This review examines knittable electronic textiles as a foundational platform for soft wearable systems. Knitting’s interlooped, three-dimensional architectures and computationally-enabled design allow the fabrication of customized electronic textiles that conform to the body while supporting integrated sensing, actuation, processing, communication, and power delivery. By constructing structures from fibers to full fabrics, knitted systems offer stretchability, breathability, and mechanical resilience, supporting functional garments and pathways toward intelligent soft wearable technologies.

The growing use of wearable devices for activity tracking, healthcare, and haptics faces challenges due to the bulkiness and short lifespan of batteries. Integration of a textile-based wireless charging and readout system into everyday clothing can enable seamless power supply and data collection around the body. However, expanding such system to cover the entire body is challenging, as it increases EM interference with the body, degrading the performance of wireless system. This article introduces a meandered textile coil designed for body-scale, efficient wireless charging and readout. The meander coil can confine a strong inductive field near the body surface, ensuring W-class safe charging and sensitive readout with uW-class low power. Moreover, its zigzag design is simple enough for mass production on industrial knitting machines. Therefore, the body-scale meander coil can continuously operate battery-free wearable devices across the body, leading to—Internet of Textiles—ubiquitous deployment of continuous full-body wearable computing into everyday clothing.

Near Field Communication (NFC) is a promising technology for ultra-low-power wearables, yet its short communication range limits its use to narrow-area, point-to-point interactions. We propose a body-scale NFC networking system that extends NFC coverage around the body, enabling surface-to-multipoint communication with distributed NFC sensor tags. This demonstration introduces two key technologies: Meander NFC and picoRing NFC. First, Meander NFC expands a clothing-based NFC networking area up to body scale while enabling a stable readout of small NFC tags occupying \(1 \,\%\) of the coverage area. Meander NFC uses a meander coil which creates a spatially confined inductive field along the textile surface, ensuring robust coupling with small tags while preventing undesired electromagnetic body coupling. Second, picoRing NFC solves the weak inductive coupling caused by distance and size mismatches. By leveraging middle-range NFC and coil optimization, picoRing NFC extends the communication range to connect these disparate nodes between the ring and wristband.

Granular jamming grippers offer high versatility, but their non-linear deformation makes grasp success prediction challenging. Reliable grasping depends on two critical factors: the macroscopic membrane shape and the internal state of particles, which governs jamming stiffness. Conventional non-invasive methods, such as those using Permanent Magnetic Elastomer (PME), effectively capture membrane shape but fundamentally fail to monitor the crucial internal particle state. To address this limitation, we propose a novel acoustic sensing technique where a small speaker and microphone are embedded within the gripper. Our hypothesis is that the internal acoustic characteristics reflect the state of the particles. We implemented a hybrid system combining PME and the proposed acoustic sensing in a single prototype for direct comparison. Experimental results show that while magnetic sensing excels at shape-dependent tasks (object and position recognition, more than 91% accuracy), acoustic sensing achieves superior performance in the Grasp Success Prediction task (86.3% vs. 79.6%).

Cameras support many pervasive computing applications, such as activity recognition, but conventional imaging raises privacy concerns. Depth thresholding offers a privacy-preserving alternative by classifying whether objects lie within a specified distance range. However, accurate depth estimation typically requires intense computation or dedicated sensors, making wearable implementation challenging. This work proposes a method for depth thresholding using only a compact monocular camera by incorporating an optimized coded aperture (CA) into the lens. The method leverages the distance-dependent variation in the optical Point Spread Function (PSF) to determine whether a subject is closer or farther than a chosen depth threshold. This approach significantly reduces computational load and eliminates the need for specialized depth sensors, enabling low-power operation. Simulations with the optimized CA achieved a depth threshold classification accuracy of 68%, substantially outperforming conventional depth CA designs. These results demonstrate the promise of achieving accurate, privacy-preserving depth thresholding with compact cameras without requiring iterative computations.

