Bioinspired nanoplatforms (BINs) are a class of nanomaterials designed to mimic the properties and functions of biological systems (Katiyar et al., 2021). These nanoplatforms are typically made from organic or inorganic materials engineered to have specific physicochemical properties for various applications. BINs have garnered significant attention in the field of HMIs owing to their unique properties and functionalities inspired by natural and biological systems. Compared to traditional materials, they have the potential to revolutionize HMIs by providing excellent skin compatibility (Xu et al., 2022a), biodegradability (Zarei et al., 2022), mechanical strengths (Dai et al., 2022), low toxicity (Wu et al., 2022a), high sensitivity, and stability (Zarei et al., 2022), which can be harnessed to improve novel and flexible electronic devices for HMIs.

With the continuous progress of research on bioinspired materials, further innovations are focused on developing safer, more efficient, and more immersive HMIs. Herein, recent trends and developments in HMIs are reviewed, while highlighting their potential applications in emerging biotechnologies. A brief overview of the recent trends and developments in bioinspired nanoplatforms and their device applications in HMIs is provided. Further, various advanced functional nanomaterials, such as graphene, carbon nanotubes (CNTs), MXenes, transition metal dichalcogenides (TMDCs), metamaterials, and biocompatible/biodegradable nanohybrids, are discussed. Finally, device applications of BINs for the new era of HMIs are highlighted. The novel devices based on HMIs and their applications in various emerging fields of biotechnologies, including healthcare, electrophysiological signal and body fluid monitoring, biomimetic flexible electronics, nanoscale biointerfaces, and wearable nano/microfluidic devices, are discussed.

Currently, scientific researchers are focused on developing bioinspired nanomaterials (BINs) (Xue et al., 2022), which are emerging materials to be incorporated in HMIs for various bioelectronic applications, such as healthcare, biofluid and biopotential monitoring, and neuroelectronic, strain, and tactile/pressure sensing, as shown in Fig. 1.

With advancements in scientific technology, biological systems, including the human body and brain, have been extensively investigated (Tang et al., 2022). In developing bioinspired nanoplatforms, nature is considered as both an inspiration and a component (Wu et al., 2022a). In particular, biological materials are ideal models for designing and fabricating nanostructures by mimicking their processes, structures, and properties. Nanomaterial-based platforms hold immense promise in HMIs but face limitations such as scalability, toxicity, biocompatibility, and long-term stability. Therefore, exploring biology or nature for inspiration provides insights into overcoming these limitations. Achieving scalability in nanotechnological platforms is challenging due to the high-cost manufacturing of nanolithography techniques. Some nanomaterials exhibit unique properties at the nanoscale but can be challenging to produce in large quantities with consistent quality. Madamsetty et al. compared the chemically synthesized gold nanoparticles (Au NPs) and biosynthesized Au NPs in the drug delivery for anti-cancer drugs and found that chemically synthesized Au NPs are more toxic than biosynthesized (Madamsetty et al., 2019). Materials that are biocompatible and have low toxicity can be used in bioelectronic devices for various applications, such as biomedical implants or wearable sensors. However, the nanomaterials made of rigid materials are incompatible with soft, wet, and living biological tissues (Yuk et al., 2022), resulting in incompatibility and affecting long-term stability. Therefore, bioinspired nanoplatforms have been utilized for different biomedical applications owing to their feasibility for commercial implementation. The quality of life can be improved by developing and integrating BINs in HMIs. This strategy has the potential to revolutionize how humans interact with technology, fostering more natural, efficient, and personalized experiences while contributing to advancements in healthcare, accessibility, and sustainability. In the fabrication of wearable and flexible devices for on-body measurements that connect humans and machines, BINs are incorporated into various flexible polymer matrixes. This facilitates user convenience and ensures adequate skin adhesion (Rdest and Janas, 2021). Moreover, they have a wide range of applications, such as robotics, wearable technologies, and virtual reality (VR), thereby transforming our interaction with machines and opening new possibilities for HMIs (Zhang et al., 2022a).

