Shared textiles used in public spaces (transportation, theaters, etc.) are notoriously difficult and expensive to clean, resulting in infrequent cleanings. To address these challenges, we developed a light-diffusing textile system with embedded LEDs that can self-clean, i.e., degrade organic stains and inhibit bacterial growth. Our self-cleaning textile system can be triggered at any time, bypassing the need for additional chemicals and manual cleaning. Specifically, we tested commercial titanium dioxide nanoparticles doped with nitrogen on polyester, applied four different ways (painting, rolling, spraying, and dip coating) with two different curing methods (7 days at room temperature and 10 min at elevated temperatures) and different textile surface pretreatments (no pretreatment, corona discharge, commercial primer, and corona discharge with commercial primer), and compared their efficacies using stain degradation testing and EDS analysis to arrive at the best performing textile cover. We then integrated LED fibers into this cover to create a self-cleaning system capable of autonomously cleaning the textile on demand. We evaluated our system through stain degradation experiments and bacterial testing, demonstrating that stain degradation increases linearly with the duration of exposure and LED intensity. Our results verify that our textile system effectively destains the textile in both dark and bright ambient conditions within 2 days and inactivates both Gram-positive and Gram-negative bacteria up to 99% after 8 h of exposure. Finally, we stack LED fibers with reflective and diffusive textile layers for modularity and programmability. This self-cleaning system can be seamlessly integrated into public environments such as automotive or theater seatings, offering enhanced hygiene and cleanliness without compromising comfort.

Since their discovery in 2011, MXenes, two-dimensional transition metal carbides and nitrides, have emerged as highly promising materials for smart textile applications. They offer exceptional properties such as high electrical conductivity, optical tunability, and mechanical flexibility. These materials can also be produced at scale and readily solution-processed into textile formats, fueling a surge of interest in integrating MXenes into various smart textile applications, from strain sensors and wearable biosensors to adaptive thermal management and electromagnetic interference (EMI) shielding. However, despite this rapid growth, existing reviews of MXene-enabled smart textiles remain narrow in scope, often focusing on single fabrication methods or specific functionalities. Such a fragmented perspective makes it difficult for researchers to gain a comprehensive understanding of how the field has evolved and where it is headed. In response, we present a quantitative bibliographic analysis of MXene–textile research from 2017 through 2024, encompassing nearly 1000 publications. This review categorizes the literature by major functional domains (sensing, energy storage/harvesting, EMI shielding, and heating) and examines their shifts over time, providing reasons and examples for these changes in research interest. Additionally, detailed analyses of functions in each category were conducted in a similar fashion. Our holistic, data-driven assessment offers guidance for future research and commercialization of MXene-functionalized smart textiles by identifying high-impact areas, emerging opportunities, and critical gaps.

Fabric has been a fundamental part of human life for thousands of years, providing comfort, protection, and aesthetic expression. While modern advancements have enhanced fabric's functionality, it remains static and unchangeable, failing to adapt to our evolving body shapes and preferences. This lack of adaptability can lead to unsustainable practices, as consumers often buy more items to meet their changing needs. In this paper, we propose ExoFabric, a re-moldable fabric system for customized soft goods applications. We created ExoFabric by embedding thermoplastic threads into fabric through computerized embroidery to allow for tunability between rigid plastic and conformable fabric. We defined a library of design primitives to enable geometric formability, stiffness, and stretchability by identifying suitable fabrics, threads, embroidery parameters, and machine limitations. To facilitate practical applications, we demonstrated practical methods for linking parameters to application requirements, showcasing form-fitting wearables, structural support, and shape-changeable furniture for repeatable or one-time customization.

Neuromorphic accelerators offer promising platforms for machine learning (ML) inference by leveraging event-driven, spatially-expanded architectures that naturally exploit unstructured sparsity through co-located memory and compute. However, their unique architectural characteristics create performance dynamics that differ fundamentally from conventional accelerators. Existing workload optimization approaches for neuromorphic accelerators rely on aggregate network-wide sparsity and operation counting, but the extent to which these metrics actually improve deployed performance remains unknown. This paper presents the first comprehensive performance bound and bottleneck analysis of neuromorphic accelerators, revealing the shortcomings of the conventional metrics and offering an understanding of what facets matter for workload performance. We present both theoretical analytical modeling and extensive empirical characterization of three real neuromorphic accelerators: Brainchip AKD1000, Synsense Speck, and Intel Loihi 2. From these, we establish three distinct accelerator bottleneck states, memory-bound, compute-bound, and traffic-bound, and identify which workload configuration features are likely to exhibit these bottleneck states. We synthesize all of our insights into the floorline performance model, a visual model that identifies performance bounds and informs how to optimize a given workload, based on its position on the model. Finally, we present an optimization methodology that combines sparsity-aware training with floorline-informed partitioning. Our methodology achieves substantial performance improvements at iso-accuracy: up to 3.86x runtime improvement and 3.38x energy reduction compared to prior manually-tuned configurations.

