Human-machine Interaction Research Articles

A human visual attention analysis model for remote interaction interface of unmanned agricultural vehicles

When unmanned agricultural vehicles (UAVs) encounter unexpected failures or incidents during autonomous operations, a highly efficient remote human–machine interaction (HMI) interface is needed to assist in the manual joint control process. The highly complex cognitive mechanisms and visual characteristics of humans make it difficult to design efficient human–machine interfaces. Therefore, a human visual attention analysis model was proposed based on fully convolutional networks for the remote interaction interface of unmanned agricultural vehicles. First, visual attention experimental software combined with gaze-contingent real-time simulation methods was developed to build the dataset. Subsequently, the human visual attention analysis model (HVAAM) is constructed, and the corresponding training strategy is proposed, which is trained on the ILSVRC2012 standard and self-constructed datasets. The accuracy of the HVAAM analysis on the test dataset was validated. Ultimately, a human–machine interaction test of random failure warning for remote monitoring interface of unmanned plowing operation is performed with a self-developed unmanned electric tractor as the carrier. In the test dataset, the average analysis accuracy of HVAAM was 79.2 %, and the proposed training strategy led to a 20.7 % improvement in the model accuracy. In the unmanned electric tractor human–machine interaction test, HVAAM achieved 77.9 % accuracy in the visual attention analysis of the monitoring interface. This study can effectively facilitate the analysis, design, and optimization of remote monitoring interfaces for unmanned agricultural vehicles.

Self-Adhesive Electronic Skin with Bio-Inspired 3D Architecture for Mechanical Stimuli Monitoring and Human-Machine Interactions.

Recent development of wearable devices is revolutionizing the way of artificial electronic skins (E-skin), physiological health monitoring and human-machine interactions (HMI). However, challenge remains to fit flexible electronic devices to the human skin with conformal deformation and identifiable electrical feedback according to the mechanical stimuli. Herein, an adhesive E-skin is developed that can firmly attach on the human skin for mechanical stimuli perception. The laser-induced adhesive layer serves as the essential component to ensure the conformal attachment of E-skin on curved surface, which ensures the accurate conversion from mechanical deformation to precise electrical readouts. Especially, the 3D architecture facilitates the non-overlapping outputs that bi-directional joint bending and distinguishes strain/pressure. The optimized E-skin with bio-inspired micro-cilia exhibited significantly improved sensing performances with sensitivity of 0.652 kPa-1 in 0-4kPa and gauge factor of 8.13 for strain (0-15%) with robustness. Furthermore, the adhesive E-skin can distinguish inward/outward joint bending in non-overlapping behaviors, allowing the establishment of ternary system to expand communication capacity for logic outputs such as effective Morse code and intelligent control. It expects that the adhesive E-skin can serve as a functional bridge between human and electrical terminals for applications from daily mechanical monitoring to efficient HMI.

Advancements in Flexible Electronics Fabrication: Film Formation, Patterning, and Interface Optimization for Cutting-Edge Healthcare Monitoring Devices.

Flexible electronics can seamlessly adhere to human skin or internal tissues, enabling the collection of physiological data and real-time vital sign monitoring in home settings, which give it the potential to revolutionize chronic disease management and mitigate mortality rates associated with sudden illnesses, thereby transforming current medical practices. However, the development of flexible electronic devices still faces several challenges, including issues pertaining to material selection, limited functionality, and performance instability. Among these challenges, the choice of appropriate materials, as well as their methods for film formation and patterning, lays the groundwork for versatile device development. Establishing stable interfaces, both internally within the device and in human-machine interactions, is essential for ensuring efficient, accurate, and long-term monitoring in health electronics. This review aims to provide an overview of critical fabrication steps and interface optimization strategies in the realm of flexible health electronics. Specifically, we discuss common thin film processing methods, patterning techniques for functional layers, interface challenges, and potential adjustment strategies. The objective is to synthesize recent advancements and serve as a reference for the development of innovative flexible health monitoring devices.