There has been an increasing trend in the applications of BINs as an alternative to conventional materials for HMIs to facilitate improved sensing, comfort, durability, biocompatibility, and sustainability (Bardhan et al., 2023). Bioinspired refers to design, concept, or innovation that draws inspiration from biological systems, processes, structural hierarchy, geometry, and functions found in nature. The scientific community is continuously exploring bioinspired approaches to mimic or adapt principles observed in living organisms to create solutions for various challenges in emerging technology. In addition, the research interest in bioinspired nanoplatforms has rapidly increased in recent years owing to their potential for various applications, such as neuromorphic computation as a sensory system (Wang et al., 2022a), wearable electronics (Hazra et al., 2022), biomimetic material for artificial organ (Ishihara, 2022), sensors/biosensors (Zhu et al., 2021a), biosignal monitoring (Amin et al., 2022), and gesture-based interfaces to control devices using hand and body movements (Li et al., 2022a). Currently, there is a recent trend unfolding the utilization of BINs to develop new biomimetic materials for various applications. Because BINs are highly customizable and can be tailored to their specific needs. Additionally, they are cost-effective and can be produced in large quantities. However, several issues are associated with chemically synthesized nanoparticles (NPs), including cost, toxicity, and effectiveness. Bioinspired NPs offer a low-cost, easy-to-synthesize, and low-toxicity alternative to conventional NPs (Madamsetty et al., 2019).

Nature is the most distinguished inspiration for different human developments (Zhu et al., 2021a). BINs are designed to mimic the sensing capabilities of the human skin to develop novel tactile sensors. Inspired by the human skin, Pang et al. mimicked the functionality of biological skin and reported a multifunctional tactile sensor for health monitoring and soft robotics (Pang et al., 2022). Piezoresistive and triboelectric sensing layers were combined to mimic the slow and adaptive mechanoreceptors of human skin to detect joint motion, muscle movement, artery pulse pressure, and voice recognition. In addition, spiders and silkworms produce natural silk fibers (SFs) with unique properties, including strength, toughness, flexibility, extensibility, biocompatibility, self-assembly, and hierarchical structures (Greco et al., 2022; Tran et al., 2023). While human skin-inspired sensors excel in tactile applications, further enhancement in the mechanical strength of bioinspired materials may extend the performance of wearable pressure and tactile sensors. The mechanical strength of natural SFs can be enhanced by incorporating mechanically strong nanomaterials into their structure (Lu et al., 2022a). Inspired by the structure of SFs, Yin et al. utilized the strategy to mimic the structure of SFs to enhance the mechanical strength of CNTs. By infiltrating SFs into CNT, they reported a breaking strength of 1023 MPa and Young's modulus of 81.3 GPa for SF/CNTs structure (Yin et al., 2021). Fig. 2a illustrates the similarities in the structural behavior and breaking process between natural SFs and biomimetic SF/CNTs fibers under a pulling force. The findings suggest that the mechanical characteristics of CNTs can be significantly improved through SF infiltration, thereby promoting the design and production of wearable pressure and tactile sensors. Recent research has focused on integrating optical elements into HMIs (Jiang et al., 2020). Integrating optical fibers in wearable sensors offers several advantages over traditional sensors. As they are non-invasive and comfortable to the wearer, combining fiber Bragg gratings (FBG) in wearable devices has enormous potential in tactile sensing (Massari et al., 2022). To demonstrate this potential, Li et al. constructed a flexible and stretchable optical fiber strain sensor to translate human body movements into control commands for HMIs applications, as shown in Fig. 2b. Compared to traditional sensors, bioinspired underwater sensors can provide a more efficient and accurate method for underwater sensing. Underwater sensors are required for various applications, such as oceanography, marine biology, navigation, and military operations (Campagnaro et al., 2023). Aquatic plants and their adaptation to underwater environments play a significant role as inspirations in developing underwater sensors. Designing BINs that mimic the slit geometry of scorpions and papilla-like structures of lotus leaves is also an ongoing trend. Liu et al. stimulated the ultrasensitive vibration-sensing capability of scorpions and superhydrophobic characteristics of a lotus leaf to fabricate an underwater sensor (Liu et al., 2020). As shown in Fig. 2c, the design and fabrication of bioinspired sensor are based on the coupling bionics strategy. The photo paper (P-paper) was used as substrate material due to its higher breaking strength for underwater strain sensing applications. The sensor is fixed underwater, and its change in resistance is measured toward the vibration wave of water droplets. Leveraging the hydrophobic characteristics observed in lotus leaves, the fabrication of an underwater sensor was developed. This approach is further extended to the realm of physiological signal monitoring, where the microstructure of lotus leaves serves as a blueprint for sensor design. Monitoring various physiological signals typically requires multiple sensors, thereby increasing equipment costs and wearer discomfort (Phan et al., 2022). Hence, developing a single device with multiple sensor combinations is important for continuous physiological signal monitoring (Pan et al., 2021). Inspired by the microstructure surfaces of lotus leaves, Wang et al. fabricated flexible electronic multifunctional sensors to detect vibrations and human physiological signals using polypyrrole/Ag film on a PDMS substrate (Wang et al., 2021a), as shown in Fig. 2d. This sensor has promising potential for artificial intelligence (AI) applications in wearable and HMIs systems. With an integration of AI in sensing technology, the domain of neuromorphic computation is seamlessly entered (Wang et al., 2023a). In this transition, the evolution from conventional intelligence emulation to the intricate modelling of neural systems is encapsulated, fostering more energy-efficient and bioinspired computing paradigms. Neuromorphic systems and HMIs are closely related owing to their abilities to mimic human information processing, resulting in advantages over traditional computing systems (Bian et al., 2021). Further, they are energy efficient, making them ideal for mobile devices and other applications where power consumption is a concern (Schuman et al., 2022). Sensory neuromorphic systems can also process data in real-time. Kim et al. developed an integrated system inspired by the golden tortoise beetle's unique ability to sense a gentle touch and change color from gold to red. This sensory neuromorphic system, like its biological counterpart, is capable of processing data in real-time. They integrated a capacitive pressure sensor as an artificial mechanoreceptor to convert the physical signal into an electrical signal, resistive random-access memory (RRAM) as an artificial synapse to train artificial neural network models, and light-emitting diodes (LEDs) as the epidermal photonic actuator to display the color change (Kim et al., 2021a), as shown in Fig. 2e. Mimicking the information processing of the human brain, bioinspired computing paradigms are exemplified through neuromorphic systems. Simultaneously, a seamless transition introduces the emulation of intricate human skin functionality in the domain of flexible electronic skin. Advanced electronic skin (e-skin) design is an emerging trend in the scientific community to mimic the functionality of human skin. Despite their potential benefits, e-skins face several challenges that limit their wide-scale adoption. Therefore, research has focused on increasing the flexibility and sensitivity of e-skins to increase their comfortability for wearers. Inspired by the structure of rose petals, Zhu et al. designed transparent and antibacterial e-skin for tactile sensing (Zhu et al., 2021a), as shown in Fig. 2f. The polyvinylidene fluoride (PVDF)/Ag nanowires (NWs)/PDMS system was used to create transparent electrodes in the e-skin, and glycerol monolaurate was added as an antibacterial agent. The resulting e-skin was highly flexible and could be easily applied to curved surfaces without losing its conformability. Further, this transparent e-skin exhibited multifunctionality for measuring distance resolution, multi-tactile sensing, and real-time trajectory recognition. The current trends in e-skin development also involve exploring stretchable e-skins that incorporate semiconducting poly(3-hexylthiophene) (P3HT) nanofibers within a percolated PDMS polymer. These advanced e-skins aim to offer diverse sensing capabilities, including force, temperature, and visible light (Liu et al., 2023a). Overall, considerable research has recently focused on BINs to their potential applications in HMIs. Recent trends in BINs focus on utilizing functional nanomaterials that mimic the structure, properties, and functions of natural SFs, predatory arachnids, and plant and animal species. This trend leads to the development of materials that can naturally interact with the human epidermis and biological systems, thereby improving device performance and functionality.

