Sensing mechanical tissue deformation in vivo can provide detailed information on organ functionality and tissue states. To bridge the huge mechanical mismatch between conventional electronics and biological tissues, stretchable electronic systems have recently been developed for interfacing tissues in healthcare applications. A major challenge for wireless electronic implants is that they typically require microchips, which adds complexity and may compromise long‐term stability. Here, a chipless wireless strain sensor technology based on a novel soft conductor with high cyclic stability is reported. The composite material consists of gold‐coated titanium dioxide nanowires embedded in a soft silicone elastomer. The implantable strain sensor is based on an resonant circuit which consists of a stretchable plate capacitor and a coil for inductive readout of its resonance frequency. Successful continuous wireless readout during 50% strain cycles is demonstrated. The sensor element has a Young's modulus of 260 kPa, similar to that of the bladder in order to not impair physiological bladder expansion. A proof‐of‐principle measurement on an ex vivo porcine bladder is presented, which shows the feasibility of the presented materials and devices for continuous, wireless strain monitoring of various tissues and organs in vivo.

Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep learning-based algorithm that selectively suppresses noise while maintaining speech signals. The algorithm restores speech intelligibility for hearing aid users to the level of control subjects with normal hearing. It consists of a deep network that is trained on a large custom database of noisy speech signals and is further optimized by a neural architecture search, using a novel deep learning-based metric for speech intelligibility. The network achieves state-of-the-art denoising on a range of human-graded assessments, generalizes across different noise categories and—in contrast to classic beamforming approaches—operates on a single microphone. The system runs in real time on a laptop, suggesting that large-scale deployment on hearing aid chips could be achieved within a few years. Deep learning-based denoising therefore holds the potential to improve the quality of life of millions of hearing impaired people soon.

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