Bio-inspired e-skin with integrated antifouling and comfortable wearing for self-powered motion monitoring and ultra-long-range human-machine interaction

Electronic skin (e-skin) inspired by the sensory function of the skin demonstrates broad application prospects in health, medicine, and human–machine interaction. Herein, we developed a self-powered all-fiber bio-inspired e-skin (AFBI E-skin) that integrated functions of antifouling, antibacterial properties, biocompatibility and breathability. AFBI E-skin was composed of three layers of electrospun nanofibrous films. The superhydrophobic outer layer Poly(vinylidene fluoride)-silica nanofibrous films (PVDF-SiO2 NFs) possessed antifouling properties against common liquids in daily life and resisted bacterial adhesion. The polyaniline nanofibrous films (PANI NFs) were used as the electrode layer, and it had strong “static” antibacterial capability. Meanwhile, the inner layer Polylactic acid nanofibrous films (PLA NFs) served as a biocompatible substrate. Based on the triboelectric nanogenerator principle, AFBI E-skin not only enabled self-powered sensing but also utilized the generated electrical stimulation for “dynamic” antibacterial. The “dynamic-static” synergistic antibacterial strategy greatly enhanced the antibacterial effect. AFBI E-skin could be used for self-powered motion monitoring to obtain a stable signal output even when water was splashed on its surface. Finally, based on AFBI E-skin, we constructed an ultra-long-range human-machine interaction control system, enabling synchronized hand gestures between human hand and robotic hand in any internet-covered area worldwide theoretically. AFBI E-skin exhibited vast application potential in fields like smart wearable electronics and intelligent robotics.

Printed-scalable microstructure BaTiO3/ecoflex nanocomposite for high-performance triboelectric nanogenerators and self-powered human-machine interaction

Triboelectric sensing technology reinforced with deep learning algorithm has shown significant potentials in the fields of sophisticated wearable devices and Internet of Things (IoT) applications. Herein, we present a large-scale and printable nanocomposite film for high-performance microstructured BaTiO3/Ecoflex triboelectric nanogenerator (MBE-TENG) and demonstrate its applications in deep-learning assisted self-powered sensing and human-machine interaction. The MBE-TENG device achieves a short-circuit current of 1.46 μA, an open-circuit voltage of 144 V, and a transfer charge density of 6.62 nC/cm². Additionally, the MBE-TENG device can charge a 10 μF capacitor from 0 to 2 V within 200 seconds, illuminate over 70 commercial LEDs, and power a digital calculator. Based on the MBE-TENG, a sensory array keyboard is demonstrated to wirelessly control the movement of a smart car via Bluetooth communication. Furthermore, a smart tactile sensing glove is developed by combining the MEB-TENG, residual neural network and convolutional block attention module, realizing the facile recognition on six different objects with a high accuracy of 96.33 %. This work presents a scalable fabrication for microstructured triboelectric layer and highlights the versatility and efficiency of TENG sensing technology when combined with advanced neural networks and embedded systems, paving the way for innovative development in human-machine interaction, personalized healthcare, and intelligent control interfaces.

Exploring the Human-Centric Interaction Paradigm: Augmented Reality-Assisted Head-Up Display Design for Collaborative Human-Machine Interface in Cockpit

Human-cybernetic interfaces (HCI) focuses on the integration and synergy of human-centric design, human factors (HFs) and economics, and human–machine interaction (HMI). The scrutiny of single pilot operations (SPO) approval from civil aviation authorities (CAAs) is subject to the aviation safety and pilot capability during exceptional handling and flight emergency. Airliners advocate the possibility from dual pilot operations (DPO) to SPO because of the financial considerations and captain shortage in the post-pandemic era. One could expect that SPO can only realise when reduced crew operations initiative is proven to be safe and airworthy. Public concerns pilot mental workload and potentially low situational awareness (SA) when handling overwhelmed multi-source information from the HMI in a cockpit, leading to degraded skills and flying performance. Advanced head up display (HUD) design, an integrated transparent display, can assist pilots in maintaining their optimal performance when SPO exercised. In this study, we are interested to design a SPO-favoured cockpit design with HUD and investigate the potential of integrating augmented reality (AR) and HUD in SPO in flight operations. An experiment involving 21 pilots was conducted to assess the effectiveness of AR-HUD on SA and workload in DPO, SPO, and SPO with AR-HUD scenarios. The Situation Awareness Present Assessment Method (SPAM) and a self-rated workload scale were used to evaluate SA and workload levels. Eye-tracking data was also collected to analyse behavioural performance. Results indicated that the AR-HUD scenario achieved optimal workload and SA levels and demonstrated superior performance in handling emergencies. Eye-tracking data revealed that AR-HUD facilitated a more equitable distribution of gaze between instruments and external view. In conclusion, the integration of AR in cockpit design introduces a human-centric interaction paradigm, contributing to aviation safety, SPO implementation and adaptive HMI design.

