Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count

GOAL

This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors.

METHODS

The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints).

RESULTS

Evaluation of the algorithm using an optical motion capture-based sensor-to-segment calibration on nine participants (7 men and 2 women, weight [Formula: see text] kg, height [Formula: see text] m, age [Formula: see text] years old), with no known gait or lower body biomechanical abnormalities, who walked within a [Formula: see text] m ² capture area shows that it can track motion relative to the mid-pelvis origin with mean position and orientation (no bias) root-mean-square error (RMSE) of [Formula: see text] cm and [Formula: see text], respectively. The sagittal knee and hip joint angle RMSEs (no bias) were [Formula: see text] and [Formula: see text], respectively, while the corresponding correlation coefficient (CC) values were [Formula: see text] and [Formula: see text].

CONCLUSION

The CKF-based algorithm was able to track the 3D pose of the pelvis, thigh, and shanks using only three inertial sensors worn on the pelvis and shanks.

SIGNIFICANCE

Due to the Kalman-filter-based algorithm's low computation cost and the relative convenience of using only three wearable sensors, gait parameters can be computed in real-time and remotely for long-term gait monitoring. Furthermore, the system can be used to inform real-time gait assistive devices.

A Sensorless Control System for an Implantable Heart Pump Using a Real-Time Deep Convolutional Neural Network

Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44%, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

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.

Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone

Activity recognition can provide useful information about an older individual'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 ± 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.

Assessment of Hypertension Using Clinical Electrocardiogram Features: A First-Ever Review

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.

Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review

Cardiovascular disease is 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 measurements can be taken by patients at home. 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.

Robust PPG motion artifact detection using a 1-D convolution neural network

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.

A Computational Model of Functionally-distinct Cervical Vagus Nerve Fibers

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.

Creation of virtual channels in the retina using synchronous and asynchronous stimulation - a modelling study

OBJECTIVE

Implantable retinal prostheses aim to provide artificial vision to those suffering from retinal degenerative diseases by electrically stimulating the remaining retinal neurons using a multi-electrode array. The spatial resolution of these devices can be improved by creation of so-called virtual channels (VCs) that are commonly achieved through synchronized stimulation of multiple electrodes. It is largely unclear though if VCs can be created using asynchronous stimulation, which was the primary aim of this study.

APPROACH

A computational model of multi-layered retina and epi-retinal dual-electrode stimulation was developed to simulate the neural activity of populations of retinal ganglion cells (RGCs) using the VC strategy under both synchronous and asynchronous stimulation conditions.

MAIN RESULTS

Our simulation suggests that VCs can be created using asynchronous stimulation. VC performance under both synchronous and asynchronous stimulation conditions can be improved by optimizing stimulation parameters such as current intensity, current ratio (α) between two electrodes, electrode spacing and the stimulation waveform. In particular, two VC performance measures; (1) linear displacement of the centroid of RGC activation, and (2) the RGC activation size consistency as a function of different current ratios α, have comparable performance under asynchronous and synchronous stimulation with appropriately selected stimulation parameters.

SIGNIFICANCE

Our findings support the possibility of creating VCs in the retina under both synchronous and asynchronous stimulation conditions. The results provide theoretical evidence for future retinal prosthesis designs with higher spatial resolution and power efficiency whilst reducing the number of current sources required to achieve these outcomes.

Towards Controlling Functionally-Distinct Retinal Ganglion Cells In Degenerate Retina

Present retinal neuroprostheses have limited performance capabilities due to indiscriminate activation of different neural pathways. Based on our success in differentially activating ON and OFF cells using high frequency stimuli in a healthy retina, in this study we explored whether we could achieve similar differential activation between these two cell types but in degenerate retina. We found that after blocking the synaptic network, ON retinal ganglion cells (RGCs) could be differentially activated at higher frequencies (4 - 6 kHz) and amplitudes (200 - 240 µA), and OFF RGCs at relatively lower amplitudes (80 - 160 µA) across all tested frequencies. We further found that both cell types could be controlled by quickly modulating the frequency using short stimulation bursts. This work takes us one step closer to reducing the likelihood of indiscriminate activation of RGCs by accurately controlling the activation of functionally-distinct neural pathways.

