Neurological disorders, cardiovascular diseases and strokes are leading causes of mortality worldwide. Diagnostics and therapeutics for patients under timely point-of-care can save thousands of lives. However, lack of access to minimally-intrusive monitoring systems makes timely diagnosis difficult and sometimes impossible. Existing ambulatory recording equipment are incapable of performing continuous remote patient monitoring because of the inability of conventional silver-silver-chloride-gel-electrodes to perform long-term monitoring, non-reusability, lack of scalable-standardized wireless communication platforms, and user-friendly design. Recent progress in textile-based nanosensors and mobile platforms has resulted in novel wearable health monitoring systems for neurological and cardiovascular disorders. This review paper discusses nanostructured-textile-based dry electrodes that are better suited for long-term measurement of electrocardiography(ECG), electroencephalography(EEG), electrooculography(EOG), electromyography(EMG) and bioimpedance with very low baseline noise, improved sensitivity and seamless integration into garments of daily use. It presents bioelectromagnetic principles of origination and propagation of bioelectric signals and nanosensor functioning, which provide a unique perspective on development of novel wearable systems that harness their potential. Combined with stateof-the-art embedded wireless network devices to communicate with smartphone, laptop or directly to remote server through mobile network (GSM,4G-LTE,GPRS), they can function as wearable wireless health-diagnostic systems that are more intuitive to use.

Wearable and ultraportable electronics coupled with pervasive computing are poised to revolutionize healthcare services delivery. The potential cost savings in both treatment, as well as preventive care are the focus of several research efforts across the globe. In this review, we describe the motivations behind wearable solutions to real-time cardiovascular monitoring from a perspective of current healthcare services, as well as from a systems design perspective. We identify areas where emerging research is underway, namely: nanotechnology in textile-based wearable monitors and healthcare solutions targeted towards smart devices, like smartphones and tablets.

Mu waves, also known as mu rhythms, comb or wicket rhythms are synchronized patterns of electrical activity involving large numbers of neurons, in the part of the brain that controls voluntary functions. Controlling, manipulating, or gaining greater awareness of these functions can be done through the process of Biofeedback. Biofeedback is a process that enables an individual to learn how to change voluntary movements for purposes of improving health and performance through the means of instruments such as EEG which rapidly and accurately 'feedback' information to the user. Biofeedback is used for therapeutic purpose for Autism Spectrum Disorder (ASD) by focusing on Mu waves for detecting anomalies in brain wave patterns of mirror neurons. Conventional EEG measurement systems use gel based gold cup electrodes, attached to the scalp with adhesive. It is obtrusive and wires sticking out of the electrodes to signal acquisition system make them impractical for use in sensitive subjects like infants and children with ASD. To remedy this, sensors can be incorporated with skull cap and baseball cap that are commonly used for infants and children. Feasibility of Textile based Sensor system has been investigated here. Textile based multi-electrode EEG, EOG and EMG monitoring system with embedded electronics for data acquisition and wireless transmission has been seamlessly integrated into fabric of these items for continuous detection of Mu waves. Textile electrodes were placed on positions C3, CZ, C4 according to 10-20 international system and their capability to detect Mu waves was tested. The system is ergonomic and can potentially be used for early diagnosis in infants and planning therapy for ASD patients.

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Targeted maintenance of blood pressure for hypertensive patients requires accurate monitoring of blood pressure at home. Use of multiparametric vital signs ECG, heart sounds, and thoracic impedance for blood pressure estimation at home has not been reported previously. In an observational multi-site study, 120 subjects (female (N = 61, 52%)) between 18 and 83 years of age were recruited with the following stratification (Normal (20%), prehypertensive (37%), stage 1(26%), and stage 2 (18%). From these subjects, 1686 measurements of blood pressure from a sphygmomanometer were associated with simultaneously acquired signals from the SimpleSense device. An ensemble of tree-based models was trained with inputs as metrics derived from the multiparametric and patient demographics data. A test Mean Absolute Difference (MAD) of ± 6.38 mm of Hg and ± 5.10 mm of Hg were obtained for systolic and diastolic blood pressures (SBP; DBP), respectively. Comparatively, the MAD for wrist-worn blood pressure cuff OMRON BP6350 (GUDID—10073796266353) was ± 8.92 mm of Hg and ± 6.86 mm of Hg, respectively. Machine learning models trained to use multiparametric data can monitor SBP and DBP without the need for calibration, and with accuracy levels comparable to at-home cuff-based blood pressure monitors.