Magnetoquasistatic wireless power transfer can deliver substantial power to mobile devices over near-field links. Room-scale implementations, such as quasistatic cavity resonators, extend this capability over large enclosed volumes, but their efficiency drops sharply for centimeter-scale or misoriented receivers because the magnetic field is spatially broad and weakly coupled to small coils. Here, we introduce hierarchical resonators that act as selectively activated relays within a room-scale quasistatic cavity resonator, coupling to the ambient magnetic field and re-emitting a stronger local field near a target receiver. This architecture enables localized power delivery to miniature devices without requiring global reshaping of the room-scale cavity mode. Experimentally, the hierarchical link improves power transfer efficiency by more than two orders of magnitude relative to direct room-scale transfer and delivers up to 500 mW of DC power to a 15 mm receiver. We further demonstrate selective multi-relay operation and field reorientation for furniture-embedded charging scenarios. These results establish a scalable route to reconfigurable wireless power delivery for miniature and batteryless devices in room-scale environments.

Traditional wireless power transfer (WPT) systems are largely limited to 1-D charging pads or 2-D charging surfaces and therefore do not support a truly ubiquitous device-powering experience. Although room-scale WPT based on multimode quasistatic cavity resonance (QSCR) has demonstrated full-volume coverage by leveraging multiple resonant modes, existing high-coverage implementations require obstructive internal conductive structures, such as a central pole. This letter presents a new structure, termed the patched-wall QSCR, that eliminates such internal obstructions while preserving full-volume coverage. By using conductive wall segments interconnected by capacitors, the proposed structure supports two complementary resonant modes that cover both the peripheral and central regions without obstructions within the charging volume. Electromagnetic simulations show that, by selectively exciting these two resonant modes, the proposed structure achieves a minimum power-transfer efficiency of 48.1

A recyclable and cuttable wireless power transfer (WPT) sheet is proposed, enabled by H-tree wiring and water-soluble channels filled with liquid metal (LM). Conventional 2D WPT systems lose their functionality when physically damaged or modified. The H-tree wiring pattern maintains the operation of the remaining coils even after the outer region of the sheet is cut away. The LM can be recovered by dissolving 3D-printed polyvinyl alcohol (PVA) channels in water. The sheet dimensions were experimentally optimized, and a Q-factor over 55 was achieved at 6.78 MHz. The sheet maintained its bending stiffness and electrical resistance during 100 bending cycles. After four dissolution-refabrication cycles, 98 percent of the LM was recovered with stable electrical properties. The WPT sheet can be integrated into everyday objects and enables long-term, continuous operation of surrounding electronic devices, contributing to IoT applications and ambient computing.

Abstract Textile-integrated meander coils offer a promising platform for body-scale wireless powering of wearable devices. Liquid metal (LM)-filled stretchable tubes are ideal wiring for these coils given their high stretchability and conductivity. Efficient wireless power transfer requires integrating rigid capacitors into this stretchable wiring. However, conventional rigid–soft interconnections using adhesion-based chemical fixation remain mechanically fragile due to poor adhesion in stretchable materials. To address this, we propose capsule-assisted, friction-based mechanical fixation. Rigid capsule-covered capacitors are inserted into LM-filled tubes and secured via friction. These capsules increase contact area and pressure for robust mechanical fixation during stretching while covering capacitor edges to minimize stress concentration during bending. By experimentally optimizing capsule dimensions, our friction-based interconnection withstood stretching and bending forces of 30.3 ± 0.9 and 25.6 ± 2.0 N, respectively, outperforming conventional adhesive joints by 2.7 and 3.4 times. Applied to a body-scale meander coil as a proof of concept, this interconnection maintained wireless power for multiple light-emitting diode (LED) and sensor devices. The coil demonstrated stable electrical performance, yielding impedance changes of <1.7% after 100 bending cycles and <3.1% after 100 washing cycles. This method enables washable, durable wireless powering textiles to support battery- and wiring-free wearable sensors in everyday clothing.