Estimating Lower Limb Kinematics using Distance Measurements with a Reduced Wearable Inertial Sensor Count

This paper presents an algorithm that makes novel use of distance measurements alongside a constrained Kalman filter to accurately estimate pelvis, thigh, and shank kinematics for both legs during walking and other body movements using only three wearable inertial measurement units (IMUs). The distance measurement formulation also assumes hinge knee joint and constant body segment length, helping produce estimates that are near or in the constraint space for better estimator stability. Simulated experiments have shown that inter-IMU distance measurement is indeed a promising new source of information to improve the pose estimation of inertial motion capture systems under a reduced sensor count configuration. Furthermore, experiments show that performance improved dramatically for dynamic movements even at high noise levels (e.g., σ_dist = 0.2 m), and that acceptable performance for normal walking was achieved at σ_dist = 0.1 m. Nevertheless, further validation is recommended using actual distance measurement sensors.

Class III Antiarrhythmic Effects of Dofetilide in Rabbit Atrial Myocardium

Background

Dofetilide is a new class III antiarrhythmic agent with demonstrated efficacy in ventricular and atrial tachyarrhythmias. We investigated its class HI actions and their modulation by stimulation rate in rabbit atrial myocardium.

Methods and Results

Standard microelectrode techniques were used to record action potentials from rabbit atrial tissue at varying stimulation rates. Dofetilide produced a dose-dependent prolongation of action potential duration at concentrations from 1 nM to 1 μM at an interstimulus interval of 1000 ms. Action potential duration at 90% repolarization (action potential duration) was prolonged from 116 ± 11.7 ms in control solutions to 148 ± 13.9 ms at 1nM dofetilide and 186 ± 49.3 ms at 1 μM dofetilide ( P < .05 for 1 nM vs control; P < .01 for 1 μM vs control). Reduction of interstimulus interval to 500 ms had no significant effect on action potential duration prolongation by dofetilide. At faster rates than this, and particularly at an interstimulus interval less than 330 ms, a marked “reverse rate dependence” of the class III effect was observed. Specifically, the high therapeutic concentration of 10 nM showed no effect on action potential duration at interstimulus interval of 250 ms or 200 ms, and even at a concentration of 30 nM, the small class III effect was no longer statistically significant at these rates.

Conclusion

Dofetilide prolongs action potential duration in rabbit atrial myocardium, but this effect is significantly attenuated at stimulation rates above 2 Hz.

Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Advances in Sweat Wearables: Sample Extraction, Real-Time Biosensing, and Flexible Platforms

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.

Multimodal Capacitive and Piezoresistive Sensor for Simultaneous Measurement of Multiple Forces

Quantitative information on the magnitudes and directions of multiple contacting forces is crucial for a wide range of applications including human-robot interaction, prosthetics, and bionic hands. Herein we report a highly stretchable sensor integrating capacitive and piezoresistive mechanisms that can simultaneously determine multiple forces. The sensor consists of three layers in a sandwich design. The two facesheets serve as both piezoresistive sensors and electrodes for the capacitive sensor, with the core being a porous structure made by using a simple sugar particle template technique to give them high stretchability. The two facesheets contain segregated conductive networks of silver nanowires (AgNWs) and carbon nanofibers (CNFs). By measuring the changes in the electrical resistance of the facesheets and the capacitance between the facesheets, three separate mechanical stimuli can be determined, including normal pressure, in-plane stretch, and transverse shear force. The newly developed multidirectional sensor offers a significant opportunity for the next generation of wearable sensors for human health monitoring and bionic skin for robots.

Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring

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.

Recreation of eyelid mechanics using the sling concept✰

BACKGROUND

Paralytic lagophthalmos causes major functional, aesthetic and psychological problems in patients with facial paralysis. The Bionic Lid Implant for Natural Closure (BLINC) project aims to restore eyelid function using an implanted electromagnetic actuator combined with an eyelid sling. The authors performed a preliminary study using cadaveric heads to investigate the optimal application of an eyelid sling in various configurations around the orbit.

METHODS

The sling was tested in a cadaveric sheep head using 2 medial anchor points and 4 lateral ostectomy points. An impulse was generated using gravitational force to test each combination of medial and lateral sling insertion sites using weights between 10 and 50 g. Each generated blink was recorded and analysed. The final result was validated in a human cadaveric model.

RESULTS

The maximum amount of eye closure and closure speed displayed in sheep were 83.7 ± 9.4% of total closure and 70.6 ± 6.9 mm/s at a maximum force of 490 mN, respectively. The 2 inferior lateral attachments performed better at displacing the eyelid than the superior attachments. The position with the highest degree of eye-closure (improvement of 21.6%, p < 0.001) and speed (improvement of 30.4 mm/s, p < 0.001) was the combination of a posterior medial attachment and an inferior-posterior lateral attachment, which resulted in a near physiological closure in human cadaver.