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we outline tasks and guidelines for benchmarks across multiple application domains, and present initial performance baselines across neuromorphic and conventional approaches for both benchmark tracks. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.

Neuromorphic technology offers advantages such as low-power processing, low latency, adaptive learning, and noise tolerance, making it ideal for edge computing applications. However, developers face significant hurdles due to the nascent nature of the field, including limited access to hardware and software, lack of benchmarks, and the need for deep interdisciplinary knowledge. Through interviews with 12 practitioners from both industry and academia, we conducted a thematic analysis to understand the current landscape of neuromorphic programming and identified key challenges, workflows, and potential solutions for enhancing accessibility and adoption. Our findings led to a set of guidelines for creating more accessible software development tools and platforms for those looking to create neuromorphic applications. Through this work, we aim to bridge the gap between neuromorphic computing and the HCI community, promoting the design of more intuitive and effective interfaces for neuromorphic development, and ultimately facilitating the creation of edge intelligent systems.

Analysing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one major problem is the computational challenge of the inference procedure, involving a combinatorial search across object identities and poses. Here we propose a neuromorphic solution exploiting three key concepts: (1) a computational framework based on vector symbolic architectures (VSAs) with complex-valued vectors, (2) the design of hierarchical resonator networks to factorize the non-commutative transforms translation and rotation in visual scenes and (3) the design of a multi-compartment spiking phasor neuron model for implementing complex-valued resonator networks on neuromorphic hardware. The VSA framework uses vector binding operations to form a generative image model in which binding acts as the equivariant operation for geometric transformations. A scene can therefore be described as a sum of vector products, which can then be efficiently factorized by a resonator network to infer objects and their poses. The hierarchical resonator network features a partitioned architecture in which vector binding is equivariant for horizontal and vertical translation within one partition and for rotation and scaling within the other partition. The spiking neuron model allows mapping the resonator network onto efficient and low-power neuromorphic hardware. Our approach is demonstrated on synthetic scenes composed of simple two-dimensional shapes undergoing rigid geometric transformations and colour changes. A companion paper demonstrates the same approach in real-world application scenarios for machine vision and robotics. The inference procedure for analysing a visual scene presents a computational challenge. Renner, Supic and colleagues develop a neural network model, the hierarchical resonator, to determine the generative factors of variation of objects in simple scenes. The resonator was implemented on neuromorphic hardware, using a spike-timing code for complex numbers.

Sustainability is an increasingly pressing global concern that demands particular attention when it comes to how our products and infrastructural components are built, used, and treated at end-of-life. Material innovation plays an important role in designing for sustainability—from raw material selection to integration into products and systems, and finally ending at disposal. In this chapter, we present a vision of how smart materials, and technologies like additive manufacturing and transient electronics, can enable the transition from a linear consumption model to a circular one. By presenting a variety of R&D projects that aim to create environmentally conscious consumer goods, clothing, and natural and built environments, we illustrate how sustainability and smart functionality can be complementary forces, rather than antagonistic ones.

Visual odometry (VO) is a method used to estimate self-motion of a mobile robot using visual sensors. Unlike odometry based on integrating differential measurements that can accumulate errors, such as inertial sensors or wheel encoders, VO is not compromised by drift. However, image-based VO is computationally demanding, limiting its application in use cases with low-latency, low-memory and low-energy requirements. Neuromorphic hardware offers low-power solutions to many vision and artificial intelligence problems, but designing such solutions is complicated and often has to be assembled from scratch. Here we propose the use of vector symbolic architecture (VSA) as an abstraction layer to design algorithms compatible with neuromorphic hardware. Building from a VSA model for scene analysis, described in our companion paper, we present a modular neuromorphic algorithm that achieves state-of-the-art performance on two-dimensional VO tasks. Specifically, the proposed algorithm stores and updates a working memory of the presented visual environment. Based on this working memory, a resonator network estimates the changing location and orientation of the camera. We experimentally validate the neuromorphic VSA-based approach to VO with two benchmarks: one based on an event-camera dataset and the other in a dynamic scene with a robotic task. Visual odometry, or self-motion estimation, is a fundamental task in robotics. Renner, Supic and colleagues introduce a neuromorphic algorithm for visual odometry that leverages hyperdimensional computing and hierarchical resonators. The approach estimates a robot's motion from event-based vision, a step towards low-power machine vision for robotics.