Topology optimization (TO) is employed in engineering to optimize structural performance while maximizing material efficiency. However, traditional TO methods incur significant computational and time costs. Although research has leveraged generative AI to predict TO outcomes and validated feasibility and accuracy, existing approaches still suffer from limited customizability and impose a high cognitive load on users. Furthermore, balancing structural performance with aesthetic attributes remains a persistent challenge. We developed Sketch2Topo, which augments a diffusion-based TO model with image-to-image generation and image editing capabilities. With Sketch2Topo, users can use sketching to customize geometries and specify physical constraints. The tool also supports mask input, enabling users to perform TO on selected regions only, thereby supporting higher levels of customization. We summarize the workflow and details of the tool and conduct a brief quantitative evaluation. Finally, we explore application scenarios and discuss how hand-drawn input improves usability while balancing functionality and aesthetics.

Air-dispersed sensor networks deployed from aerial robotic systems (e.g., UAVs) provide a low-cost approach to wide-area environmental monitoring. However, existing methods often rely on active actuators for mid-air shape or trajectory control, increasing both power consumption and system cost. Here, we introduce a passive elastic-folding hinge mechanism that transforms sensors from a flat, stackable form into a three-dimensional structure upon release. Hinges are fabricated by laminating commercial sheet materials with rigid printed circuit boards (PCBs) and programming fold angles through a single oven-heating step, enabling scalable production without specialized equipment. Our geometric model links laminate geometry, hinge mechanics, and resulting fold angle, providing a predictive design methodology for target configurations. Laboratory tests confirmed fold angles between 10 deg and 100 deg, with a standard deviation of 4 deg and high repeatability. Field trials further demonstrated reliable data collection and LoRa transmission during dispersion, while the Horizontal Wind Model (HWM)-based trajectory simulations indicated strong potential for wide-area sensing exceeding 10 km.

Gaze provides valuable insights into people's interests and can facilitate seamless interaction with computational systems. However, capturing natural gaze during dynamic motions with compact, low-cost, low-power wearables like earbuds is challenging. While gaze estimation using inertial measurement units (IMUs) has been explored, it typically requires the user to remain still or relies on additional data such as posture, limiting its use in various scenarios. In this work, we propose HeadGaze, which estimates gaze also in walking postures only from a single IMU inside an earbud with a long short-term memory (LSTM) network. We hypothesized that the acceleration caused by walking can be utilized to estimate the head orientation. To estimate gaze from head orientation, we obtained the linear head-gaze relationship coefficients. Our user study, in which participants looked freely in various postures, showed that our method estimated gaze relative to the body with an error of 12.9 degrees in the yaw direction and 10.2 degrees in the pitch direction. HeadGaze enables gaze estimation using a single IMU inside a wearable device, such as earbuds and smartglasses worn on the head, making gaze interaction techniques available on various devices. Moreover, our method essentially requires only earbuds and a smartphone for user-specific data collection without requiring special equipment such as eye trackers.

ABSTRACT This paper addresses the short battery lifespan of wireless mouse rings by introducing the picoRing mouse . We develop a near‐field sensitive inductive telemetry, which allows a ring coil to send its data to a nearby wristband coil in an ultra‐low‐power way. This approach drastically extends the ring's operation time from a few hours to over 200 h on a 27 mAh battery.

In smart homes and offices, the deployment of autonomous mobile robots is accelerating. Accurate self-localization is essential for these robots; however, existing approaches still struggle to achieve reliable absolute positioning in everyday environments. SLAM is indispensable for local navigation but suffers from drift, degraded performance in feature-poor spaces, and cannot provide absolute coordinates, while emerging single-anchor methods often require specialized hardware like UWB. To address these limitations and complement SLAM with an absolute global constraint, we propose a ubiquitous localization method leveraging acoustic TDoA from a single speaker via floor reflections. This study investigates whether the weak geometric constraints derived solely from floor reflections can enable accurate localization when fused with odometry via a Particle Filter. Real-world experiments demonstrate a converged accuracy of 0.18 m. By utilizing everyday audio infrastructure, this approach significantly lowers deployment barriers for service robots.