CONCLUSION

Closure improved with an inferior lateral position due to increased force acting in the direction of closure. Posterior positioning increases force acting radially, towards the centre of eyelid movement. The latter directs the closure force to effectively move the eyelid around the curved globe.

A Three-Dimensional Microelectrode Array to Generate Virtual Electrodes for Epiretinal Prosthesis Based on a Modeling Study

Despite many advances in the development of retinal prostheses, clinical reports show that current retinal prosthesis subjects can only perceive prosthetic vision with poor visual acuity. A possible approach for improving visual acuity is to produce virtual electrodes (VEs) through electric field modulation. Generating controllable and localized VEs is a crucial factor in effectively improving the perceptive resolution of the retinal prostheses. In this paper, we aimed to design a microelectrode array (MEA) that can produce converged and controllable VEs by current steering stimulation strategies. Through computational modeling, we designed a three-dimensional concentric ring–disc MEA and evaluated its performance with different stimulation strategies. Our simulation results showed that electrode–retina distance (ERD) and inter-electrode distance (IED) can dramatically affect the distribution of electric field. Also the converged VEs could be produced when the parameters of the three-dimensional MEA were appropriately set. VE sites can be controlled by manipulating the proportion of current on each adjacent electrode in a current steering group (CSG). In addition, spatial localization of electrical stimulation can be greatly improved under quasi-monopolar (QMP) stimulation. This study may provide support for future application of VEs in epiretinal prosthesis for potentially increasing the visual acuity of prosthetic vision.

Optimizing Stimulation Strategies for Retinal Electrical Stimulation: a Modelling Study

In this research, a continuum multi-compartmental model of retinal electrical stimulation was utilized to find the best strategy for activating retinal ganglion cells (RGCs). Two types of return electrodes configuration placed suprachoroidally were used: monopolar and hexapolar. The current was delivered either simultaneously or sequentially with two kinds of waveforms: biphasic symmetric charge-balanced cathodic and anodic first pulses. Our results revealed there is no significant difference in current threshold between single monopolar and hexapolar stimulation regardless of the applied current stimulus waveform. Moreover, sequential stimulation for both monopolar or hexapolar was more effective in reducing current threshold than simultaneous stimulation when biphasic cathodic first pulses were used. Concurrent monopolar stimulation was significant in reducing the current threshold compared to single monopolar whereas concurrent hexapolar did not alter the current threshold. Overall, concurrent monopolar stimulation was efficacious in reducing current threshold regardless of the stimulus waveforms and sequential stimulation was more useful only with biphasic cathodic first pulses.

SMCT - An Innovative Tool for Mental Health Analysis of Twitter Data

Mental health conditions affect individuals, families and the wider community. It is estimated by the Australian Institute of Health and Welfare that 45% of Australians will experience some form of mental health issue in their lifetime. The World Health Organization (WHO; Switzerland) has assessed that mental health is a cornerstone of overall health.Large-scale collection of data related to mental health is traditionally difficult and carried out infrequently. It is a challenge for researchers to assess seasonal, weekly or diurnal mental health trends. Social media has been demonstrated to present opportunities to extend traditional data collection techniques, particularly the expression of an individual's state of mind.The "Social Media Collection Tool" (SMCT) is a research tool developed to collect Twitter data for mental health analysis. Data collected is filtered to identify "of interest" users, allowing for cross-sectional analysis. Additionally, SMCT provides the capability to "re-poll" of interest users to develop a contextual understanding for these users on Twitter, providing a longitudinal analysis. SMCT, for the first time, combined the two data analysis techniques into a single tool.In demonstrative analysis scenarios the SMCT has collected more than 500,000 tweets and identified almost 6,000 as being of interest. The tool has also been used to investigate trends in the responses made by other users to an of interest tweet.