A human-centered intelligent platform for holographic control and management of 3D printers

Under the wave of emerging human-centric concepts such as Industry 5.0 and Human-Centered Intelligent Manufacturing, Mixed Reality (MR) has emerged as a pivotal enabling technology with profound impacts on human–machine interaction. Simultaneously, the affordability of Fused Deposition Modeling (FDM) 3D printers has expanded their accessibility beyond industrial applications to homes and public spaces. However, the operational and management complexities of different 3D printers and their varying human–machine interfaces (HMI) present challenges for users, especially beginners. In this paper, we presented a human-centered intelligent platform for FDM 3D printer control and management enabled by Mixed Reality. A flexible system architecture is developed in the paper with two essential communication protocols, MQTT and WebSocket, for stable data transfer. In addition, a task-oriented HMI is explored on the MR headset to simplify the operation procedure with little training effort and allow parallel supervision of multiple machines. By leveraging the advantages of our architecture, real-time monitoring, failure detection, and hologram visualization were able to be integrated into our platform for intelligent operation and management. Importantly, the flexibility of the architecture via a holographic interface enables our platform to easily expand to various machining operations in the future.

Magnetron sputtering preparation of flexible ZnO/AlN thin-films sensors with hybrid piezoelectric effect for broad-range human motions detection

Wearable sensors with excellent comfort, flexibility and fast response are widely applied in human-machine interactions, artificial skin, motion detection and healthcare monitoring. However, preparation processes of the most of current piezoelectric sensors are relatively expensive and complicate, limiting their mass productions in the applications. This work demonstrates the superior piezoelectric performance and stability of ZnO/AlN flexible thin films prepared by magnetron sputtering for broad-range human motions detection, and first-principles calculation are applied to reveal the complicate piezoelectric effect of the hybrid system. The ZnO/AlN hybrid thin films sensor exhibits superior piezoelectric performance and excellent reliability, with presenting high outputs voltage (3.63 V), high degree of response speed (42.33 ms), water stability and robust performance upon 10000 cycles. First-principles calculations reveal that formation of the ZnO/AlN interface facilitates charge transport and improves efficiency of carrier transport, and results in reduced in band gap and enhanced electrical conductivity. By applying the sensors in human health monitoring, the underlying behaviors can be determined by output voltage of the sensor in real-time. This work offers a sensitive, simple structured and robust solution for human motion detection.

Wet-adaptive Strain Sensor Based on Hierarchical Core-sheath Yarns for Underwater Motion Monitoring and Energy Harvesting

Bifunctional flexible electronics with motion monitoring and energy harvesting have broad application prospects in day-to-day activities of human society. Nevertheless, conventional electronics are difficult to adapt to the wear comfortability and anti-sensing interference properties in wet environments. Herein, a wet-adaptive spandex/graphene@cotton/polyurethane yarn (SGCPY) sensor is fabricated with excellent sensing and triboelectric performance, which comprises core layer of spandex fibers, middle layer of graphene@cotton sensing fibers and outer layer of spindle-knotted polyurethane nanofibers. Benefiting from its unique structure, as-prepared SGCPY sensor exhibits high mechanical properties (~80%), superhydrophobic performance (>130°), good strain sensitivity (1.82) and fatigue resistance (12000 cycles) even under water condition. The usage of the SGCPY sensor is demonstrated for the stable monitoring of human body motions and human-machine interaction in both air and water environments. When SGCPY sensor weave as plain fabric, the textile also can convert various mechanical energy into electric power for driving electronic device, showing a maximum voltage of ~3.9V and ~0.7V with solid-solid and liquid-solid contact. This work fosters the in-depth study of textile-based electronics for underwater applications and highlights the promising prospects of multi-functional flexible electronics based on textiles.

What’s so complex about conversational speech? A comparison of HMM-based and transformer-based ASR architectures

Highly performing speech recognition is important for more fluent human-machine interaction (e.g., dialogue systems). Modern ASR architectures achieve human-level recognition performance on read speech but still perform sub-par on conversational speech, which arguably is or, at least, will be instrumental for human-machine interaction. Understanding the factors behind this shortcoming of modern ASR systems may suggest directions for improving them. In this work, we compare the performances of HMM- vs. transformer-based ASR architectures on a corpus of Austrian German conversational speech. Specifically, we investigate how strongly utterance length, prosody, pronunciation, and utterance complexity as measured by perplexity affect different ASR architectures. Among other findings, we observe that single-word utterances – which are characteristic of conversational speech and constitute roughly 30% of the corpus – are recognized more accurately if their F0 contour is flat; for longer utterances, the effects of the F0 contour tend to be weaker. We further find that zero-shot systems require longer utterance lengths and are less robust to pronunciation variation, which indicates that pronunciation lexicons and fine-tuning on the respective corpus are essential ingredients for the successful recognition of conversational speech.

