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We present HPSpeech, a silent speech interface for commodity headphones. HPSpeech utilizes the existing speakers of the headphones to emit inaudible acoustic signals. The movements of the temporomandibular joint (TMJ) during speech modify the reflection pattern of these signals, which are captured by a microphone positioned inside the headphones. To evaluate the performance of HPSpeech, we tested it on two headphones with a total of 18 participants. The results demonstrated that HPSpeech successfully recognized 8 popular silent speech commands for controlling the music player with an accuracy over 90%. While our tests use modified commodity hardware (both with and without active noise cancellation), our results show that sensing the movement of the TMJ could be as simple as a firmware update for ANC headsets which already include a microphone inside the hear cup. This leaves us to believe that this technique has great potential for rapid deployment in the near future. We further discuss the challenges that need to be addressed before deploying HPSpeech at scale. 

In this paper, we introduce PoseSonic, an intelligent acoustic sensing solution for smartglasses that estimates upper body poses. Our system only requires two pairs of microphones and speakers on the hinges of the eyeglasses to emit FMCW-encoded inaudible acoustic signals and receive reflected signals for body pose estimation. Using a customized deep learning model, PoseSonic estimates the 3D positions of 9 body joints including the shoulders, elbows, wrists, hips, and nose. We adopt a cross-modal supervision strategy to train our model using synchronized RGB video frames as ground truth. We conducted in-lab and semi-in-the-wild user studies with 22 participants to evaluate PoseSonic, and our user-independent model achieved a mean per joint position error of 6.17 cm in the lab setting and 14.12 cm in semi-in-the-wild setting when predicting the 9 body joint positions in 3D. Our further studies show that the performance was not significantly impacted by different surroundings or when the devices were remounted or by real-world environmental noise. Finally, we discuss the opportunities, challenges, and limitations of deploying PoseSonic in real-world applications.

 

Extracellular vesicles (EVs) have been identified as promising biomarkers for the noninvasive diagnosis of various diseases. However, challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low purities. Here, we report a high-yield ( > 90%) and rapid ( < 10 min) EV isolation method calledFLocculation viaOrbitalAcousticTrapping (FLOAT). The FLOAT approach utilizes an acoustofluidic droplet centrifuge to rotate and controllably heat liquid droplets. By adding a thermoresponsive polymer flocculant, nanoparticles as small as 20 nm can be rapidly and selectively concentrated at the center of the droplet. We demonstrate the ability of FLOAT to separate urinary EVs from the highly abundant Tamm-Horsfall protein, addressing a significant obstacle in the development of EV-based liquid biopsies. Due to its high-yield nature, FLOAT reduces biofluid starting volume requirements by a factor of 100 (from 20 mL to 200 µL), demonstrating its promising potential in point-of-care diagnostics.

 

Sensing movements and gestures inside the oral cavity has been a long-standing challenge for the wearable research community. This paper introduces EchoNose, a novel nose interface that explores a unique sensing approach to recognize gestures related to mouth, breathing, and tongue by analyzing the acoustic signal reflections inside the nasal and oral cavities. The interface incorporates a speaker and a microphone placed at the nostrils, emitting inaudible acoustic signals and capturing the corresponding reflections. These received signals were processed using a customized data processing and machine learning pipeline, enabling the distinction of 16 gestures involving speech, tongue, and breathing. A user study with 10 participants demonstrates that EchoNose achieves an average accuracy of 93.7% in recognizing these 16 gestures. Based on these promising results, we discuss the potential opportunities and challenges associated with applying this innovative nose interface in various future applications. 

While mesenchymal stem cells (MSCs) have gained enormous attention due to their unique properties of self-renewal, colony formation, and differentiation potential, the MSC secretome has become attractive due to its roles in immunomodulation, anti-inflammatory activity, angiogenesis, and anti-apoptosis. However, the precise stimulation and efficient production of the MSC secretome for therapeutic applications are challenging problems to solve. Here, we report on Acoustofluidic Interfaces for the Mechanobiological Secretome of MSCs: AIMS. We create an acoustofluidic mechanobiological environment to form reproducible three-dimensional MSC aggregates, which produce the MSC secretome with high efficiency. We confirm the increased MSC secretome is due to improved cell-cell interactions using AIMS: the key mediator N-cadherin was up-regulated while functional blocking of N-cadherin resulted in no enhancement of the secretome. After being primed by IFN-γ, the secretome profile of the MSC aggregates contains more anti-inflammatory cytokines and can be used to inhibit the pro-inflammatory response of M1 phenotype macrophages, suppress T cell activation, and support B cell functions. As such, the MSC secretome can be modified for personalized secretome-based therapies. AIMS acts as a powerful tool for improving the MSC secretome and precisely tuning the secretory profile to develop new treatments in translational medicine.

 

Abstract 2D material hydrogels have recently sparked tremendous interest owing to their potential in diverse applications. However, research on the emerging 2D MXene hydrogels is still in its infancy. Herein, we show a universal 4D printing technology for manufacturing MXene hydrogels with customizable geometries, which suits a family of MXenes such as Nb 2 CT x , Ti 3 C 2 T x , and Mo 2 Ti 2 C 3 T x . The obtained MXene hydrogels offer 3D porous architectures, large specific surface areas, high electrical conductivities, and satisfying mechanical properties. Consequently, ultrahigh capacitance (3.32 F cm −2 (10 mV s −1 ) and 233 F g −1 (10 V s −1 )) and mass loading/thickness-independent rate capabilities are achieved. The further 4D-printed Ti 3 C 2 T x hydrogel micro-supercapacitors showcase great low-temperature tolerance (down to –20 °C) and deliver high energy and power densities up to 93 μWh cm −2 and 7 mW cm −2 , respectively, surpassing most state-of-the-art devices. This work brings new insights into MXene hydrogel manufacturing and expands the range of their potential applications.