Multi-Site Photoplethysmography Technology for Blood Pressure Assessment: Challenges and Recommendations

Hypertension is one of the most prevalent diseases and is often called the “silent killer” because there are usually no early symptoms. Hypertension is also associated with multiple morbidities, including chronic kidney disease and cardiovascular disease. Early detection and intervention are therefore important. The current routine method for diagnosing hypertension is done using a sphygmomanometer, which can only provide intermittent blood pressure readings and can be confounded by various factors, such as white coat hypertension, time of day, exercise, or stress. Consequently, there is an increasing need for a non-invasive, cuff-less, and continuous blood pressure monitoring device. Multi-site photoplethysmography (PPG) is a promising new technology that can measure a range of features of the pulse, including the pulse transit time of the arterial pulse wave, which can be used to continuously estimate arterial blood pressure. This is achieved by detecting the pulse wave at one body site location and measuring the time it takes for it to reach a second, distal location. The purpose of this review is to analyze the current research in multi-site PPG for blood pressure assessment and provide recommendations to guide future research. In a systematic search of the literature from January 2010 to January 2019, we found 13 papers that proposed novel methods using various two-channel PPG systems and signal processing techniques to acquire blood pressure using multi-site PPG that offered promising results. However, we also found a general lack of validation in terms of sample size and diversity of populations.

Computational Models And Tools For Developing Sophisticated Stimulation Strategies For Retinal Neuroprostheses

Improvements to the efficacy of retinal neuroprostheses can be achieved by developing more sophisticated neural stimulation strategies to enable selective or preferential activation of specific retinal ganglion cells (RGCs). Computational models are particularly well suited for these investigations. The electric field can be accurately described by mathematical formalisms, and the population-based neural responses to the electrical stimulation can be investigated at resolutions well beyond those achievable by current state-of-the-art biological techniques. In this study, we used a biophysically-and morphologically-detailed RGC model to explore the ability of high frequency electrical stimulation (HFS) to preferentially activate ON and OFF RGC subtypes, the two major information pathways of the retina. The performance of a wide range of electrical stimulation amplitudes (0 - $100~\mu \mathbf {A}$) and frequencies (1 - 10 kHz) on functionally-distinct RGC responses were evaluated. We found that ON RGCs could be preferentially activated at relatively higher stimulation amplitudes $( > 50 {\mu } \mathrm {A})$ and frequencies $( >2$ kHz) while OFF RGCs were activated by lower stimulation amplitudes (10 to $50 {\mu } \mathrm {A})$ across all tested frequencies. These stimuli also show great promise in eliciting RGC responses that parallel RGC encoding: one RGC type exhibited an increase in spiking activity during electrical stimulation whilst another exhibited decreased spiking activity, given the same stimulation parameters.

Computational Simulation Expands Understanding of Electrotransfer-Based Gene Augmentation for Enhancement of Neural Interfaces

The neural interface is a critical factor in governing efficient and safe charge transfer between a stimulating electrode and biological tissue. The interface plays a crucial role in the efficacy of electric stimulation in chronic implants and both electromechanical properties and biological properties shape this. In the case of cochlear implants, it has long been recognized that neurotrophins can stimulate growth of the target auditory nerve fibers into a favorable apposition with the electrode array, and recently such arrays have been re-purposed to enable electrotransfer (electroporation)-based neurotrophin gene augmentation to improve the "bionic ear." For both this acute bionic array-directed electroporation and for chronic conventional cochlear implant arrays, the electric fields generated in target tissue during pulse delivery are central to efficacy, but are challenging to map. We present a computational model for predicting electric fields generated by array-driven DNA electrotransfer in the cochlea. The anatomically realistic model geometry was reconstructed from magnetic resonance images of the guinea pig cochlea and an eight-channel electrode array embedded within this geometry. The model incorporates a description of both Faradaic and non-Faradaic mechanisms occurring at the electrode-electrolyte interface with frequency dependency optimized to match experimental impedance spectrometry measurements. Our simulations predict that a tandem electrode configuration with four ganged cathodes and four ganged anodes produces three to fourfold larger area in target tissue where the electric field is within the range for successful gene transfer compared to an alternate paired anode-cathode electrode configuration. These findings matched in vivo transfection efficacy of a green fluorescent protein (GFP) reporter following array-driven electrotransfer of the reporter-encoding plasmid DNA. This confirms utility of the developed model as a tool to optimize the safety and efficacy of electrotransfer protocols for delivery of neurotrophin growth factors, with the ultimate aim of using gene augmentation approaches to improve the characteristics of the electrode-neural interfaces in chronically implanted neurostimulation devices.