Recent developments in HMIs include brain-computer interfaces (BCIs), metaverse, and haptic feedback interfaces (Yao et al., 2022a). Electronic devices that can be integrated with soft biological tissues similar to the surface of the human body have great potential applications in health monitoring. Nanomaterials/electrodes should exhibit flexibility and biocompatibility before being implanted in the human body to record human bioelectrical signals. This human-implanted nanoplatform serves as a multifunctional biosensor that allows real-time detection and monitoring of physiological signals, such as the heart rate (HR), body temperature, and oxygen saturation, without interfering to normal body movements. Previous studies provide significant insights into the development of a single device with multiple-sensing functionalities for continuous physiological signal monitoring (Pan et al., 2021). Technological advancements in HMIs enable the simultaneous real-time monitoring of several physiological parameters (Sharma et al., 2022; S. Shen et al., 2022).

Recently, the growing interest in the Metaverse has facilitated a fully immersive and interconnected VR world (Le et al., 2022). To create a VR experience, researchers typically apply machine learning (ML) models to the sensing data. The VR community exploits the self-generating signals and low power consumption ability of triboelectric nanogenerator (TENG) sensors to develop VR headsets (Si et al., 2022), motion sensors (Zeng et al., 2022), and haptic feedback devices (Zhu et al., 2022). Self-powered TENG sensors provide a new possibility for realizing self-sustainable HMIs by directly converting biomechanical energies into valuable sensory information (Sun et al., 2021). Sun et al. developed augmented tactile-perception and haptic-feedback rings (ATH-Rings) for VR applications (Sun et al., 2022). They utilized TENG tactile sensors to detect continuous bending and temperature using flexible pyroelectric sensors, vibrohaptic feedback using eccentric rotating mass vibrators, and thermohaptic feedback using nichrome metal wires. A wireless internet-of-things (IoT) module was utilized to integrate these sensors and haptic stimulators into a single ATH-Ring, as shown in Fig. 3a. Despite significant advancements in the HMI technology, existing systems and devices still rely on bulky machinery and rigid electronics, which pose limitations in terms of wearability, comfort, and range of functions (Lim et al., 2021; Luo et al., 2023). Flexible e-skin is a promising technology that can be used in HMI systems. Liu et al. combined visual and haptic VR through skin-integrated flexible electronics to create a closed-loop HMI for robotic VR with promising potential for noncontact biosample collection and treatment of infectious diseases (Liu et al., 2022b). This closed-loop HMI system was compatible with the entire body for wireless motion capturing and haptic feedback via the Bluetooth module, WiFi, and internet.

