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Stretchable conductive materials are originally conceived as radio frequency (RF) and electromagnetic interference (EMI) shielding materials, and, under stretch, they generally function as distributed strain-gauges. These commercially available conductive elastomers have found their space in low power health monitoring systems, for example, to monitor respiratory and cardiac functions. Conductive elastomers do not behave linearly due to material constraints; hence, when used as a sensor, a full characterisation to identify ideal operating ranges are required. In this paper, we studied how the continuous stretch cycles affected the material electrical and physical properties in different embodiment impressed by bodily volume change. We simulated the stretch associated with breathing using a bespoke stress rig to ensure reproducibility of results. The stretch rig is capable of providing constant sinusoidal waves in the physiological ranges of extension and frequency. The material performances is evaluated assessing the total harmonic distortion (THD), signal-to-noise ratio (SNR), correlation coefficient, peak to peak (P-P) amplitude, accuracy, repeatability, hysteresis, delay, and washability. The results showed that, among the three controlled variables, stretch length, stretch frequency and fabric width, the most significant factor to the signal quality is the stretch length. The ideal working region is within 2% of the original length. The material cut in strips of >3 mm show more reliable to handle a variety of stretch parameter without losing its internal characteristics and electrical properties.Sensors 2020, 20, 90 2 of 35 services to achieve a pervasive response [7]. Even with the current state-of-the-art technologies, end sensors remain moderately larger in size, cumbersome to use and unsuitable for long-term monitoring. Sleep studies are a good example scenario where long-term vital parameter monitoring is paramount. The sleep monitoring system should provide minimum intrusion to sleep [8,9]; however, the t current state-of-the-art Polysomnography (PSG) [10] is time-consuming, expensive, limited to in-clinic and provides minimum comfort. The alternative at-home portable sleep study devices use the same conventional sensors in smaller package. A radical change in sensor technologies required to make vital monitoring devices comfortable and "invisible" to the user. The rise of "invisibles" targets making wearable devices built into the commodities people use daily, such as clothes, shoes and jewellery [11]. Usage of the correct type of sensors would enable low-cost implementations that can be embedded in day-to-day clothes that facilitate long-term monitoring [12].Recently, many sensors suitable for tidal monitoring have been presented [13][14][15][16][17]. Electro-resistive sensors based on carbon polymers have shown a great potential for biomedical sensors, and specifically wearable sensors [18], and bio-impedance monitoring [19]. Electro-resistive sensors are made by re-purposing electromagnetic interfer...

Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future.