A Physiological Control System for an Implantable Heart Pump That Accommodates for Interpatient and Intrapatient Variations

Left ventricular assist devices (LVADs) can provide mechanical support for a failing heart as a bridge to transplant and destination therapy. Physiological control systems for LVADs should be designed to respond to changes in hemodynamic across a variety of clinical scenarios and patients by automatically adjusting the heart pump speed. In this study, a novel adaptive physiological control system for an implantable heart pump was developed to respond to interpatient and intrapatient variations to maintain the left-ventricle-end-diastolic-pressure (LVEDP) in the normal range of 3 to 15 mmHg to prevent ventricle suction and pulmonary congestion. A new algorithm was also developed to detect LVEDP from pressure sensor measurement in real-time mode. Model-free adaptive control (MFAC) was employed to control the pump speed via simulation of 100 different patient conditions in each of six different patient scenarios, and compared to standard PID control. Controller performance was tracked using the sum of the absolute error (SAE) between the desired and measured LVEDP. The lower SAE on control tracking performance means that the measured LVEDP follows the desired LVEDP faster and with less amplitude oscillations, preventing ventricle suction and pulmonary congestion (mean and standard deviation of SAE (mmHg) for all 600 simulations were 18813 ± 29345 and 24794 ± 28380 corresponding to MFAC and PID controller, respectively). In four out of six patient scenarios, MFAC control tracking performance was better than the PID controller. This study shows the control performance can be guaranteed across different patients and conditions when using MFAC over PID control, which is a step toward clinical acceptance of these systems.

The use of photoplethysmography for assessing hypertension

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.

Mediating Retinal Ganglion Cell Spike Rates Using High-Frequency Electrical Stimulation

Recent retinal studies have directed more attention to sophisticated stimulation strategies based on high-frequency (>1.0 kHz) electrical stimulation (HFS). In these studies, each retinal ganglion cell (RGC) type demonstrated a characteristic stimulus-strength-dependent response to HFS, offering the intriguing possibility of focally targeting retinal neurons to provide useful visual information by retinal prosthetics. Ionic mechanisms are known to affect the responses of electrogenic cells during electrical stimulation. However, how these mechanisms affect RGC responses is not well understood at present, particularly when applying HFS. Here, we investigate this issue via an in silico model of the RGC. We calibrate and validate the model using an in vitro retinal preparation. An RGC model based on accurate biophysics and realistic representation of cell morphology, was used to investigate how RGCs respond to HFS. The model was able to closely replicate the stimulus-strength-dependent suppression of RGC action potentials observed experimentally. Our results suggest that spike inhibition during HFS is due to local membrane hyperpolarization caused by outward membrane currents near the stimulus electrode. In addition, the extent of HFS-induced inhibition can be largely altered by the intrinsic properties of the inward sodium current. Finally, stimulus-strength-dependent suppression can be modulated by a wide range of stimulation frequencies, under generalized electrode placement conditions. In vitro experiments verified the computational modeling data. This modeling and experimental approach can be extended to further our understanding on the effects of novel stimulus strategies by simulating RGC stimulus-response profiles over a wider range of stimulation frequencies and electrode locations than have previously been explored.

Smart Triggering of the Barometer in a Fall Detector Using a Semi-Permeable Membrane

The inclusion of a barometer in a wearable fall detector has been shown to improve the detection accuracy by measuring the altitude change associated with a fall event. However, the barometer is a power-hungry sensor, and the sensing power of barometer can be the dominant power consumption source in a wearable fall detector. In this study, we propose a triggering method that reduces barometer power consumption and prolongs the battery life. This approach utilizes a hermetically sealed and waterproof enclosure, with a small inlet covered by a semi-permeable membrane (SPM) to delay the time at which equilibrium between the internal and external pressures is reached, allowing the barometer to be woken from an idle low-power mode and capture the rising air pressure caused by the decrease in altitude during the fall. Two alternative signal processing methods were applied to the pressure waveform to detect the rising pressure pattern, a differential moving average filter (DMAF) and a Kalman filter (KF). The proposed fall detector was evaluated with data collected from a laboratory-based trial and a free-living trial, in which the barometric pressure data, recorded in open-air, were passed through a mathematical model of the leaky enclosure and the SPM assembly. The results show that the proposed fall detector with a 3.7 V, 140 mAh lithium-polymer battery provides a long battery life (DMAF 447 days, KF 444 days) while not compromising the sensitivity (DMAF 91.8%, KF 91.9%), specificity (DMAF 95.2% and KF 95.5%), or the false alarm rate (DMAF 0.035 alarms/hour and KF 0.064 alarms/hour).