Ultra-stretchable, robust, self-healable conductive hydrogels enabled by the synergistic effects of hydrogen bonds and ionic coordination bonds toward high-performance e-skins

Ionic conductive hydrogels as electronic skins (e-skins) have showcased pivotal potentials in the realm of human health monitoring and human–machine interfaces. However, developing intelligent hydrogel with remarkable stretchability and mechanical elasticity is yet challenging. Herein, high-performance ion-conducting hydrogels with unprecedented mechanical properties and good transparent, conductive, self-healing properties are constructed via free-radical polymerization of acrylic acid (AA) within the polyvinyl alcohol (PVA) network, and the subsequent freeze–thaw (F-T) treatment and Zr4+ ion immersion crosslinking technique. Profiting from the synergy of multiple intermolecular/intramolecular hydrogen bonds between different polymers and coordination bonds of carboxyl-Zr4+, the resultant PVA/PAA/Zr4+ hydrogel exhibits high transparency (∼97 %), superior conductivity (0.6089 S/m), a long elongation at break (2053 %), robust self-healing efficiency (96.3 %), high deformation-tolerate property, and notable mechanical repeatability. These multifaceted attributes render the hydrogel to serve as an ionic conductor for capacitive/resistive −type strain sensor. The hydrogel-based resistive-type strain sensor features a wide detection range (0.5 %-800 %), rapid response time (150 ms), long-term stability, and consistent output stability, making it promising in monitoring diverse human motions and seamless human–machine interactions. Additionally, the hydrogel sensor demonstrates excellent linear response in ultra-wide range (0–900 % strain), high sensitivity, and excellent cycling stability in a capacitive mode, enabling to be used for accurately detecting the weights of objects. As such, this work advances the development of ionic conductive hydrogel as flexible and wearable electronic devices.

Multifunctional Sensing Platform Based on Single Antenna for Noncontact Human–Machine Interaction and Environment Sensing

Multifunctional Sensing Platform Based on Single Antenna for Noncontact Human–Machine Interaction and Environment Sensing

Wave-transparent and eco-friendly flame-retardant phosphorus-based phthalonitrile/quartz composites by a synchronized self-curing strategy with cyano and alkynyl groups

The robust human–machine interaction systems of the aerospace industry necessitate the development of thermal resistant wave-transparent composites, with a pivotal focus on organic resin matrix. However, enhancing the heat resistance of such materials while preserving other critical attributes has been a formidable challenge. In this study, a novel designed strategy of a dual-curing functional phosphorus-based phthalonitrile monomer (P-ALK-PN) has been proposed. The optimized curing temperatures for intra- or intermolecular Diels-Alder reactions of alkynyl groups and polyaddition reaction of cyano groups resulted in a homogeneous and highly crosslinked N-enriched phthalonitrile system. After curing at 450 °C, the resin, namely P-ALK-PN-450 °C, demonstrated exceptional thermal stability (T5% = 644 °C), thermo-oxidative stability (T5% = 554 °C), and char yield at 800 °C (90.7 % in N2 and 67.5 % in air). Their quartz fiber reinforced composites (P-ALK-PN/QFs) were subsequently fabricated by the hot-pressing process. Upon post-curing at 420 °C, P-ALK-PN-420 °C/QF displayed elevated glass transition temperature (550 °C), flexural strength (319 MPa), and relatively low dielectric properties with a dielectric constant (Dk) of approximately 4.2 and a dissipation factor (Df) less than 0.02 across a wide temperature ranging from 25 °C to 600 °C. Especially, it exhibited excellent flame retardancy (only a 6.7 % weight loss at ambient temperatures exceeding 1300 °C for 200 s) as well, stemming from release of eco-friendly inert gases (NH3) and formation of a graphitized protective layer during pyrolysis. These promising results highlight the potential applications of P-ALK-PN resins in aerospace and high-performance engineering fields.

