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With the evaluation of a next-generation human–machine interface (HMI) concept for excavators, this study aims to discuss the HMI quality measurement based on usability and user experience (UUX) metrics. Regarding the digital transformation of construction sites, future work environments will have to be capable of presenting various complex visual data and enabling efficient and safe interactivity while working. The evaluated HMI focused on introducing a touch display-based interface, providing advanced operation functions and different interaction modalities. The assessment of UUX should show whether the novel HMI can be utilised to perform typical tasks (usability) and how it is accepted and assessed in terms of non-instrumental qualities (user experience, UX). Using the collected data, this article also aims to contribute to the general discussion about the role of UX beyond usability in industrial applications and deepen the understanding of non-instrumental qualities when it comes to user-oriented process and machine design. The exploratory study examines insights into the application of elaborated UUX measuring tools like the User Experience Questionnaire (UEQ) on the interaction with industrial goods accompanied by their rating with other tools, namely System Usability Scale (SUS), Intuitive Interaction Questionnaire (INTUI) and the National Aeronautics and Space Administration (NASA) Task Load Index (NASA-TLX). Four goals are pursued in this study. The first goal is to compare in-depth two different ways of interaction with the novel HMI—namely one by a control pad on the right joystick and one by touch. Therefore, a sample of 17 subjects in total was split into two groups and differences in UUX measures were tested. Secondly, the performances of both groups were tested over the course of trials to investigate possible differences in detail. The third goal is to interpret measures of usability and user experience against existing benchmark values. Fourth and finally, we use the data gathered to analyse correlations between measures of UUX. The results of our study show that the different ways of interaction did not impact any of the measures taken. In terms of detailed performance analysis, both groups yielded differences in terms of time per action, but not between the groups. The comparison of UUX measures with benchmark values yielded mixed results. The UUX measures show some relevant significant correlations. The participants mostly reported enjoying the use of the HMI concept, but several practical issues (e.g., efficiency) still need to be overcome. Once again, the study confirms the urge of user inclusion in product development. Especially in the course of digitalisation, as big scale advancements of systems and user interfaces bring uncertainty for many manufacturers regarding whether or how a feature should be integrated.

Advancements of virtual reality technology pave the way for developing wearable devices to enable somatosensory sensation, which can bring more comprehensive perception and feedback in the metaverse-based virtual society. Here, we propose augmented tactile-perception and haptic-feedback rings with multimodal sensing and feedback capabilities. This highly integrated ring consists of triboelectric and pyroelectric sensors for tactile and temperature perception, and vibrators and nichrome heaters for vibro- and thermo-haptic feedback. All these components integrated on the ring can be directly driven by a custom wireless platform of low power consumption for wearable/portable scenarios. With voltage integration processing, high-resolution continuous finger motion tracking is achieved via the triboelectric tactile sensor, which also contributes to superior performance in gesture/object recognition with artificial intelligence analysis. By fusing the multimodal sensing and feedback functions, an interactive metaverse platform with cross-space perception capability is successfully achieved, giving people a face-to-face like immersive virtual social experience. Current wearable solutions for Virtual Reality (VR) have limitations of complicated structures and large driven power. Here, the authors report a highly integrated ring consisting of multimodal sensing and feedback units for augmented interactions in metaverse.

Stretchable ionic skins are intriguing in mimicking the versatile sensations of natural skins. However, for their applications in advanced electronics, good elastic recovery, self-healing, and more importantly, skin-like nonlinear mechanoresponse (strain-stiffening) are essential but can be rarely met in one material. Here we demonstrate a robust proton-conductive ionic skin design via introducing an entropy-driven supramolecular zwitterionic reorganizable network to the hydrogen-bonded polycarboxylic acid network. The design allows two dynamic networks with distinct interacting strength to sequentially debond with stretch, and the conflict among elasticity, self-healing, and strain-stiffening can be thus defeated. The representative polyacrylic acid/betaine elastomer exhibits high stretchability (1600% elongation), immense strain-stiffening (24-fold modulus enhancement), ~100% self-healing, excellent elasticity (97.9 ± 1.1% recovery ratio, <14% hysteresis), high transparency (99.7 ± 0.1%), moisture-preserving, anti-freezing (elastic at −40 °C), water reprocessibility, as well as easy-to-peel adhesion. The combined advantages make the present ionic elastomer very promising in wearable iontronic sensors for human-machine interfacing. Ionic skins are of interest for a range of electronic, sensing and interfacing applications but often have trade-offs in properties. Here, the authors report on the creation of a dual network ionic skin using a supramolecular zwitterionic competing network to produce a strain-stiffening, self-healing adhesive sensor.

Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for traditional systems. Here, we propose a highly efficient neuromorphic physiological signal processing system based on VO 2 memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO 2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO 2 memristors is utilized in compact Leaky Integrate and Fire (LIF) and Adaptive-LIF (ALIF) neurons, which are incorporated into a decision-making Long short-term memory Spiking Neural Network. The system demonstrates superior computing capabilities, needing only small-sized LSNNs to attain high accuracies of 95.83% and 99.79% in arrhythmia classification and epileptic seizure detection, respectively. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces. Next-generation human-machine interfaces require efficient physiological signal processing systems. Here, the authors propose a hardware system that uses VO 2 memristors to perform brain-like encoding and analysis of physiological signals, and is capable of identifying arrhythmia and epileptic seizures.

Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO 2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO 2 memristor including endurance (>10 12 ), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces. Constructing crossmodal in-sensor processing system based on high-performance flexible devices is important for the development of wearable human-machine interfaces. This work reports a bio-inspired spiking sensory neuron based on a flexible VO2 memristor and demonstrates a crossmodal in-sensor encoding and computing system.

Endowing textiles with perceptual function, similar to human skin, is crucial for the development of next-generation smart wearables. To date, the creation of perceptual textiles capable of sensing potential dangers and accurately pinpointing finger touch remains elusive. In this study, we present the design and fabrication of intelligent perceptual textiles capable of electrically responding to external dangers and precisely detecting human touch, based on conductive silk fibroin-based ionic hydrogel (SIH) fibers. These fibers possess excellent fracture strength (55 MPa), extensibility (530%), stable and good conductivity (0.45 S·m –1 ) due to oriented structures and ionic incorporation. We fabricated SIH fiber-based protective textiles that can respond to fire, water, and sharp objects, protecting robots from potential injuries. Additionally, we designed perceptual textiles that can specifically pinpoint finger touch, serving as convenient human-machine interfaces. Our work sheds new light on the design of next-generation smart wearables and the reshaping of human-machine interfaces. Perception plays a pivotal role in advancing future intelligent textiles. Here, the authors develop smart perceptual textiles using natural-derived ionic-conductive silk fibers. These textiles can electrically detect external hazards and precisely pinpointing human touch, making them suitable for smart protective clothing and soft human-machine interfaces.

Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating principles fundamentally different from the ion signal modulation of biology, traditional Silicon-based neuromorphic implementations have limited bio-integration potential. Here, we report the first organic electrochemical neurons (OECNs) with ion-modulated spiking, based on all-printed complementary organic electrochemical transistors. We demonstrate facile bio-integration of OECNs with Venus Flytrap ( Dionaea muscipula ) to induce lobe closure upon input stimuli. The OECNs can also be integrated with all-printed organic electrochemical synapses (OECSs), exhibiting short-term plasticity with paired-pulse facilitation and long-term plasticity with retention >1000 s, facilitating Hebbian learning. These soft and flexible OECNs operate below 0.6 V and respond to multiple stimuli, defining a new vista for localized artificial neuronal systems possible to integrate with bio-signaling systems of plants, invertebrates, and vertebrates. The integration of artificial neuromorphic devices with biological systems plays a fundamental role for future brain-machine interfaces, prosthetics, and intelligent soft robotics. Harikesh et al. demonstrate all-printed organic electrochemical neurons on Venus flytrap that is controlled to open and close.

Combining traditional textiles with triboelectric nanogenerators (TENGs) gives birth to self-powered electronic textiles (e-textiles). However, there are two bottlenecks in their widespread application, low power output and poor sensing capability. Herein, by means of the three-dimensional five-directional braided (3DB) structure, a TENG-based e-textile with the features of high flexibility, shape adaptability, structural integrity, cyclic washability, and superior mechanical stability, is designed for power and sensing. Due to the spatial frame-column structure formed between the outer braided yarn and inner axial yarn, the 3DB-TENG is also endowed with high compression resilience, enhanced power output, improved pressure sensitivity, and vibrational energy harvesting ability, which can power miniature wearable electronics and respond to tiny weight variations. Furthermore, an intelligent shoe and an identity recognition carpet are demonstrated to verify its performance. This study hopes to provide a new design concept for high-performance textile-based TENGs and expand their application scope in human-machine interfacing. Low power output and poor sensing ability are bottlenecks for the practical application of fabric-based triboelectric nanogenerators (TENGs). The authors develop a shape adaptable and highly resilient 3D braided TENG, which is endowed with enhanced power output and improved pressure sensitivity.

In daily life, people use many appliances, where different machines and tools should be operated with their specialized interfaces. These specialized interfaces are often not intuitive and thus require considerable time and effort to master. On the other hand, human communications are rich in modalities and mostly intuitive. One of them is eye contact. This study proposes eye contact for enriching modalities for human-machine interface. The proposed interface modality, based on a neural network for object detection, allows humans to initiate machine operations by looking at them. In this paper, the hardware framework for building this interface is elaborated and the results of usability assessment through users’ experiments are reported.

Accurate and imperceptible monitoring of electrophysiological signals is of primary importance for wearable healthcare. Stiff and bulky pregelled electrodes are now commonly used in clinical diagnosis, causing severe discomfort to users for long-time using as well as artifact signals in motion. Here, we report a ~100 nm ultra-thin dry epidermal electrode that is able to conformably adhere to skin and accurately measure electrophysiological signals. It showed low sheet resistance (~24 Ω/sq, 4142 S/cm), high transparency, and mechano-electrical stability. The enhanced optoelectronic performance was due to the synergistic effect between graphene and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), which induced a high degree of molecular ordering on PEDOT and charge transfer on graphene by strong π-π interaction. Together with ultra-thin nature, this dry epidermal electrode is able to accurately monitor electrophysiological signals such as facial skin and brain activity with low-motion artifact, enabling human-machine interfacing and long-time mental/physical health monitoring. Novel dry electrodes with enhanced mechano-electrical stability and conformability are attractive for long-time electrophysiological signal monitoring. Here, the authors report polymer-covered CVD-grown graphene with enhanced optoelectronic performance for biopotential monitoring.