Topology optimization(TO) is widely used in engineering because of its ability to save material and optimize structural performance. Although prior work has explored 2D human-centered design tool for TO, the results are often limited in variety and offer weak customizability. Meanwhile, due to the high computational and time costs of TO, researchers have attempted to address these issues using generative AI; however, such methods often provide limited interactivity. In addition, topology optimization in many cases needs to balance structural performance and aesthetic qualities through iterative design, a perspective that has rarely been emphasized in traditional TO. We present TopoStyle, an iterative design tool for 2.5D topology optimization using a 2D diffusion model. We explore two interaction methods. The first exports 3D parts to a graphical interface for hand-drawn interaction. The second enables direct interaction within 3D modeling software using points. Our tool also supports the use of masks to apply topology optimization to specific regions, allowing users to address customized design needs. We compare and evaluate both performance and interaction methods, and investigate how TopoStyle can balance performance and aesthetics while improving design efficiency through customization and iterative design. Finally, we demonstrate the application scenarios of TopoStyle through several design cases.

Electronic textiles (e-textiles) integrated with wearable sensors are essential for daily motion monitoring and long-term physiological sensing. For example, capturing optimal kinematic or bio-signals requires aligning sensors with specific anatomical parts, which vary significantly across individuals and application scenarios. This necessity for personalization makes e-textile prototyping inherently iterative, however current fabrication methods, such as manual conductive stitching, rely on permanent bonds that restrict rapid adjustment. This paper introduces Plug-n-play e-knit, a machine-knittable e-textile prototyping platform that enables repeatable, quick adjustment of sensor positions across garments. First, to cover the large area of the textile for prototyping, we use industrial digital knitting of conductive yarn to integrate power and communication buses directly into the large-scale textile. Then, to ensure plug-n-play attachment to the textile, we employ soft-magnetic connectors that enable sensors to be repeatedly plugged into the wiring without damaging the fabric. Furthermore, our LED-positioning system enables the automatic identification and localization of each sensor node. We demonstrate the platform's capabilities through forearm movement calibration and position-aware temperature mapping.

Topology optimization (TO) is employed in engineering to optimize structural performance while maximizing material efficiency. However, traditional TO methods incur significant computational and time costs. Although research has leveraged generative AI to predict TO outcomes and validated feasibility and accuracy, existing approaches still suffer from limited customizability and impose a high cognitive load on users. Furthermore, balancing structural performance with aesthetic attributes remains a persistent challenge. We developed Sketch2Topo, which augments a diffusion-based TO model with image-to-image generation and image editing capabilities. With Sketch2Topo, users can use sketching to customize geometries and specify physical constraints. The tool also supports mask input, enabling users to perform TO on selected regions only, thereby supporting higher levels of customization. We summarize the workflow and details of the tool and conduct a brief quantitative evaluation. Finally, we explore application scenarios and discuss how hand-drawn input improves usability while balancing functionality and aesthetics.

We present Full-body WPT, wireless power networking around the human body using a meandered textile coil. Unlike traditional inductive systems that emit strong fields into the deep tissue inside the body, the meander coil enables localized generation of strong magnetic field constrained to the skin surface, even when scaled to the size of the human body. Such localized inductive system enhances both safety and efficiency of wireless power around the body. Furthermore, the use of low-loss conductive yarn achieve energy-efficient and lightweight design. We analyze the performance of our design through simulations and experimental prototypes, demonstrating high power transfer efficiency and adaptability to user movement and posture. Our system provides a safe and efficient distributed power network using meandered textile coils integrated into wearable materials, highlighting the potential of body-centric wireless power networking as a foundational layer for ubiquitous health monitoring, augmented reality, and human-machine interaction systems.

The growing use of wearable devices for activity tracking, healthcare, and haptics faces challenges due to the bulkiness and short lifespan of batteries. Integration of a textile-based wireless charging and readout system into everyday clothing can enable seamless power supply and data collection around the body. However, expanding such system to cover the entire body is challenging, as it increases electromagnetic interference with the body, degrading the performance of wireless system. This article introduces a meandered textile coil designed for body-scale, efficient wireless charging and readout. The meander coil can confine a strong inductive field near the body surface, ensuring W-class safe charging and sensitive readout with uW-class low power. Moreover, its zigzag design is simple enough for mass production on industrial knitting machines. Therefore, the body-scale meander coil can continuously operate battery-free wearable devices across the body, leading to ubiquitous deployment of continuous full-body wearable computing into everyday clothing.