Development and evaluation of a novel force myography (FMG) based human-machine interface for transradial prosthetics: A comparative study with electromyography (EMG) techniques

Human-Machine Interfaces (HMIs) must bridge human skills with machine control, notably in active prosthesis. Electromyography (EMG) is the most popular prosthesis actuation method. However, researchers need new HMIs for active prosthesis development. This research introduces Forcemyography (FMG) based HMI using force- sensitive resistors (FSRs) to capture myoelectric data. A particularly constructed case with two integral parts—an internal and an exterior component—is FMG. This FMG enclosure adapts effortlessly to the user's limb and shape for best performance and comfort. FMG and sEMG were compared in the experimental evaluation. Stability, forecast accuracy, hair and sweat resistance, wearability, and cost-effectiveness were examined. FMG signals outperformed sEMG signals in this comprehensive examination. FMG outperformed sEMG in stability and prediction accuracy. FMG produced outstanding results right from capture without post-processing, a unique advantage. The specialised enclosure protected FSRs from outside interference and delivered precise signals. The Forcemyography (FMG)-based HMI is a breakthrough in active prosthesis technology. Its versatility, consistent signal acquisition, and user-centric design promise improved human-machine interactions. FMG leads the way to smooth, intuitive, and efficient humanmachine interface.

Biodegradable, stretchable, and high-performance triboelectric nanogenerators through interfacial polarization in bilayer structure

The increasing demand for wearable electronics has led to the development of triboelectric nanogenerators (TENGs) as a promising energy harvesting and sensing technology. However, conventional TENGs often utilize non-biodegradable materials, contributing to environmental pollution. In this work, we present a stretchable and biodegradable TENG based on hydroxyethyl cellulose (HEC) and gelatin (HG-TENG). The HG-TENG features a bilayered structure, where the large difference in their relative permittivity between HEC and gelatin induces interfacial polarization, effectively mitigating charge recombination and enhancing triboelectric performance. The optimized HG-TENG achieves an open-circuit voltage (Voc) of 93 V, a maximum power density of 57.8 µW/cm2, and can power 38 blue light-emitting diodes. The device exhibits a stretchability of 150 % and biodegrades within 3 hours in phosphate-buffered saline. Furthermore, we demonstrate the application of the HG-TENG as a wearable sensor by modifying it with trichloro(1H, 1H, 2H, 2H-perfluorooctyl)silane (FOTS) (FHG-TENG). The FHG-TENG-based smart glove, integrated with machine learning algorithms, enables real-time monitoring of blood pressure waveforms and finger motions, showcasing its potential for human-machine interfaces. The smart glove, equipped with five FHG-TENGs on the proximal interphalangeal joints of each finger, detects diverse finger gestures and generates voltage signals that control a robotic hand in real-time, demonstrating effective human-machine interaction through synchronized motion. Moreover, the smart glove achieves a high recognition accuracy of 96.15 % for 10 different hand sign languages.

Efficient Permeable Monolithic Hybrid Tribo-Piezo-Electromagnetic Nanogenerator Based on Topological-Insulator-Composite.

Escalating energy demands of self-independent on-skin/wearable electronics impose challenges on corresponding power sources to offer greater power density, permeability, and stretchability. Here, a high-efficient breathable and stretchable monolithic hybrid triboelectric-piezoelectric-electromagnetic nanogenerator-based electronic skin (TPEG-skin) is reported via sandwiching a liquid metal mesh with two-layer topological insulator-piezoelectric polymer composite nanofibers. TPEG-skin concurrently extracts biomechanical energy (from body motions) and electromagnetic radiations (from adjacent appliances), operating as epidermal power sources and whole-body self-powered sensors. Topological insulators with conductive surface states supply notably enhanced triboelectric and piezoelectric effects, endowing TPEG-skin with a 288V output voltage (10 N, 4Hz), ∼3 times that of state-of-the-art devices. Liquid metal meshes serve as breathable electrodes and extract ambient electromagnetic pollution (±60V, ±1.6µA cm-2). TPEG-skin implements self-powered physiological and body motion monitoring and system-level human-machine interactions. This study provides compatible energy strategies for on-skin/wearable electronics with high power density, monolithic device integration, and multifunctionality.

Asymmetric Wettability Hydrogel Surfaces for Enduring Electromyographic Monitoring.