A Multi-Domain Continuum Model of Electrical Stimulation of Healthy and Degenerate Retina

A continuum multi-domain model of electrical stimulation of the retina is presented and validated against retinal ganglion cell (RGC) excitation thresholds reported in a recently published in vitro experimental study. We applied our model to investigate the response of the RGC layer to electrical stimulation during mid-to-late stage retinal degeneration for both epiretinal and suprachoroidal configurations. Interestingly, our model predicted that suprachoroidal stimulation of the degenerate retina required increased current thresholds, mainly because of the presence of the glial scar layer. In contrast, epiretinal stimulation thresholds were almost similar for both healthy and degenerate models. The latter finding implies that there is no influence of the glial scar layer on epiretinal stimulation current thresholds.

Computational Simulation of Array-based Electroporation in the Cochlea

We present a computational model for predicting electric field distributions following array-based closed-loop electroporation in the cochlea. The model geometry was reconstructed from magnetic resonance images of the guinea pig cochlea and an eight-channel electrode array embedded within this geometry. The model's electrode voltage output waveform was obtained from electric potential mapping conducted in physiological solution following constant-current stimulation using the electrode array. Our simulations predict that a tandem electrode configuration with four ganged cathodes and four ganged anodes produces a larger area in target tissue where the electric field is within the range for successful gene transfer compared to an alternate paired anode-cathode electrode configuration. These findings corroborate published in vivo evidence comparing the two configurations and support the utility of the developed model as a tool to optimize the efficacy of electroporation electrodes.

Detection of Atrial Fibrillation from RR Intervals and PQRST Morphology using a Neural Network Ensemble

Early detection and discrimination of cardiac arrhythmia, atrial fibrillation (AF) in particular, is essential for timely intervention to improve patient outcomes. In this work, an algorithm was developed to classify ECG records as normal, AF, other arrhythmia, or too noisy to classify. This algorithm, which was an entry for the PhysioNet Computing in Cardiology Challenge 2017 (the Challenge), is described. Artifact masking and QRS detection were applied to lead-I equivalent ECG records and 17 features were extracted which captured the irregularity of the RR intervals, the PQRST morphology, and artifact/noise. An ensemble of ten neural networks (NN) was trained on the features from a training set of 5,970 records. A final classification was taken by majority vote over the 10 classifiers. The trained NN models were validated on a further 2,558 ECG records and then tested on a blind out-of-sample test set of 3,658 records. A mean $F_{1}$ score across the four classes of 0.78 for the training/validation sets and 0.80 for the testing set was achieved. A higher $F_{1}$ score for the testing set indicates that overtraining did not occur, unlike most entries to the Challenge (winner mean $F_{1}$ score of 0.89 for training/validation set, and 0.83 for testing set). Performance of the Challenge winner was not ideal and there is evidence of overtraining, indicating the difficulty of classifying AF from single-lead ECG. The features and method described here performed comparably and overtraining did not occur (high likelihood of generalization) indicating a good starting point for future work.

Adaptive template matching of photoplethysmogram pulses to detect motion artefact

OBJECTIVE

The photoplethysmography (PPG) signal, commonly used in the healthcare settings, is easily affected by movement artefact leading to errors in the extracted heart rate and SpO₂ estimates. This study aims to develop an online artefact detection system based on adaptive (dynamic) template matching, suitable for continuous PPG monitoring during daily living activities or in the intensive care units (ICUs).

APPROACH

Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database. The master template is then updated with each incoming clean PPG pulse. The correlation coefficient is used to classify the PPG pulse into either good or bad quality categories. The performance of our algorithm was evaluated using data obtained from two different sources: (i) our own data collected from 19 healthy subjects using the wearable Sotera Visi Mobile system (Sotera Wireless Inc.) as they performed various movement types; and (ii) ICU data provided by the PhysioNet MIMIC II database. The developed algorithm was evaluated against a manually annotated 'gold standard' (GS).

MAIN RESULTS

Our algorithm achieved an overall accuracy of 91.5%  ±  2.9%, with a sensitivity of 94.1%  ±  2.7% and a specificity of 89.7%  ±  5.1%, when tested on our own data. When applying the algorithm to data from the PhysioNet MIMIC II database, it achieved an accuracy of 98.0%, with a sensitivity and specificity of 99.0% and 96.1%, respectively.

