Peak Space Motion Artifact Cancellation Applied to Textile Electrode Waist Electrocardiograms Recording During Outdoors Walking and Jogging

Background: Obtaining reliable rate heart estimates from waist based electrocardiograms (ECGs) poses a very challenging problem due to the presence of extreme motion artifacts. The literature reveals few, if any, attempts to apply motion artifact cancellation methods to waist based ECGs. This paper describes a new methodology for ameliorating the effects of motion artifacts in ECGs by specifically targeting ECG peaks for elimination that are determined to be correlated with accelerometer peaks. This peak space cancellation was applied to real world waist based ECGs. Algorithm Summary: The methodology includes successive applications of a previously described pattern-based heart beat detection scheme (Temporal Pattern Search, or TEPS) that can also detect patterns in other types of peak sequences. In the first application, TEPS is applied to accelerometer signals recorded contemporaneously with ECG signals to identify high-quality accelerometer peak sequences (SA) indicative of quasi-periodic motion likely to impair identification of peaks in a corresponding ECG signal. The process then performs ECG peak detection and locates the closest in time ECG peak to each peak in an SA. The differences in time between ECG and SA peaks are clustered. If the number of elements in a cluster of peaks in an SA exceeds a threshold, the ECG peaks in that cluster are removed from further processing. After this peak removal process, further QRS detection proceeds according to TEPS. Experiment: The above procedure was applied to data from real world experiments involving four sessions of walking and jogging on a dirt road for approximately 20-25 minutes. A compression shirt with textile electrodes served as the ground truth recording. A textile electrode based chest strap was worn around the waist to generate a single channel signal upon which to test peak space cancellation/TEPS. Results: Both walking and jogging heart rates were generally well tracked. In the four recordings, the percentage of 5 second segments within 10 beats/minute of reference was 96%, 99%, 92% and 96%. The percentage of segments within 5 beats/minute of reference was 86%, 90%, 82% and 78%. There was very good agreement between the RR intervals associated with the reference and waist recordings. For acceptable quality segments, the root mean square sum of successive RR interval differences (RMSSD) was calculated for both the reference and waist recordings. Next, the difference between waist and reference RMSSDs was calculated (∆RMSSD). The mean ∆RMSSD (over acceptable segments) was 4.6 m, 5.2 ms, 5.2 ms and 6.6 ms for the four recordings. Conclusion: Given that only one waist ECG channel was available, and that the strap used for the waist recording was not tailored for that purpose, the proposed methodology shows promise for waist based sinus rhythm QRS detection.

Deep-Learning Based Estimation of Dielectrophoretic Force

The ability to accurately quantify dielectrophoretic (DEP) force is critical in the development of high-efficiency microfluidic systems. This is the first reported work that combines a textile electrode-based DEP sensing system with deep learning in order to estimate the DEP forces invoked on microparticles. We demonstrate how our deep learning model can process micrographs of pearl chains of polystyrene (PS) microbeads to estimate the DEP forces experienced. Numerous images obtained from our experiments at varying input voltages were preprocessed and used to train three deep convolutional neural networks, namely AlexNet, MobileNetV2, and VGG19. The performances of all the models was tested for their validation accuracies. Models were also tested with adversarial images to evaluate performance in terms of classification accuracy and resilience as a result of noise, image blur, and contrast changes. The results indicated that our method is robust under unfavorable real-world settings, demonstrating that it can be used for the direct estimation of dielectrophoretic force in point-of-care settings.

A review of textile-based electrodes developed for electrostimulation

Electrical stimulation can be used for the treatment of various nerve and muscle injuries as well as acute and chronic pain conditions. An electrical pulse is applied to a muscle or nerve to activate excitable tissue using internal or external electrodes with the aim of building muscle strength, artificially creating or supporting limb movement or reducing pain. Textile electrodes offer several advantages over conventionally used disposable surface electrodes: they are flexible and re-usable and they do not require hydrogels, thereby avoiding skin irritation and allergic reactions and enhancing user comfort. This article presents a literature review that assesses the state of research on textile electrode constructions. Based on the review, production approaches and designs are compared, methods for evaluating stimulation discomfort and pain are proposed and issues related to user compliance are discussed. The article concludes with suggestions for future work focused on investigating the impacts of textile-based electrode parameters on comfort, convenience and ease of use.

