Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
As a core component of MEMS LiDAR, the 2D MEMS mirror, with high-precision optical angle detection, is a key technology for radar scanning and imaging. Existing piezoresistive detection schemes of mirrors suffer from high fabrication complexity, high temperature sensitivity, and a limited accuracy of only 0.08◦, failing to meet the requirements for vehicular and airborne scanning applications. This study focuses on a two-dimensional electromagnetic MEMS mirror. Based on the reflection principles of geometric optics, angle detection schemes with photodiode (PD) arrays are analyzed. A novel four-quadrant optical measurement sensor featuring a 16-PD array is proposed. This design replaces conventional large-area PDs with a compact PD array, effectively mitigating nonlinearity and low accuracy issues caused by oversized PD trenches and edge dimensions. High-precision detection of the mirror’s deflection angle is achieved by measuring the current variations induced by the reflected spot position on the PDs in each quadrant. The experimental results demonstrate that the 16-PD array optical angle sensor achieves an accuracy between 0.03◦ and 0.036◦ over a detection range of ±8◦....
Fluidic circuits have shown significant promise in enabling complex functionality in soft robots with a minimal number of input signals. However, implementing complex behaviors typically involves numerous specialized components, resulting in intricate and nonversatile circuits. To address this challenge, a multifunctional fluidic unit designed to operate flexibly as a valve, sensor, or actuator is introduced. This unit provides an extensive design space that allows precise tuning to achieve the desired functionality. In particular, one configuration integrates all three functions simultaneously, resulting in a self-sensing oscillating actuator. By assembling multiple units—each customized for specific roles—complex robotic behaviors can be realized. The versatility and effectiveness of this modular approach are demonstrated by creating several robotic systems, including a controlled shaker, a multimodal hopper, and a crawler capable of sensing environmental boundaries. Furthermore, when these units are mechanically coupled via a shared body, it exhibit emergent passive behaviors, such as self-synchronization—a behavior that is elucidated with a Kuramoto model of networks of oscillators. This study highlights the potential of multifunctionality as a powerful and efficient strategy for realizing embodied intelligence in fluidic robotic systems....
Robots play an ever-expanding role in society by performing a broad range of tasks. However, there are growing concerns about their environmental sustainability, as many conventional robotic systems rely on materials that are neither renewable nor degradable. Consequently, significant efforts are being made to develop eco-friendly robots built from sustainable and biodegradable materials. In this context, plants represent a promising direction, as the biomaterials composing plants are biodegradable, and their inherent multifunctionality as living organisms, including sensing, actuation, energy harvesting, and self-healing, makes them strong candidates for realizing biodegradable robotic systems. Moreover, they are abundant and renewable resources. Recent studies have demonstrated plant-based robotic systems that harness some of these features, helping to establish plant robotics as an emerging research field. Among the many functions plants offer, actuation is pivotal, as it enables physical robotic motion, such as locomotion and grasping, which substantially broadens the potential applications of plant robots. Focusing on plant movement, this article reviews key plant species and their behaviors through the perspective of actuation characteristics. It also examines the current landscape of plant-based robotic systems and outlines future research directions in this rapidly growing field....
Conventional respiratory monitoring is often invasive, while most non-contact technologies like radar or cameras are limited to estimating respiratory rate, failing to reconstruct the detailed waveform of the respiratory flow itself. This gap limits their clinical utility for advanced diagnostics. We introduce a novel system that bridges this gap by combining a contactless, impedance-based sensor (the Thoraxmonitor) with a dedicated machine learning framework to directly reconstruct the full respiratory flow signal. Operating at 433MHz, the system’s antenna array detects subtle changes in thoracic impedance, which are then translated into a quantitative flow signal by a Multilayer Perceptron Regressor. Based on data from 17 subjects benchmarked against a gold-standard flowmeter, our system accurately detected 98% of respiratory cycles. It achieved remarkable precision in timing respiratory events, with mean deviations of +60 ms (±79 ms) for inspiration and +50 ms (±63 ms) for expiration, making it suitable for time-critical applications. While a systematic bias in absolute tidal volume prediction currently limits inter-subject comparisons, the system excels at tracking relative intra-subject changes. Crucially, our model quantifies its own reliability, providing an intrinsic self-assessment mechanism. This work demonstrates a significant step beyond simple rate detection towards comprehensive, comfortable, and reliable respiratory analysis in clinical and everyday settings....
The adoption of wearable sensors for precision training has accelerated in recent years, yet most studies and reviews remain device- or feasibility-centric and lack a field-ready decision framework. This review organizes wearable sensing across four monitoring dimensions—physiological, kinematic, biochemical, and dynamic—and maps them onto three training pillars: physical, technical, and tactical. From the perspectives of athletes and coaches, we operationalize quality control, threshold, and feedback loop to translate measurement into action. We critically appraise key limitations, including signal robustness under high-intensity motion, inter-individual variability and limited model generalizability, cross-device data fusion and latency, battery life and wearability, privacy and data ownership, and limited accessibility beyond elite settings. Looking ahead, we advocate a shift from mere multidimensional measurement to a verifiable, reusable, and deployable precision-training ecosystem that delivers actionable metrics and clear decision support for practitioners....
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