SIGNIFICANCE

The proposed method is simple and robust against individual variations in the PPG characteristics, thus making it suitable for a diverse range of datasets. Integration of the proposed artefact detection technique into remote monitoring devices could enhance reliability of the PPG-derived physiological parameters.

A Multiphysics Biventricular Cardiac Model: Simulations With a Left-Ventricular Assist Device

Computational models have become essential in predicting medical device efficacy prior to clinical studies. To investigate the performance of a left-ventricular assist device (LVAD), a fully-coupled cardiac fluid-electromechanics finite element model was developed, incorporating electrical activation, passive and active myocardial mechanics, as well as blood hemodynamics solved simultaneously in an idealized biventricular geometry. Electrical activation was initiated using a simplified Purkinje network with one-way coupling to the surrounding myocardium. Phenomenological action potential and excitation-contraction equations were adapted to trigger myocardial contraction. Action potential propagation was formulated within a material frame to emulate gap junction-controlled propagation, such that the activation sequence was independent of myocardial deformation. Passive cardiac mechanics were governed by a transverse isotropic hyperelastic constitutive formulation. Blood velocity and pressure were determined by the incompressible Navier-Stokes formulations with a closed-loop Windkessel circuit governing the circulatory load. To investigate heart-LVAD interaction, we reduced the left ventricular (LV) contraction stress to mimic a failing heart, and inserted a LVAD cannula at the LV apex with continuous flow governing the outflow rate. A proportional controller was implemented to determine the pump motor voltage whilst maintaining pump motor speed. Following LVAD insertion, the model revealed a change in the LV pressure-volume loop shape from rectangular to triangular. At higher pump speeds, aortic ejection ceased and the LV decompressed to smaller end diastolic volumes. After multiple cycles, the LV cavity gradually collapsed along with a drop in pump motor current. The model was therefore able to predict ventricular collapse, indicating its utility for future development of control algorithms and pre-clinical testing of LVADs to avoid LV collapse in recipients.

A continuum model of electrical stimulation of multi-compartmental retinal ganglion cells

A continuum multi-domain model of electrical stimulation of the retina is presented. Each point in the retinal ganglion cell layer could be thought of as representing a single cell, whose biophysics is described using a four-compartment formulation incorporating varying ion channel expressions in the soma, axon initial segment, dendrites and axon. Our continuum model was validated against a discrete morphologically-realistic OFF RGC model, using intra- and extra-cellular electrical stimulation scenarios. Simulations from the continuum model reproduced the same results as that of the discrete model. Our continuum model is the first multi-domain model to represent all main RGC compartments, not just the soma. Moreover, we demonstrated that this model allows the investigation of axonal activation which has been observed to influence the perception of phosphenes.

Progress in artificial vision through suprachoroidal retinal implants

Retinal implants have proven their ability to restore visual sensation to people with degenerative retinopathy, characterized by photoreceptor cell death and the retina's inability to sense light. Retinal bionics operate by electrically stimulating the surviving neurons in the retina, thus triggering the transfer of visual sensory information to the brain. Suprachoroidal implants were first investigated in Australia in the 1950s. In this approach, the neuromodulation hardware is positioned between the sclera and the choroid, thus providing significant surgical and safety benefits for patients, with the potential to maintain residual vision combined with the artificial input from the device. Here we review the latest advances and state of the art devices for suprachoroidal prostheses, highlight future technologies and discuss challenges and perspectives towards improved rehabilitation of vision.

Stimulation of peripheral nerves using conductive hydrogel electrodes

Nerve block via electrical stimulation of nerves requires a device capable of transferring large amounts of charge across the neural interface on chronic time scales. Current metal electrode designs are limited in their ability to safely and effectively deliver this charge in a stable manner. Conductive hydrogel (CH) coatings are a promising alternative to metal electrodes for neural interfacing devices. This study assessed the performance of CH electrodes compared to platinum-iridium (PtIr) electrodes in commercial nerve cuff devices in both the in vitro and acute in vivo environments. CH electrodes were found to have higher charge storage capacities and lower impedances compared to bare PtIr electrodes. Application of CH coatings also resulted in a three-fold increase in in vivo charge injection limit. These significant improvements in electrochemical properties will allow for the design of smaller and safer stimulating devices for nerve block applications.

Differential electrical responses in retinal ganglion cell subtypes: effects of synaptic blockade and stimulating electrode location

OBJECTIVE

Visual prostheses have shown promising results in restoring visual perception to blind patients. The ability to differentially activate retinal ganglion cell (RGC) subtypes could further improve the efficacy of these medical devices.