Hydrogel electrode interfaces have shown tremendous promise in the acquisition of surface electromyography (EMG) signals. However, the perspiration or moisture environments will trigger the deadhesion between hydrogel electrodes and human skin. Despite the hydrophobic/hydrophilic surfaces can perform the anti-moisture or adhesion respectively, it remains a challenge to integrally form a Janus hydrogel with homogeneous mechanical elasticity and electronic performance. Herein, a surface induction strategy is proposed to approach the hydrophobic/hydrophilic hydrogel surfaces. The hydrophobic interaction between surfactants and molds regulates the distribution of hydrophobic/hydrophilic monomers on the surface. The hydrophobic molds induce a hydrophilic hydrogel surface, while the hydrophilic molds induce a hydrophobic surface. It presents a new phenomenon of reversal wettability inducing and optional hydrogel surfaces. The integral Janus hydrogel can be easily obtained by the hydrophilic molds. Balance of adhesion, elasticity, and conductivity endows the hydrogel electrode patch with durable conformal adhesion and high-fidelity EMG signals even in the sweaty epidermis due to the asymmetric wettability surfaces. This hydrogel performs the quantitative description of muscle strength and accurate fatigue assessment. It offers a reliable candidate for future practical applications in continuous digital healthcare and intelligent human-machine interaction, even the Metaverse.

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Accurate recognition of human dynamic movement is essential for seamless human–machine interaction (HMI) across various domains. However, most of the existing methods are single‐modal movement recognition, which has inherent limitations, such as limited feature representation and instability to noise, which will affect its practical performance. To address these limitations, this article proposes a novel fusion approach that can integrate two biological signals, including electromyography (EMG) and bioelectrical impedance (BI). The fusion method combines EMG for capturing dynamic movement features and BI for discerning key postures representing discrete points within dynamic movements. In this method, the identification of key postures and their temporal sequences provide a guiding framework for the selection and weighted correction of probability prediction matrices in EMG‐based dynamic recognition. To verify the effectiveness of the method, six dynamic upper limb movements and nine key postures are defined, and a Universal Robot that can follow movements is employed for experimental validation. Experimental results demonstrate that the recognition accuracy of the dynamic movement reaches 96.2%, representing an improvement of nearly 10% compared with single‐modal signal. This study illustrates the potential of multimodal fusion of EMG and BI in movement recognition, with broad prospects for application in HMI fields.

Advances in Human–Machine Interaction, Artificial Intelligence, and Robotics

The convergence of artificial intelligence (AI), robotics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and extended reality (XR) is transforming the way humans interact with machines [...]

Multiphase soft metal enabled high-performance fabric-based wearable energy harvesting

Highly efficient and flexible power sources have always been the focus and goal in the field of wearable electronics. However, the existing wearable power sources are limited by their large size, high weight, electrolyte leakage, and poor mechanical compliance, which seriously hinders their practical applications. Herein, we propose a novel strategy to achieve and demonstrate a fabric-based high-performance wearable triboelectric nanogenerator (TENG) structure. The metals of gallium (Ga), bismuth (Bi), indium (In), and tin (Sn) are mixed according to specific mass ratios, and then they are transformed in liquid phase to form room-temperature multiphase soft metals of GaBiInSn. The material exhibits both metallic and fluidic properties, and possesses a characteristic of multiphase structure. The GaBiInSn material is directly deposited between polytetrafluoroethylene (PTFE) layers and nonwoven fabrics to establish multi-level conductive and frictional interfaces, creating charge transfer and solid-liquid hybrid dielectric layers. Therefore, the thin film-type triboelectric nanogenerators with a structure of PTFE/GaBiInSn/nonwoven fabric (LM-P-TENGs) is manufactured. The LM-P-TENGs can be integrated onto fabrics without compromising their breathability, comfortableness, and flexibility. Particularly, LM-P-TENGs efficiently convert the mechanical energy of human limb movements into sustainable electrical energy output. Under the human limb mechanical triggering conditions, LM-P-TENG achieves a champion specific open-circuit voltage up to 900 V, peak short-circuit current density of 43.3 mA/m2, and high power density of 12.56 W/m2. This work demonstrates the application potential of room-temperature multiphase soft materials in flexible wearable power sources, while introducing a novel power supply mode for wearable electronics. Additionally, the LM-P-TENGs also present applications in self-powered wearables, medical biosensing systems, human-machine interaction systems, near-eye display systems, etc. for the flexible electronics.