Plant life plays a critical role in the ecosystem. However, it is difficult for humans to perceive plants' reactions because the biopotential and biochemical responses are invisible to humans. Guttation droplets contain various chemicals which can reflect plant physiology and environmental conditions in real-time. Traditionally, these droplets are collected manually and analyzed in the lab with expensive instruments. Here, we introduce the Guttation Monitor, an on-site and low-cost monitoring technology for guttation droplets. It consists of three parts 1) a paper-based microfluidic chip that can collect guttation droplets and perform colorimetric detection of six chemicals, 2) a self-contained and solar-powered camera module that can capture the result from the chip, and 3) an end-user app that can interpret the result. We discuss this technology's design and implementation, conduct evaluations on tomato plants, conduct interviews, and envision how such a technology could enhance the human-plant relationship in four dimensions.

Wearables have long been integral to human culture and daily life. Recent advances in intelligent soft wearables have dramatically transformed how we interact with the world, enhancing our health, productivity, and overall well-being. These innovations, combining advanced sensor design, fabrication, and computational power, ofer unprecedented opportunities for monitoring, assistance, and augmentation. However, the benefts of these advancements are not yet universally accessible. Economic and technical barriers often limit the reach of these technologies to domain-specifc experts. There is a growing need for democratizing intelligent wearables that are scalable, seamlessly integrated, customized, and adaptive. By bringing researchers from relevant disciplines together, this workshop aims to identify the challenges and investigate opportunities for democratizing intelligent soft wearables within the HCI community via interactive demos, invited keynotes, and focused panel discussions.

Navigation of mobile agents in unknown, unmapped environments is a critical task for achieving general autonomy. Recent advancements in combining Reinforcement Learning with Deep Neural Networks have shown promising results in addressing this challenge. However, the inherent complexity of these approaches, characterized by multi-layer networks and intricate reward objectives, limits their autonomy, increases memory footprint, and complicates adaptation to energy-efficient edge hardware. To overcome these challenges, we propose a brain-inspired method that employs a shallow architecture trained by a local learning rule for self-supervised navigation in uncharted environments. Our approach achieves performance comparable to a state-of-the-art Deep Q Network (DQN) method with respect to goal-reaching accuracy and path length, with a similar (slightly lower) number of parameters, operations, and training iterations. Notably, our self-supervised approach combines novelty-based and random walks to alleviate the need for objective reward definition and enhance agent autonomy. At the same time, the shallow architecture and local learning rule do not call for error backpropagation, decreasing the memory overhead and enabling implementation on edge neuromorphic processors. These results contribute to the potential of embodied neuromorphic agents utilizing minimal resources while effectively handling variability.

The merging of electronic materials and textiles has triggered the proliferation of wearables and interactive surfaces in the ubiquitous computing era. However, this leads to e-textile waste that is difficult to recycle and decompose. Instead, we demonstrate an eco-design approach to upcycle waste cotton fabrics into functional textile elements through carbonization without the need for additional materials. We identify optimal parameters for the carbonization process and develop encapsulation techniques to improve the response, durability, and washability of the carbonized textiles. We then configure these e-textiles into various 'design primitives' including sensors, interconnects, and heating elements, and evaluate their electromechanical properties against commercially available e-textiles. Using these primitives, we demonstrate several applications, including a haptic-transfer fabric, a joint-sensing wearable, and an intelligent sailcloth. Finally, we highlight how the sensors can be composted, re-carbonized and coated onto other fabrics, or repurposed into different sensors towards their end-of-life to promote a circular manufacturing process.

