeks) were included in this study. Surgical anesthesia was maintained for more than 15 h by combining constant intravenous infusion of ketamine (24.0-34.5 mg/kg/h), xylazine (0.9-1.2 mg/kg/h), and inhaled isoflurane in oxygen (

The eleven papers presented in this special issue provide a snapshot of the latest advances in the field of enabling technologies in health engineering and health informatics for the new revolution of Healthcare 4.0, hoping to further enable, drive and accelerate the research, development, and application of key technologies into healthcare systems.

Purpose Mechanical circulatory support (MCS) including left ventricular assist devices (LVADs) are increasingly important in managing patients with end-stage heart failure. Current generation LVADs, such as the HeartWare HVAD operate on a continuous flow basis. Many authors have identified the lack of physiological pulsatility as a limitation in these devices. We sought to test the feasibility of implementing physiological pulsatility using sino-atrial node (SAN) electrograms. Methods Three different rabbits had SAN tissue extracted, placed in a tissue bath and superfused with Krebs-Ringer solution. An extracellular fine metal electrode was placed into the tissue preparation for activity capture. The rate of spontaneous SAN activity was modulated by changing the temperature of the superfusing solution. Electrical activity was captured and identified using a data acquisition system and custom-made software. This was used to trigger a stepwise speed change in an HVAD running in a mock circulation loop. Signal capture was tested in three heart rate ranges (>120, 60-80 and ∼60 beats per minute). Results Unipolar electrogram signals were successfully recorded for all tissue extracts. To simulate physiological settings, we implemented a 100 ms delay between capture and effect, as well as a speed change duration of 30% per cycle (obtained from the beat-to-beat variation). HVAD motor speed was successfully increased from 2500 RPM to 2800 RPM in synchronicity with the firing of the SAN. Similarly, we were also able to achieve a reduction of speed from 2500 RPM to 2000 RPM in synchronicity with the firing of the SAN. Conclusion We were able to successfully capture electrical activity from the SAN for the purpose of introducing physiological pulsatility in all three of our rabbit experiments. This has the potential to change fundamental MCS flow hemodynamics.

Falls are the number one cause of injuries in the elderly. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or a medical rescue group for immediate assistance, mitigating fall-related injuries. However, most studies on fall detection to date have focused on the accuracy of detection while neglecting power efficiency and battery life, and hence the developed fall detectors usually cannot operate for a long period (a year or more) without recharging or replacing their batteries. This paper presents a low-power fall detector that utilizes triaxial accelerometry and barometric pressure sensing. This fall detector reduces its power consumption through both hardware- and firmware-based approaches. This study also incorporates several human trials to develop and evaluate the device, including simulated falls and activities of daily living. A benchtop power measurement test is also conducted to estimate the battery life with data from a one-week free-living trial. These experiments show that the fall detector achieves high sensitivity (97.5% and 93.0%) and specificity (93.2% and 87.3%) on training and testing datasets, while providing an estimated battery life of 664.9 days.

Background and objectives Continuous monitoring of physiological parameters such as photoplethysmography (PPG) has attracted increased interest due to advances in wearable sensors. However, PPG recordings are susceptible to various artifacts, and thus reducing the reliability of PPG-driven parameters, such as oxygen saturation, heart rate, blood pressure and respiration. This paper proposes a one-dimensional convolution neural network (1-D-CNN) to classify five-second PPG segments into clean or artifact-affected segments, avoiding data-dependent pulse segmentation techniques and heavy manual feature engineering. Methods Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network. Results A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%). Conclusion This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion artifact detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.

The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.

Background Th appropriate placement and size selection of mitral prostheses in transcatheter mitral valve implantation (TMVI) is critical, as encroachment on the left ventricular outflow tract (LVOT) may lead to flow obstruction. Recent advances in computed tomography (CT) can be employed for pre-procedural planning of mitral prosthetic valve placement. This study aims to develop patient-specific computational fluid dynamics models of the left ventricle (LV) in the presence of a mitral valve prosthesis to investigate blood flow and LVOT pressure gradient during systole. Methods Patient-specific computational fluid dynamics simulations of TMVI with varied cardiac anatomy and insertion angles were performed (n = 30). Wide-volume full cycle cardiovascular CT images prior to TMVI were used as source anatomical data (n = 6 patients). Blood movement was governed by Navier-Stokes equations and the LV endocardial wall deformation was derived from each patient's CT images. Results The computed pressure gradients in the presence of the mitral prosthesis compared well with clinically measured gradients. Analysis of the effects of prosthetic valve angulation, aorto-mitral annular angle, ejection fraction, LV size and new LVOT area (neo-LVOT) after TMVI in silico revealed that the neo-LVOT area (p < 0.001) was the most significant factor affecting LVOT pressure gradient. Angulation of the mitral valve can substantially mitigate LVOT gradient. Conclusions Computational fluid dynamics simulation is a promising method to aid in pre-TMVI planning and understanding the factors underlying LVOT obstruction.

Low vision and ultimately, profound blindness can result from impaired connectivity or dysfunctional elements in the neural pathways transferring visual information to the brain. Blindness is one of the most debilitating sensory impairments affecting tens of millions of people worldwide, with its prevalence constantly increasing as the global population ages. Immense progress in the last decade has revealed the potential of electronic devices to restore visual perception through applying artificial electrical stimulation to neurons along the pathway from the eye to the brain. Prostheses implanted in the visual cortex, optic nerve, lateral geniculate nucleus, and the retina have led to elicitation of visual perceptions and improvement in functional visual ability in individuals with severe vision disorders. Here, we review the progress in visual bionics, focusing on retinal and cortical prostheses, and discuss key challenges and next-generation technologies.

Purpose Mechanical Circulatory Support (MCS) devices such as ventricular assist devices (VADs) are increasingly important in managing patients with end-stage heart failure. Current generation VADs, such as the HVAD operate on a continuous flow basis. Many authors have identified the lack of pulsatility as a limitation. We tested the feasibility of using a signal from ex-vivo tissue of rabbit sinoatrial node (SAN) to induce pulsatility in an HVAD as well as the BiVACOR total artificial heart through synchronous speed variation. Methods Seven different rabbits had SAN tissue extracted, placed in a tissue bath and superfused with Krebs-Ringer solution. An extracellular fine metal electrode was placed into the tissue preparation for activity capture. The rate of spontaneous SAN activity was modulated by changing the temperature of the superfusing solution. Electrical activity was captured and identified using a data acquisition system and custom-made software. This was used to trigger a stepwise speed change in an HVAD as well as the BiVACOR running in a mock circulatory loop. Signal capture was tested across a range of heart rates. Results Electrogram signals were successfully recorded for all tissue extracts. To simulate physiological settings, we implemented a 100ms delay between capture and effect, as well as a speed change duration of 30% per cycle (obtained from beat-to-beat variation). HVAD motor speed was successfully increased from 2500RPM to 2800RPM in synchronicity with the firing of the SAN. We were also able to achieve a speed reduction from 2500RPM to 2000RPM in synchronicity with the firing of the SAN. The BIVACOR was similarly able to undergo motor speed increase and decrease in synchrony with the SAN at the lower heart rate setting. Conclusion We successfully captured electrical activity from the SAN, then relayed it to both an HVAD and the BiVACOR controller, in doing so we were able to induce pulsatility through speed variation in synchrony with SAN activity. This has the potential to change MCS haemodynamics.