The Effect of Sleeve Pattern and Fit on E-Textile Electromyography (EMG) Electrode Performance in Smart Clothing Design

When e-textile EMG electrodes are integrated into clothing, the fit of the clothing on the body, and therefore its pattern and cut become important factors affecting the EMG signal quality in relation to the seamless contact between the skin and the e-textile electrode. The research so far on these effects was conducted on commercially available clothing or in tubular sleeve forms for arms. There is no study that investigated different clothing pattern and fit conditions and their effect on e-textile EMG electrode performance. This study investigates the effect of clothing pattern and fit in EMG applications using e-textile electrodes integrated onto the sleeves of custom drafted t-shirts in set-in and raglan sleeve pattern variations. E-textile electrode resistance, signal-to-noise ratio (SNRdB), power spectral density and electrode–skin impedance are measured and evaluated in set-in sleeve and raglan sleeve conditions with participants during a standardized arm movement protocol in comparison to the conventional hydrogel Ag/AgCl electrodes. The raglan sleeve pattern, widely used in athletic wear to provide extra ease for the movement of the shoulder joint, showed superior performance and therefore indicated the pattern and cut of a garment could have significant effect on EMG signal quality in designing smart clothing.

CHARACTERIZATION OF TEXTILE ELECTRODES FOR EMG MEASUREMENTS

Textile electrodes are an alternative to conventional silver-chloride electrodes in wearable systems. Their easy integration in garments and comfort provided to the user make them an interesting development of textile engineering. The potential of such electrodes to allow more unobtrusive data collection in health and sports context may enable the development of biosensing garments to be used in biomechanics. However, proper validation of the recorded signals is paramount, and few studies have yet presented consistent methodologies for textile-based electromyographic recordings. This study presents the validation of the electrical and morphological properties of electromyographic signals recorded with textile electrode, in comparison to conventional silver-chloride electrodes. Results indicate that both sets of electrodes have identical signal-to-noise ratios, but with distinct impedance frequency responses. Electromyographic envelope morphologies are also identical, although textile electrodes usually have lower amplitudes.

Performance and Signal Quality Analysis of Electrocardiogram Textile Electrodes for Smart Apparel Applications

Electronic textile (e-textile) systems applied to biological signal monitoring are of great interest to the healthcare industry, given the potential to provide continuous and long-term monitoring of healthy individuals and patients. Most developments in e-textiles have focused on novel materials and systems without systematic considerations into how the hierarchical structure of fibrous assemblies may influence performance and compatibility of the materials during use. This study examines mechanisms underlying the stability and quality of textile-based electrocardiogram (ECG) electrodes used in a smart bra. Signal quality of the biometric data obtained affects feedback and user experience and may be influenced by characteristics and properties of the material. Under stationary and dynamic conditions, analysis of the raw ECG signal and heart rate, with respect to textile-electrode material properties have been performed. Currently, there is no standardized procedure to compare the ECG signal between electrode materials. In this study, several methods have been applied to compare differences between silver-based textile electrodes and silver/silver-chloride gel electrodes. The comparison methods serve to complement visual observations of the ECG signal acquired, as possible quantitative means to differentiate electrode materials and their performance. From the results obtained, signal quality, and heart rate (HR) detection were found to improve with increased skin contact, and textile structures with lower stretch and surface resistance, especially under dynamic/movement test conditions. It was found that the performance of the textile electrode materials compared exceeded ECG signal quality thresholds previously established for acceptable signal quality, specifically for the kurtosis (K > 5), and Pearson correlation coefficients (r ≥ 0.66) taken from average ECG waveforms calculated.