APPROACH

Using whole-cell patch clamp, we investigated membrane potential differences between ON and OFF RGCs in the mouse retina when their synaptic inputs were blocked by synaptic blockers, and examined the differences in stimulation thresholds under such conditions. By injecting intracellular current, we further confirmed the relationship between RGC stimulation thresholds and resting membrane potentials (RMPs). In addition, we investigated the effects of stimulating electrode location on the differences in stimulation thresholds between ON and OFF RGCs.

MAIN RESULTS

With synaptic blockade, ON RGCs became significantly more hyperpolarized (from  -61.8  ±  1.4 mV to  -70.8  ±  1.6 mV), while OFF RGCs depolarized slightly (from  -60.5  ±  0.7 mV to  -58.6  ±  0.9 mV). RGC stimulation thresholds were negatively correlated with their RMPs (Pearson r value:  -0.5154; p-value: 0.0042). Thus, depriving ON RGCs of synaptic inputs significantly increased their thresholds (from 14.7  ±  1.3 µA to 22.3  ±  2.1 µA) over those of OFF RGCs (from 13.2  ±  0.7 µA to 13.1  ±  1.1 µA). However, with control solution, ON and OFF RGC stimulation thresholds were not significantly different. Finally, placement of the stimulating electrode away from the axon enhanced differences in stimulation thresholds between ON and OFF RGCs, facilitating preferential activation of OFF RGCs.

SIGNIFICANCE

Since ON and OFF RGCs have antagonistic responses to natural light, achieving differential RGC activation could convey more natural visual information, leading to better visual prosthesis outcomes.

Closed-Loop Efficient Searching of Optimal Electrical Stimulation Parameters for Preferential Excitation of Retinal Ganglion Cells

The ability for visual prostheses to preferentially activate functionally-distinct retinal ganglion cells (RGCs) is important for improving visual perception. This study investigates the use of high frequency stimulation (HFS) to elicit RGC activation, using a closed-loop algorithm to search for optimal stimulation parameters for preferential ON and OFF RGC activation, resembling natural physiological neural encoding in response to visual stimuli. We evaluated the performance of a wide range of electrical stimulation amplitudes and frequencies on RGC responses in vitro using murine retinal preparations. It was possible to preferentially excite either ON or OFF RGCs by adjusting amplitudes and frequencies in HFS. ON RGCs can be preferentially activated at relatively higher stimulation amplitudes (>150 μA) and frequencies (2-6.25 kHz) while OFF RGCs are activated by lower stimulation amplitudes (40-90 μA) across all tested frequencies (1-6.25 kHz). These stimuli also showed great promise in eliciting RGC responses that parallel natural RGC encoding: ON RGCs exhibited an increase in spiking activity during electrical stimulation while OFF RGCs exhibited decreased spiking activity, given the same stimulation amplitude. In conjunction with the in vitro studies, in silico simulations indicated that optimal HFS parameters could be rapidly identified in practice, whilst sampling spiking activity of relevant neuronal subtypes. This closed-loop approach represents a step forward in modulating stimulation parameters to achieve appropriate neural encoding in retinal prostheses, advancing control over RGC subtypes activated by electrical stimulation.

Selecting Power-Efficient Signal Features for a Low-Power Fall Detector

Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.

Conductive Hydrogel Electrodes for Delivery of Long-Term High Frequency Pulses

Nerve block waveforms require the passage of large amounts of electrical energy at the neural interface for extended periods of time. It is desirable that such waveforms be applied chronically, consistent with the treatment of protracted immune conditions, however current metal electrode technologies are limited in their capacity to safely deliver ongoing stable blocking waveforms. Conductive hydrogel (CH) electrode coatings have been shown to improve the performance of conventional bionic devices, which use considerably lower amounts of energy than conventional metal electrodes to replace or augment sensory neuron function. In this study the application of CH materials was explored, using both a commercially available platinum iridium (PtIr) cuff electrode array and a novel low-cost stainless steel (SS) electrode array. The CH was able to significantly increase the electrochemical performance of both array types. The SS electrode coated with the CH was shown to be stable under continuous delivery of 2 mA square pulse waveforms at 40,000 Hz for 42 days. CH coatings have been shown as a beneficial electrode material compatible with long-term delivery of high current, high energy waveforms.