Textile-Based Body Capacitive Sensing for Knee Angle Monitoring.

Monitoring human movement is highly relevant in mobile health applications. Textile-based wearable solutions have the potential for continuous and unobtrusive monitoring. The precise estimation of joint angles is important in applications such as the prevention of osteoarthritis or in the assessment of the progress of physical rehabilitation. We propose a textile-based wearable device for knee angle estimation through capacitive sensors placed in different locations above the knee and in contact with the skin. We exploited this modality to enhance the baseline value of the capacitive sensors, hence facilitating readout. Moreover, the sensors are fabricated with only one layer of conductive fabric, which facilitates the design and realization of the wearable device. We observed the capability of our system to predict knee sagittal angle in comparison to gold-standard optical motion capture during knee flexion from a seated position and squats: the results showed an R2 coefficient between 0.77 and 0.99, root mean squared errors between 4.15 and 12.19 degrees, and mean absolute errors between 3.28 and 10.34 degrees. Squat movements generally yielded more accurate predictions than knee flexion from a seated position. The combination of the data from multiple sensors resulted in R2 coefficient values of 0.88 or higher. This preliminary work demonstrates the feasibility of the presented system. Future work should include more participants to further assess the accuracy and repeatability in the presence of larger interpersonal variability.

Passive and Wireless All-Textile Wearable Sensor System.

Mobile health technology and activity tracking with wearable sensors enable continuous unobtrusive monitoring of movement and biophysical parameters. Advancements in clothing-based wearable devices have employed textiles as transmission lines, communication hubs, and various sensing modalities; this area of research is moving towards complete integration of circuitry into textile components. A current limitation for motion tracking is the need for communication protocols demanding physical connection of textile with rigid devices, or vector network analyzers (VNA) with limited portability and lower sampling rates. Inductor-capacitor (LC) circuits are ideal candidates as textile sensors can be easily implemented with textile components and allow wireless communication. In this paper, the authors report a smart garment that can sense movement and wirelessly transmit data in real time. The garment features a passive LC sensor circuit constructed of electrified textile elements that sense strain and communicate through inductive coupling. A portable, lightweight reader (fReader) is developed for achieving a faster sampling rate than a downsized VNA to track body movement, and for wirelessly reading sensor information suitable for deployment with a smartphone. The smart garment-fReader system monitors human movement in real-time and exemplifies the potential of textile-based electronics moving forward.

Non-invasive cardiovascular magnetic resonance assessment of pressure recovery distance after aortic valve stenosis.

BACKGROUND: Decisions in the management of aortic stenosis are based on the peak pressure drop, captured by Doppler echocardiography, whereas gold standard catheterization measurements assess the net pressure drop but are limited by associated risks. The relationship between these two measurements, peak and net pressure drop, is dictated by the pressure recovery along the ascending aorta which is mainly caused by turbulence energy dissipation. Currently, pressure recovery is considered to occur within the first 40-50 mm distally from the aortic valve, albeit there is inconsistency across interventionist centers on where/how to position the catheter to capture the net pressure drop. METHODS: We developed a non-invasive method to assess the pressure recovery distance based on blood flow momentum via 4D Flow cardiovascular magnetic resonance (CMR). Multi-center acquisitions included physical flow phantoms with different stenotic valve configurations to validate this method, first against reference measurements and then against turbulent energy dissipation (respectively n = 8 and n = 28 acquisitions) and to investigate the relationship between peak and net pressure drops. Finally, we explored the potential errors of cardiac catheterisation pressure recordings as a result of neglecting the pressure recovery distance in a clinical bicuspid aortic valve (BAV) cohort of n = 32 patients. RESULTS: In-vitro assessment of pressure recovery distance based on flow momentum achieved an average error of 1.8 ± 8.4 mm when compared to reference pressure sensors in the first phantom workbench. The momentum pressure recovery distance and the turbulent energy dissipation distance showed no statistical difference (mean difference of 2.8 ± 5.4 mm, R2 = 0.93) in the second phantom workbench. A linear correlation was observed between peak and net pressure drops, however, with strong dependences on the valvular morphology. Finally, in the BAV cohort the pressure recovery distance was 78.8 ± 34.3 mm from vena contracta, which is significantly longer than currently accepted in clinical practise (40-50 mm), and 37.5% of patients displayed a pressure recovery distance beyond the end of the ascending aorta. CONCLUSION: The non-invasive assessment of the distance to pressure recovery is possible by tracking momentum via 4D Flow CMR. Recovery is not always complete at the ascending aorta, and catheterised recordings will overestimate the net pressure drop in those situations. There is a need to re-evaluate the methods that characterise the haemodynamic burden caused by aortic stenosis as currently clinically accepted pressure recovery distance is an underestimation.

Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection.

With advances in portable and wearable devices, it should be possible to analyze and interpret the collected biosignals from those devices to tailor a psychological intervention to help patients. This study focuses on detecting anxiety by using a portable device that collects electrocardiogram (ECG) and respiration (RSP) signals. The feature extraction focused on heart-rate variability (HRV) and breathing-rate variability (BRV). We show that a significant change in these signals occurred between the non-anxiety-induced and anxiety-induced states. The HRV biomarkers were the mean heart rate (MHR; p¯ = 0.04), the standard deviation of the heart rate (SD; p¯ = 0.01), and the standard deviation of NN intervals (SDNN; p¯ = 0.03) for ECG signals, and the mean breath rate (MBR; p¯ = 0.002), the standard deviation of the breath rate (SD; p¯ < 0.0001), the root mean square of successive differences (RMSSD; p¯ < 0.0001) and SDNN (p¯ < 0.0001) for RSP signals. This work extends the existing literature on the relationship between stress and HRV/BRV by being the first to introduce a transitional phase. It contributes to systematically processing mental and emotional impulse data in humans measured via ECG and RSP signals. On the basis of these identified biomarkers, artificial-intelligence or machine-learning algorithms, and rule-based classification, the automated biosignal-based psychological assessment of patients could be within reach. This creates a broad basis for detecting and evaluating psychological abnormalities in individuals upon which future psychological treatment methods could be built using portable and wearable devices.

Towards patient-specific prediction of conduction abnormalities induced by transcatheter aortic valve implantation: a combined mechanistic modelling and machine learning approach.

Aims: Post-procedure conduction abnormalities (CA) remain a common complication of transcatheter aortic valve implantation (TAVI), highlighting the need for personalized prediction models. We used machine learning (ML), integrating statistical and mechanistic modelling to provide a patient-specific estimation of the probability of developing CA after TAVI. Methods and results: The cohort consisted of 151 patients with normal conduction and no pacemaker at baseline who underwent TAVI in nine European centres. Devices included CoreValve, Evolut R, Evolut PRO, and Lotus. Preoperative multi-slice computed tomography was performed. Virtual valve implantation with patient-specific computer modelling and simulation (CM&S) allowed calculation of valve-induced contact pressure on the anatomy. The primary composite outcome was new onset left or right bundle branch block or permanent pacemaker implantation (PPI) before discharge. A supervised ML approach was applied with eight models predicting CA based on anatomical, procedural and mechanistic data. CA occurred in 59% of patients (n = 89), more often after mechanical than first or second generation self-expanding valves (68% vs. 60% vs. 41%). CM&S revealed significantly higher contact pressure and contact pressure index in patients with CA. The best model achieved 83% accuracy (area under the curve 0.84) and sensitivity, specificity, positive predictive value, negative predictive value, and F1-score of 100%, 62%, 76%, 100%, and 82%. Conclusion: ML, integrating statistical and mechanistic modelling, achieved an accurate prediction of CA after TAVI. This study demonstrates the potential of a synergetic approach for personalizing procedure planning, allowing selection of the optimal device and implantation strategy, avoiding new CA and/or PPI.

Deformation of leukaemia cell lines in hyperbolic microchannels: investigating the role of shear and extensional components.

The mechanical properties of cells are of enormous interest in a diverse range of physio and pathological situations of clinical relevance. Unsurprisingly, a variety of microfluidic platforms have been developed in recent years to study the deformability of cells, most commonly employing pure shear or extensional flows, with and without direct contact of the cells with channel walls. Herein, we investigate the effects of shear and extensional flow components on fluid-induced cell deformation by means of three microchannel geometries. In the case of hyperbolic microchannels, cell deformation takes place in a flow with constant extensional rate, under non-zero shear conditions. A sudden expansion at the microchannel terminus allows one to evaluate shape recovery subsequent to deformation. Comparison with other microchannel shapes, that induce either pure shear (straight channel) or pure extensional (cross channel) flows, reveals different deformation modes. Such an analysis is used to confirm the softening and stiffening effects of common treatments, such as cytochalasin D and formalin on cell deformability. In addition to an experimental analysis of leukaemia cell deformability, computational fluid dynamic simulations are used to deconvolve the role of the aforementioned flow components in the cell deformation dynamics. In general terms, the current study can be used as a guide for extracting deformation/recovery dynamics of leukaemia cell lines when exposed to various fluid dynamic conditions.