A multimodal wearable sensor combines different sensing functions into a single compact unit to enhance the comfort of the wearer (Zhu et al., 2022). Furthermore, it eliminates the complexity and burden of using multiple sensors for signal monitoring (Kwon et al., 2021). The simultaneous real-time monitoring of multiple physical signals can also benefit health monitoring and diagnosis (Sempionatto et al., 2021a). Inspired by the liquid transport strategy to detect electrocardiogram (ECG) and electrooculogram (EOG) signals with and without sweating, Xu et al. prepared e-skins using Au/TPU/cellulose membrane (CM) electrodes (Xu et al., 2022a), as shown in Fig. 3b. They successfully addressed the major bottleneck in the continuous long-term monitoring of biosignals with high precision. Recent advances in bionic devices have demonstrated remarkable progress in HMIs that enable enhanced functionality and improved quality of life for individuals with disabilities. Therefore, various bionic devices have recently emerged to achieve breakthroughs in biomimetic underwater robots and harvest hydrokinetic energy that provides a paradigm of combining TENGs and bionic technology to address various scientific problems (Jing et al., 2022; Zhang et al., 2023a). The ability to sense small muscle fluctuations using traditional sensors remains a challenge. The combination of TENGs and bionic technology may overcome this problem. This strategy was implemented by Zhou et al. to develop a safe, accurate, and stable sensor inspired by the frogs' croaking behavior by integrating bionic and TENG for sensing small muscle fluctuations to replace traditional surface electromyography (sEMG) devices for HMI applications (Zhou et al., 2021a), as shown in Fig. 3c. The bionic-TENG-based sensor produced an output signal 206 times stronger than that of a traditional sEMG device.

Biosignal monitoring and gesture recognition analysis in real-time using automated data processing technologies, such as ML (Reel et al., 2022) and deep learning algorithms, which allow rapid analysis of complex data (Zhou et al., 2022). The human tactile perception system is an essential aspect of our sensory experience, which allows us to interpret and understand the physical properties of objects through touch. As such, developing effective HMIs has been a significant research interest in recent years (Lee et al., 2023). A promising avenue for advancing this field is to draw inspiration from human tactile perception systems. Inspired by the human fingertip's stick–slip movement mechanism, Li et al. reported multifunctional ML-assisted flexible bionic tactile systems for HMIs to detect force and slippage, material classification, and roughness discrimination (Li et al., 2022a), as shown in Fig. 3d. Liu et al. developed a star-nose-mimicking bioinspired olfactory-tactile sensing system and combined it with ML architecture for robust object recognition. The sensing system was designed to identify humans during rescue conditions (Liu et al., 2022b). AI-powered machines are rapidly developing to inevitably replace human decision-making. Shin et al. developed a neuroadaptable wireless closed-loop system between the brain and an AI device using electroencephalogram (EEG) signal measurement (Shin et al., 2022a). An error-related potential (ErrP) signal generated by the human brain was utilized as the input data to the AI device for its continuous reinforcement learning by the ErrP feedback using deep learning and ML algorithms, as shown in Fig. 3e. In contrast to conventional machines that use unidirectional commands, this machine automatically learns appropriate decisions and revises inaccurate ones. Researchers have recognized the applications of AI/ML models for technological breakthroughs in HMIs and advanced intelligent era. Motivated by AI and full-skin bionic structures, Niu et al. constructed an advanced intelligent material recognition system for the real-time detection of material species via tactile sensing, thereby surpassing the human capability (Niu et al., 2022a). The output of intelligent material recognition systems in the form of voltage waveforms for the 12 materials is shown in Fig. 3f. Therefore, ML algorithms have the potential to significantly improve HMIs by training models using complex input data to achieve accurate results. The trend in advancing HMI devices involves the integration of numerous self-powered sensors with AI technology. This approach aims to extract comprehensive sensory information from compact data volumes instead of superficially analyzing extensive data (Guo et al., 2021a). These recent developments in HMIs can greatly improve our interactions with machines and devices to increase their intuitiveness, efficiency, and responsiveness to human needs.