As the Internet of Things (IoT) expands, electronics will take on new form factors. With the ubiquity of textiles in our daily lives, integrating functionality into them is a promising proposition. Realizing a future with textile-based electronics (e-textiles) will require on-textile power supplies. Due to their high conductivity, electrochemically active surface, and ability to produce additive-free coatings from aqueous inks, MXenes are an ideal material to integrate into textiles to add functionality as well as generate and store electrical energy. Herein, we demonstrate an on-garment energy grid utilizing MXenes in textile-based supercapacitors and wireless chargers. Our on-garment energy grid can power real-world electronics, including peripheral electronics performing environmental sensing and data transmission, including an all-MXene surface electromyography (sEMG) sensor with real-time data transmission. Finally, we create a fully wireless textile-MXene joule heater directly powered by our MXene coil.

In light of the HCI community’s growing alignment with Sustainable HCI (SHCI) and the awareness of its currently narrow focus. We propose Ecological HCI (EHCI). EHCI highlights emerging, nature-centric research efforts and aims to expand SHCI’s scope to encompass a broader range of Sustainable Development Goals set by the United Nations [16]. It focuses on understanding the complex interplay between technology, human activities, and the natural environment, and redefining HCI’s role in promoting ecological well-being. This special interest group will gather researchers to discuss key questions in EHCI’s development, focusing on refining its vision, positioning within HCI, technical approaches, design strategies, evaluation methods and long-term impact.

Aerial seeding can quickly cover large and physically inaccessible areas1 to improve soil quality and scavenge residual nitrogen in agriculture2, and for postfire reforestation3–5 and wildland restoration6,7. However, it suffers from low germination rates, due to the direct exposure of unburied seeds to harsh sunlight, wind and granivorous birds, as well as undesirable air humidity and temperature1,8,9. Here, inspired by Erodium seeds10–14, we design and fabricate self-drilling seed carriers, turning wood veneer into highly stiff (about 4.9 GPa when dry, and about 1.3 GPa when wet) and hygromorphic bending or coiling actuators with an extremely large bending curvature (1,854 m−1), 45 times larger than the values in the literature15–18. Our three-tailed carrier has an 80% drilling success rate on flat land after two triggering cycles, due to the beneficial resting angle (25°–30°) of its tail anchoring, whereas the natural Erodium seed’s success rate is 0%. Our carriers can carry payloads of various sizes and contents including biofertilizers and plant seeds as large as those of whitebark pine, which are about 11 mm in length and about 72 mg. We compare data from experiments and numerical simulation to elucidate the curvature transformation and actuation mechanisms to guide the design and optimization of the seed carriers. Our system will improve the effectiveness of aerial seeding to relieve agricultural and environmental stresses, and has potential applications in energy harvesting, soft robotics and sustainable buildings. A study describes a wood-based, three-tailed, biodegradable seed carrier that self-drills into the ground in response to moisture fluctuations with a success rate higher than that of natural self-drilling seeds.

As voice assistant usage continues to grow, their homogeneity becomes even more problematic with the UNESCO report, "I'd Blush if I could" showing that designing only feminine voice assistants encourages negative behavior, both with virtual assistants and with real people [3]. While masculine text-to-speech (TTS) voices exist, ones that cover the full range of gender presentations, such as non-binary or gender-ambiguous voices are largely missing. In this paper, we present a method of creating a non-binary TTS voice and an example voice, Sam, created with input from the non-binary and transgender communities. We have open-sourced the resulting voice, along with the process and data used to create it. Finally, we present results from a large-scale survey showing that non-binary individuals are more likely to prefer a non-binary voice assistant compared to cisgendered individuals and discuss differences across age and gender.

We introduce MechSense, 3D-printed rotary encoders that can be fabricated in one pass alongside rotational mechanisms, and report on their angular position, direction of rotation, and speed. MechSense encoders utilize capacitive sensing by integrating a floating capacitor into the rotating element and three capacitive sensor patches in the stationary part of the mechanism. Unlike existing rotary encoders, MechSense does not require manual assembly but can be seamlessly integrated during design and fabrication. Our MechSense editor allows users to integrate the encoder with a rotating mechanism and exports files for 3D-printing. We contribute a sensor topology and a computational model that can compensate for print deviations. Our technical evaluation shows that MechSense can detect the angular position (mean error: 1.4°) across multiple prints and rotations, different spacing between sensor patches, and different sizes of sensors. We demonstrate MechSense through three application examples on 3D-printed tools, tangible UIs, and gearboxes.

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics.